This document contains 30 multiple choice questions and answers related to Tableau. It covers topics like reference bands, reference distributions, trend line models, map visualizations, actions, highlighting, sheets vs dashboards, identifying continuous vs discrete fields, using measures multiple times, creating sets, variable size bins, reference lines, disaggregation, displaying measures over time, grouped fields, reference bands, heat maps, confidence intervals, trend lines, profit ratios, and more. The questions range from basic to more advanced levels and are meant to help with Tableau certification preparation or job interviews.
The document discusses the importance of data for evidence-based policymaking, organizational development, detecting security issues, and improving business outcomes. It provides examples of how New Zealand Registry Services (NZRS) uses data for these purposes, including operating a national broadband map and open data portals. The document advocates for making more data openly available to enable reproducible research, more informed policy debates, and increased public trust.
Machine learning for customer classificationAndrew Barnes
Machine learning can be used to classify customers into segments based on their behavior patterns and perceptions. This allows companies to better target customers. Traditional approaches provide a narrow view of customers due to limited data sources. Machine learning uses both internal customer data and market research to understand customers. Case studies showed how machine learning was used to segment customers for a telecommunications provider to improve bundle fit and satisfaction, segment physicians for a pharmaceutical company to target sales, and predict potential purchasers for a retailer.
Knowledge graphs ilaria maresi the hyve 23apr2020Pistoia Alliance
Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.
Introduction to Data Mining and Data WarehousingKamal Acharya
This document provides details about a course on data mining and data warehousing. The course objectives are to understand the foundational principles and techniques of data mining and data warehousing. The course description covers topics like data preprocessing, classification, association analysis, cluster analysis, and data warehouses. The course is divided into 10 units that cover concepts and algorithms for data mining techniques. Practical exercises are included to apply techniques to real-world data problems.
The document describes the key phases of a data analytics lifecycle for big data projects:
1) Discovery - The team learns about the problem, data sources, and forms hypotheses.
2) Data Preparation - Data is extracted, transformed, and loaded into an analytic sandbox.
3) Model Planning - The team determines appropriate modeling techniques and variables.
4) Model Building - Models are developed using selected techniques and training/test data.
5) Communicate Results - The team analyzes outcomes, articulates findings to stakeholders.
6) Operationalization - Useful models are deployed in a production environment on a small scale.
This document provides an overview of big data analytics and data visualization. It discusses key concepts like data wrangling, exploring patterns, drawing conclusions, and communicating findings. Common techniques are also summarized, including classification, clustering, association rules, and predictive analytics. Specific algorithms like decision trees, k-means clustering, and hierarchical clustering are explained. The CRISP-DM process model and applications of analytics in areas like customer understanding and process optimization are also covered at a high level. Visualization is presented as an important part of the overall analytics process.
The document discusses the importance of data for evidence-based policymaking, organizational development, detecting security issues, and improving business outcomes. It provides examples of how New Zealand Registry Services (NZRS) uses data for these purposes, including operating a national broadband map and open data portals. The document advocates for making more data openly available to enable reproducible research, more informed policy debates, and increased public trust.
Machine learning for customer classificationAndrew Barnes
Machine learning can be used to classify customers into segments based on their behavior patterns and perceptions. This allows companies to better target customers. Traditional approaches provide a narrow view of customers due to limited data sources. Machine learning uses both internal customer data and market research to understand customers. Case studies showed how machine learning was used to segment customers for a telecommunications provider to improve bundle fit and satisfaction, segment physicians for a pharmaceutical company to target sales, and predict potential purchasers for a retailer.
Knowledge graphs ilaria maresi the hyve 23apr2020Pistoia Alliance
Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.
Introduction to Data Mining and Data WarehousingKamal Acharya
This document provides details about a course on data mining and data warehousing. The course objectives are to understand the foundational principles and techniques of data mining and data warehousing. The course description covers topics like data preprocessing, classification, association analysis, cluster analysis, and data warehouses. The course is divided into 10 units that cover concepts and algorithms for data mining techniques. Practical exercises are included to apply techniques to real-world data problems.
The document describes the key phases of a data analytics lifecycle for big data projects:
1) Discovery - The team learns about the problem, data sources, and forms hypotheses.
2) Data Preparation - Data is extracted, transformed, and loaded into an analytic sandbox.
3) Model Planning - The team determines appropriate modeling techniques and variables.
4) Model Building - Models are developed using selected techniques and training/test data.
5) Communicate Results - The team analyzes outcomes, articulates findings to stakeholders.
6) Operationalization - Useful models are deployed in a production environment on a small scale.
This document provides an overview of big data analytics and data visualization. It discusses key concepts like data wrangling, exploring patterns, drawing conclusions, and communicating findings. Common techniques are also summarized, including classification, clustering, association rules, and predictive analytics. Specific algorithms like decision trees, k-means clustering, and hierarchical clustering are explained. The CRISP-DM process model and applications of analytics in areas like customer understanding and process optimization are also covered at a high level. Visualization is presented as an important part of the overall analytics process.
The document discusses business intelligence and data warehousing. It describes the evolution of business intelligence from manual data retrieval and report preparation to modern integrated systems that provide analytics, dashboards, reporting, and key performance indicators. The Performa BI Suite is presented as a user-friendly business intelligence software that offers advanced visualization tools, multidimensional analysis, and integrated analytics, dashboards, and reporting in a single platform. Testimonials from users praise Performa for its ease of use, stability, and ability to meet reporting and analysis needs.
This document discusses clickstream analysis, which involves analyzing the paths users take while browsing websites. Clickstream data records users' clicks and is useful for market research and analyzing user behavior. The document then discusses how clickstream data can be collected from server logs, analyzed to identify popular and unpopular pages, and used to improve websites and target marketing strategies. It also discusses how clickstream data combined with other data sources can enable personalization.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
Data Visualization Design Best Practices WorkshopJSI
This document provides guidance on effective data visualization. It emphasizes starting with the audience and their needs, identifying the key story or message in the data, and using simple, clear design principles. Charts should be designed in 5-8 seconds to engage the audience. The document recommends several resources for choosing effective chart types and improving visualization skills. Overall, it stresses the importance of visualization in empowering stakeholders to make informed decisions.
This document provides an overview of Tableau, a data visualization software. It outlines the agenda for the presentation, which will cover connecting to data, visual analytics with Tableau, dashboards and stories, calculations, and mapping capabilities. Tableau allows users to connect to various data sources, transform raw data into interactive visualizations, and share dashboards or publish them online. It is a leading tool for data analysis and visualization.
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
The 'macro view' on Big Query:
We started with an overview, some typical uses and moved to project hierarchy, access control and security.
In the end we touch about tools and demos.
Power BI is a self-service business intelligence tool that allows users to analyze and visualize data. It consists of Power BI Desktop, the Power BI web service, and the Power BI mobile app. Power BI Desktop is used to build reports and dashboards locally, while the web service allows users to publish, share, and collaborate on reports and dashboards online. To create a dashboard in Power BI, a user would connect to a data source, build visualizations with the data, publish the report to the web, combine reports into a dashboard, and then share the dashboard.
Team project - Data visualization on Olist company dataManasa Damera
This document discusses optimizing the analytics process for a Brazilian e-commerce company called Olist. It begins with an overview of the client scenario and scattered data. The goals are to normalize the data, optimize the ETL process, and automate analytics insights. The document describes plans to normalize the data into tables, extract data from CSV files, transform and clean the data, and load it into a PostgreSQL database. It discusses how analytical procedures and a dashboard can provide insights for different departments. Finally, it demonstrates the database interaction through a Metabase dashboard.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
The document discusses the key components of a big data architecture. It describes how a big data architecture is needed to handle large volumes of data from multiple sources that is too large for traditional databases. The architecture ingests data from various sources, stores it, enables both batch and real-time analysis, and delivers business insights to users. It also provides examples of Flipkart's data platform which includes components like an ingestion system, batch/streaming processing, and a messaging queue.
This document discusses big data analytics. It defines big data as large, complex datasets that come from a variety of sources and are analyzed to reveal insights. It explains that big data is characterized by its volume, variety, velocity, variability, and complexity. The document outlines different types of data (structured, unstructured, semi-structured) and sources of data (internal, external). It also contrasts traditional data analytics with big data analytics and describes various analysis types including basic, advanced, and operationalized analytics. Finally, it provides an overview of common big data approaches like Hadoop, NoSQL databases, and massively parallel analytic databases.
This document provides an overview of Marco Torchiano's presentation on data visualization. It introduces Marco Torchiano and his research interests. The agenda outlines an introduction to data visualization, a brief history, visual perception, graphical integrity, visual encoding, and visual relationships. Examples are provided to demonstrate concepts like pre-attentive attributes, quantitative and categorical encoding, Gestalt principles, principles of integrity, and relationships within and between data. Common mistakes in data visualization are also discussed.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
1) The document provides study tips for the SAP C_TAW12_70 certification exam, including understanding concepts behind questions rather than cramming, reviewing the document twice, and checking for updates before the exam date.
2) Feedback on questions can be provided to feedback@passguide.com including exam number, version, page and question numbers.
3) Each PDF file contains a unique serial number associated with the purchaser's name and contact information for security purposes. Unauthorized distribution may result in legal action.
This document contains instructions for an assignment in Statistics for Management. It includes 6 questions about topics in statistics, such as descriptive statistics, probability, probability distributions, hypothesis testing, and forecasting. Students are asked to provide short answers for 1-2 mark questions and approximately 400 word answers for 10 mark questions. The questions cover definitions, calculations, explanations of statistical concepts, and applying concepts to examples and case studies.
The document discusses business intelligence and data warehousing. It describes the evolution of business intelligence from manual data retrieval and report preparation to modern integrated systems that provide analytics, dashboards, reporting, and key performance indicators. The Performa BI Suite is presented as a user-friendly business intelligence software that offers advanced visualization tools, multidimensional analysis, and integrated analytics, dashboards, and reporting in a single platform. Testimonials from users praise Performa for its ease of use, stability, and ability to meet reporting and analysis needs.
This document discusses clickstream analysis, which involves analyzing the paths users take while browsing websites. Clickstream data records users' clicks and is useful for market research and analyzing user behavior. The document then discusses how clickstream data can be collected from server logs, analyzed to identify popular and unpopular pages, and used to improve websites and target marketing strategies. It also discusses how clickstream data combined with other data sources can enable personalization.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
Data Visualization Design Best Practices WorkshopJSI
This document provides guidance on effective data visualization. It emphasizes starting with the audience and their needs, identifying the key story or message in the data, and using simple, clear design principles. Charts should be designed in 5-8 seconds to engage the audience. The document recommends several resources for choosing effective chart types and improving visualization skills. Overall, it stresses the importance of visualization in empowering stakeholders to make informed decisions.
This document provides an overview of Tableau, a data visualization software. It outlines the agenda for the presentation, which will cover connecting to data, visual analytics with Tableau, dashboards and stories, calculations, and mapping capabilities. Tableau allows users to connect to various data sources, transform raw data into interactive visualizations, and share dashboards or publish them online. It is a leading tool for data analysis and visualization.
The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
The 'macro view' on Big Query:
We started with an overview, some typical uses and moved to project hierarchy, access control and security.
In the end we touch about tools and demos.
Power BI is a self-service business intelligence tool that allows users to analyze and visualize data. It consists of Power BI Desktop, the Power BI web service, and the Power BI mobile app. Power BI Desktop is used to build reports and dashboards locally, while the web service allows users to publish, share, and collaborate on reports and dashboards online. To create a dashboard in Power BI, a user would connect to a data source, build visualizations with the data, publish the report to the web, combine reports into a dashboard, and then share the dashboard.
Team project - Data visualization on Olist company dataManasa Damera
This document discusses optimizing the analytics process for a Brazilian e-commerce company called Olist. It begins with an overview of the client scenario and scattered data. The goals are to normalize the data, optimize the ETL process, and automate analytics insights. The document describes plans to normalize the data into tables, extract data from CSV files, transform and clean the data, and load it into a PostgreSQL database. It discusses how analytical procedures and a dashboard can provide insights for different departments. Finally, it demonstrates the database interaction through a Metabase dashboard.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
The document discusses the key components of a big data architecture. It describes how a big data architecture is needed to handle large volumes of data from multiple sources that is too large for traditional databases. The architecture ingests data from various sources, stores it, enables both batch and real-time analysis, and delivers business insights to users. It also provides examples of Flipkart's data platform which includes components like an ingestion system, batch/streaming processing, and a messaging queue.
This document discusses big data analytics. It defines big data as large, complex datasets that come from a variety of sources and are analyzed to reveal insights. It explains that big data is characterized by its volume, variety, velocity, variability, and complexity. The document outlines different types of data (structured, unstructured, semi-structured) and sources of data (internal, external). It also contrasts traditional data analytics with big data analytics and describes various analysis types including basic, advanced, and operationalized analytics. Finally, it provides an overview of common big data approaches like Hadoop, NoSQL databases, and massively parallel analytic databases.
This document provides an overview of Marco Torchiano's presentation on data visualization. It introduces Marco Torchiano and his research interests. The agenda outlines an introduction to data visualization, a brief history, visual perception, graphical integrity, visual encoding, and visual relationships. Examples are provided to demonstrate concepts like pre-attentive attributes, quantitative and categorical encoding, Gestalt principles, principles of integrity, and relationships within and between data. Common mistakes in data visualization are also discussed.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
1) The document provides study tips for the SAP C_TAW12_70 certification exam, including understanding concepts behind questions rather than cramming, reviewing the document twice, and checking for updates before the exam date.
2) Feedback on questions can be provided to feedback@passguide.com including exam number, version, page and question numbers.
3) Each PDF file contains a unique serial number associated with the purchaser's name and contact information for security purposes. Unauthorized distribution may result in legal action.
This document contains instructions for an assignment in Statistics for Management. It includes 6 questions about topics in statistics, such as descriptive statistics, probability, probability distributions, hypothesis testing, and forecasting. Students are asked to provide short answers for 1-2 mark questions and approximately 400 word answers for 10 mark questions. The questions cover definitions, calculations, explanations of statistical concepts, and applying concepts to examples and case studies.
MATH 533 Education Specialist / snaptutorial.comMcdonaldRyan97
This document outlines the contents of the MATH 533 course, including homework assignments, quizzes, discussion questions, and projects for each week, as well as two final exam sets. Key elements covered include statistics, probability, hypothesis testing, confidence intervals, regression, and correlation. Completing all elements would provide comprehensive coverage of statistical analysis techniques and applications.
Question 1 – Exercise 8.3Why is it not possible in Example 8.1 o.docxIRESH3
This document contains information and questions related to exercises on variable costing, absorption costing, and contribution margin analysis. It provides cost and sales data for multiple companies and products, and asks the reader to calculate various financial metrics like variable costs, contribution margins, and net income under different costing methods and sales levels. The exercises are intended to help the reader practice converting between variable and absorption costing income statements and calculating contribution margins.
Six Sigma Green Belt - A company is receiving an unusually high number of ret...Suma Kamadod
This document contains a 35 question multiple choice exam on Six Sigma concepts including control charts, process capability, variation, and statistical process control. It provides contact information to obtain the answer sheet. The questions cover topics such as the purpose of control charts, assessing process capability, measures of precision and variation, conditions for a process to be in statistical control, control chart types, cause and effect diagrams, process capability charts, flow charts, team performance, standard deviation, randomization, and attribute vs variable data. Contact is provided to receive the answer sheet for this exam.
Six Sigma Green Belt - In analysis of variance, which of the following distri...Suma Kamadod
This document contains a 35 question multiple choice exam on Six Sigma concepts including control charts, process capability, variation, and statistical process control. It provides contact information to obtain the answer sheet. The questions cover topics such as the purpose of control charts, assessing process capability, measures of precision and variation, conditions for a process to be in statistical control, control chart types, cause and effect diagrams, process capability charts, and Six Sigma and Lean tools.
The document discusses marketing performance measurement and control systems. It covers 6 steps to developing an effective marketing strategy:
1. Set financial and productivity growth goals to increase shareholder value over the long and short term.
2. Define the customer value proposition to attract new customers and increase business from existing customers.
3. Establish a timeline for achieving short term financial goals through marketing activities.
4. Identify critical strategic themes of innovation, customer, and operations management to create and deliver customer value and improve internal processes.
5. Determine the human, information, and organizational resources needed to support the internal processes driving the strategies.
6. Develop an action plan that coordinates strategic initiatives and investments
1. The document discusses managing the personal selling function, focusing on key aspects like organizing the sales force, managing key accounts, and selecting and training high-performing account managers.
2. It emphasizes that effective sales force management through planning, organizing, directing, and controlling personal selling efforts is fundamental to a firm's success. This involves tasks like forecasting, determining the size of the sales force, and establishing goals and metrics to monitor performance.
3. Managing key accounts, which are large customers that demand special services, requires a dedicated key account manager and cross-functional team. High performing account managers excel at building relationships within the customer organization to design proposals that meet the customer's needs.
1. The document discusses various communication methods used for business-to-business (B2B) marketing and sales, with an emphasis on the importance of personal selling.
2. It notes that while other methods such as advertising, catalogs, and trade shows have roles to play, personal selling is still the primary vehicle for selling complex, expensive business products and services.
3. The document also discusses best practices for integrating different communication methods into effective, multi-channel marketing programs, and how to measure the impact of various promotional activities.
This document discusses pricing strategies and considerations for setting prices. It identifies the most important factors to consider which include pricing objectives, demand determinants, cost determinants, and competition. It then outlines the typical price-setting decision process which involves setting objectives, estimating demand and costs, examining competitors, and setting the price level. Specific strategies like price skimming, penetration pricing, and following the market leader are also summarized.
1. Effective supply chain management requires integrated computer systems and collaborative tools to synchronize activities across members in real time.
2. SCM has become a more important strategic variable that affects all aspects of business, including costs, customer service, and revenue.
3. The best companies are innovating their SCM through new technologies to improve speed and coordination between members.
The document discusses channels of distribution and go-to-market strategies for business markets. It describes selecting channels based on understanding customer needs and segments. Direct channels involve no intermediaries while indirect channels use representatives or distributors. Distributors take inventory and provide services like delivery. Representatives sell for multiple companies and are paid on commission. The channel design process involves defining customer segments, identifying their needs, and aligning channel functions to satisfy those needs.
The document discusses how businesses can shift from a product-focused approach to a customer-centered solutions approach. It notes that most companies think they deliver a superior customer experience when in fact most customers feel their experience was just average. By understanding customer problems and co-creating solutions with customers, companies can build stronger relationships and increase value and profitability. The document provides recommendations for how to market solutions instead of products by educating customers and focusing on the value of the solution rather than just features. It also discusses the benefits of a solutions approach like new growth opportunities and differentiation.
- Firms derive significant sales and profits from recently introduced products within the last 5 years. However, product innovation carries high risks and failure rates.
- There are two categories of strategic behavior for product development - induced and autonomous. Induced involves planned innovation within existing markets/products, while autonomous allows for more creative thinking outside current offerings.
- Successful management of innovation requires flexibility but also structure. Firms must anticipate customer needs, communicate in real-time, experiment with new ideas, and carefully manage project transitions.
This document discusses various topics related to marketing industrial products, including:
- Product positioning involves measuring customer perceptions of a product's attributes relative to competitors. Repositioning may involve emphasizing important attributes or better communicating advantages.
- New technologies often face a "chasm" between early adopters and mainstream customers. Crossing the chasm requires winning niche customers with 100% solutions to prove the technology for pragmatists.
- The "bowling alley strategy" aims to leverage success in one niche to spread adoption to surrounding segments, while the "main street strategy" pushes for rapid distribution to drive the product mainstream before it becomes a commodity.
RDEs are becoming major competitors due to lower labor costs and raw materials. 80% of the world's population lives in emerging markets like China, India, and Brazil. Hundreds of millions now form a middle class market. To compete globally, companies need strategies to meet needs in both low and high growth markets using new competitive models as power shifts east and RDEs rise.
- The document discusses strategies at three levels: corporate, business, and functional. At the corporate level, marketing assesses market attractiveness and promotes customer orientation. At the business level, firms compete through differentiation and positioning. At the functional level, resources are allocated to support business strategies.
- Developing strategies requires negotiation between different functional areas that may have competing interests. Successful marketing managers understand capabilities across functions and facilitate strategies responsive to customer needs.
- Four components contribute to strategy success: the customer interface, core strategy, strategic resources, and value network. These must be tied together through customer benefits, configuration, and boundaries.
The document discusses various topics related to business-to-business marketing segmentation and the purchasing process. It describes how purchases can be segmented based on complexity and corporate impact. It also discusses the benefits of e-procurement, reverse auctions, and centralized versus decentralized purchasing. Additionally, it examines the different roles that individuals can play in the buying center, including initiators, influencers, gatekeepers, deciders, purchasers, and users. Finally, it outlines factors like risk reduction, selective information processing, and confronting uncertainty that influence the purchasing decision.
This document discusses relationship marketing and the spectrum of buyer-seller relationships. It explains that relationship marketing focuses on establishing, developing and maintaining successful exchanges with customers. Buyer-seller relationships can range from transactional exchanges, where there is little ongoing relationship, to collaborative exchanges, where there are close, long-term relationships and commitments between buyers and sellers. The type of relationship depends on factors such as market conditions, the complexity of the purchase, and switching costs.
This document discusses the organizational buying process and the factors that influence it. It describes the typical stages in the buying process, including problem recognition, defining needs, specifying products, searching for suppliers, acquiring proposals, selecting suppliers, establishing order routines, and performance reviews. It also discusses the three main types of buying situations: new tasks, straight rebuys, and modified rebuys. Additionally, it outlines the environmental, organizational, group, and individual forces that shape organizational buying behavior and how marketers can understand and influence the process.
The document discusses business marketing and business markets. It defines business marketing as marketing to businesses, governments, and institutions rather than individual consumers. It notes that business products are used to make other products, become part of other products, aid operations, or are resold without change. Business markets include a variety of customers like commercial firms, governments, institutions, OEMs, wholesalers and retailers. Demand in business markets is derived from ultimate consumer demand and tends to fluctuate more. Relationship building is important in business marketing. The document also discusses supply chain management and categories of business customers.
This document defines distribution channels and marketing intermediaries. It explains that distribution channels are networks of organizations that make products available to consumers. Channel decisions are important as they affect other marketing strategies. Intermediaries increase efficiency and availability of goods to target markets. They provide contacts, experience, specialization, and scale. The document then lists common functions of distribution channels and types of institutions like wholesalers, agents, retailers, and discusses channel management decisions.
Cold supply chains are logistical systems that maintain ideal storage conditions for perishable products from origin to consumption. They utilize refrigerated warehouses, transportation like reefer trucks/ships, and packaging to extend shelf life. Key activities include procurement, transportation, pre-cooling, and storage. The objective is to reduce costs and waste while improving product quality and customer satisfaction. Industries like food, pharmaceuticals, and floriculture rely on cold chains. Effective cold chains utilize various technologies like dry ice, gel packs, refrigerated containers, and temperature monitoring to safely transport temperature-sensitive goods.
This document discusses various aspects of transportation and freight management. It covers strategies for optimizing freight efficiency including reducing costs, improving scheduling, and increasing load factors. It also describes responsibilities in freight management such as carrier evaluation and vehicle scheduling. Key factors affecting freight costs are identified as volumes, distance, product density, shape, handling requirements, type, and market dynamics. Different transportation modes like air, water, rail, truck, and intermodal are outlined and their selection criteria discussed.
The document discusses various supply chain management best practices and strategies. It describes tierization of suppliers to outsource complete modules rather than individual parts. Reverse logistics and recyclable packaging help process returns and dispose of materials sustainably. Vendor-managed inventory, milk runs, barcoding, and hub-and-spoke networks optimize transportation. 4PL providers help integrate these areas for improved supply chain coordination. Modern strategies like postponement, risk pooling, and RFID tracking further enhance supply chain efficiency.
The document discusses traditional procurement and e-procurement. Traditional procurement is time-consuming, lacks transparency, and has high transaction costs. E-procurement allows for quicker processing, global reach, transparency, and reduced costs through methods like reverse auctions, e-catalogs, and online ordering. While e-procurement provides benefits, it also faces constraints like infrastructure and reluctance to new systems. The e-procurement process involves request for quotation, offer evaluation, reverse auctions, purchase orders, acceptance, and payment tracking.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Challenges of Nation Building-1.pptx with more important
Mcq tableau.docx
1. Tableau Multiple Choice Questions And Answers
1. A Reference Band cannot be based on two fixed points.
a) False
b) True
Answer Explanation: a
2. A Reference Distribution plot cannot be along a continuous axis.
a) True
b) False
Answer Explanation: b
A Reference Distribution plot can be along a continuous axis.
3. Which of the following is not a Trend Line model
a) Linear Trend Line
b) Exponential Trend Line
c) Binomial Trend Line
d) Logarithmic Trend Line
Answer Explanation: c
Binomial Trend Line is not a Trend Line model.
4. The image below uses which map visualization?
a) Filled maps
b) Layered maps
c) WMS server maps
d) Symbol maps
Answer Explanation: c
5. Is it possible to deploy a URL action on a dashboard object to open a Web Page within a
dashboard rather than opening the system’s web browser?
a) True, with the use of Tableau Server
b) True, with the use of a Web Page object
c) False, not possible
d) True, requires a plug-in
Answer Explanation: b
True, with the use of a Web Page object it is possible to deploy a URL action on a dashboard
object to open a web page within a dashboard rather than opening the system’s web browser.
6. The Highlighting action can be disabled for the entire workbook.
a) True
b) False
2. Answer Explanation: a
From the toolbar the Highlighting action can be disabled for the entire workbook.
7. A sheet cannot be used within a story directly. Either sheets should be used within a dashboard,
or a dashboard should be used within a story.
a) True
b) False
Answer Explanation: b
A sheet can be used within a story directly.
8. How do you identify a continuous field in Tableau?
a) It is identified by a blue pill in the visualization.
b) It is identified by a green pill in a visualization.
c) It is preceded by a # symbol in the data window.
d) When added to the visualization, it produces distinct values.
Answer Explanation: b
It is identified by a green pill in a visualization
9. Is it possible to use measures in the same view multiple times (e.g. SUM of the measure and
AVG of the measure)?
a) No
b) Yes
Answer Explanation: b
Yes, measures can be used multiple times in the same view.
10. Sets can be created on Measures.
a) False
b) True
Answer Explanation: a
Sets can be created on dimensions.
Tableau Multiple Choice Interview Questions And Answer
11. For creating variable size bins we use _____________.
a) Sets
b) Groups
c) Calculated fields
d) Table Calculations
Answer Explanation: c
For creating variable size bins we use Calculated Fields.
3. 12. A good reason to use a bullet graph.
a) Analyzing the trend for a time period
b) Comparing the actual against the target sales
c) Adding data to bins and calculating count measure
d) Displaying the sales growth for a particular year
Answer Explanation: b
13. The line shown in the image below is a Reference Line. True or False?
a) true
b) false
Answer Explanation: b
The line shown in the image is a Trend Line.
14. Disaggregation returns all records in the underlying data source.
a) True
b) False
Answer Explanation: a
Disaggregation returns all records in the underlying data sources.
15. By definition, Tableau displays measures over time as a ____________.
a) Bar
b) Line
c) Histogram
d) Scatter Plots
Answer Explanation: b
By definition, Tableau displays measures over time as a Lines.
16. The icon associated with the field that has been grouped is a ______________.
a) Paper Clip
b) Set
c) Hash
d) Equal To
Answer Explanation: a
The icon associated with the field that has been grouped is a paper clip.
17. In the West region, which state’s sales fall within the Reference Band starting from average
sales of that region till median of sales? (Perform the below questions in Tableau 9.0 and connect
to the Saved Sample – Superstore dataset)
a) California
b) Colorado
c) Montana
d) New Mexico
4. Answer Explanation: b
18. Create a simple bar chart with Region and Total Expenses from the Sample- Superstore
dataset and Sample -Coffee Chain dataset, respectively. (Establish the link on State). Identify the
budgeted profit for the region having the 2nd highest total expenditure. (Connect to the Sample-
Coffee Chain access file using the CoffeeChain Query table)
a) 84850
b) 87680
c) 80231
d) 84823
Answer Explanation: b
19. In 2012, what is the percent contribution of sales for Decaf in the East market? (Perform all
the questions in Tableau 9.0 and connect to the Saved Sample-Superstore dataset)
a) 48.942%
b) 54.765%
c) 51.231%
d) 55.875%
Answer Explanation: a
48.942% is the percent contribution of sales of Decaf in 2012 in the East market.
20. In 2013, what is the percentage of total profit for Caffe Mocha falling under Major Market
(Market Size)?(Perform all the questions in Tableau 9.0 and connect to the Saved Sample-
Superstore dataset)
a) 60%
b) 45%
c) 58%
d) 55%
Answer Explanation: d
In 2013, the percentage of total profit for Caffe Mocha falling under Major Market is 55%.
Tableau Certification Questions And Answers
21. Create a heat map for Product Type, State, and Profit. Which state in the East market has the
lowest profit for Espresso?(Use the Sample- Coffee Chain dataset for the following questions)
a) Florida
b) Connecticut
c) New York
d) New Hampshire
Answer Explanation: d
New Hampshire has the lowest profit for Espresso, in the East market.
22. In 2012, what is the difference in budget profit, in Q3 from the previous quarter for major
market (Market Size)? (Use the Sample- Coffee Chain dataset for the following questions)
5. a) 630
b) -287
c) 667
d) 654
Answer Explanation: a
23. In which month did the running sales cross $30,000 for Decaf in Colorado and Florida? (Use
the Sample- Coffee Chain dataset for the following questions)
a) November 2013
b) September 2013
c) May 2013
d) December 2013
Answer Explanation: c
24. Create a bar chart with Product Type, Product, and Profit. Identify which of the following
products fall below the overall 99.9% Confidence Interval Distribution (Table across)? (Use the
Sample- Coffee Chain dataset for the following questions)
a) Decaf Espresso
b) Green Tea
c) Caffe Latte
d) Regular Espresso
Answer Explanation: b
25. Using quartiles, identify which of the following Espresso product has the highest distribution
of sales? (Use the Sample- Coffee Chain dataset for the following questions)
a) Decaf Espresso
b) Caffe Mocha
c) Caffe Latte
d) Regular Espresso
Answer Explanation: d
Regular Espresso has the highest distribution of sales in Espresso product.
26. In 2013, identify the state with the highest profit in the West market? (Use the Sample- Coffee
Chain dataset for the following questions)
a) Utah
b) Nevada
c) California
d) Washington
Answer Explanation: c
27. Create a scatter plot with State, Sales, and Profit. Identify the Trend Line with ‘R-Squared’
value between 0.7 to 0.8? (Use the Sample- Coffee Chain dataset for the following questions)
a) Linear Trend Line
6. b) Logarithmic Trend Line
c) Exponential Trend Line
d) Polynomial Trend Line with Degree 2
Answer Explanation: d
The Trend Line with ‘R-Squared’ value between 0.7 to 0.8 is a Polynomial Trend Line with Degree
2.
28. Identify the total expenses to sales ratio of the state with the lowest profit. (Use the Sample-
Coffee Chain dataset for the following questions)
a) 47.31%
b) 45.58%
c) 41.98%
d) 40.78%
Answer Explanation: b
29. Create a Combined Field with Product and State. Identify the highest selling product and its
state. (Use the Sample- Coffee Chain dataset for the following questions)
a) Colombian, California
b) Colombian, Texa
c) Lemon, Neva
d) Darjeeling, Iowa
Answer Explanation: a
30. What is the contribution of tea to the overall Profit in 2012? (Use the Sample- Coffee Chain
dataset for the following questions)
a) 24.323%
b) 22.664%
c) 20.416%
d) 21.765%
Answer Explanation: c
Tableau Multiple Choice Questions For Experienced
31. What is the average profit ratio for all the products starting with C? (Use the Sample- Coffee
Chain dataset for the following questions)
a) 30%
b) 25%
c) 33%
d) 20%
Answer Explanation: c
7. 32. What is the distinct count of area codes for the state with the lowest budget margin in small
markets? (Use the Sample- Coffee Chain dataset for the following questions)
a) 3
b) 1
c) 2
d) 6
Answer Explanation: b
33. Which product type does not have any of its product within the Top 5 Products by sales? (Use
the Sample- Coffee Chain dataset for the following questions)
a) Tea
b) Espresso
c) Coffee
d) Herbal Tea
Answer Explanation: a
34. In the Central region, the Top 5 Products by sales contributed _____ % of the total
expenditure. (Use the Sample- Coffee Chain dataset for the following questions)
a) 48.54%
b) 51.66%
c) 69.21%
d) 54.02%
Answer Explanation: d
In the Central region, the Top 5 Products by sales contributed 54.02 % of the total expenditure.
Beginners Level Tableau Interview Questions
1. What is the difference between Traditional BI Tools and Tableau?
Traditional BI Tools vs Tableau
Traditional BI Tools Tableau
1. Architecture has hardware limitations. 1. Do not have dependencies.
2. Based on a complex set of technologies.
2. Based on Associative Search which makes it
dynamic and fast
3. Do not support in-memory, multi-thread,
multi-core computing.
3. Supports in memory when used with
advanced technologies.
8. 4. Has a predefined view of data.
4. Uses predictive analysis for various business
operations.
2. What is Tableau?
Tableau is a business intelligence software.
It allows anyone to connect to the respective data.
Visualizes and creates interactive, shareable dashboards.
3. What are the different Tableau Products and what is the latest version of
Tableau?
Here is the Tableau Product family.
(i)Tableau Desktop:
It is a self service business analytics and data visualization that anyone can use. It translates
pictures of data into optimized queries. With tableau desktop, you can directly connect to data
from your data warehouse for live upto date data analysis. You can also perform queries without
9. writing a single line of code. Import all your data into Tableau’s data engine from multiple sources
& integrate altogether by combining multiple views in a interactive dashboard.
(ii)Tableau Server:
It is more of an enterprise level Tableau software. You can publish dashboards with Tableau
Desktop and share them throughout the organization with web-based Tableau server. It leverages
fast databases through live connections.
(iii)Tableau Online:
This is a hosted version of Tableau server which helps makes business intelligence faster and easier
than before. You can publish Tableau dashboards with Tableau Desktop and share them with
colleagues.
(iv)Tableau Reader:
It’s a free desktop application that enables you to open and view visualizations that are built in
Tableau Desktop. You can filter, drill down data but you cannot edit or perform any kind of
interactions.
(v)Tableau Public:
This is a free Tableau software which you can use to make visualizations with but you need to save
your workbook or worksheets in the Tableau Server which can be viewed by anyone.
4. What are the different datatypes in Tableau?
Tableau supports the following data-types:
5. What are Measures and Dimensions?
Measures are the numeric metrics or measurable quantities of the data, which can be analyzed
by dimension table. Measures are stored in a table that contain foreign keys referring uniquely to
10. the associated dimension tables. The table supports data storage at atomic level and thus, allows
more number of records to be inserted at one time. For instance, a Sales table can have product
key, customer key, promotion key, items sold, referring to a specific event.
Dimensions are the descriptive attribute values for multiple dimensions of each attribute, defining
multiple characteristics. A dimension table ,having reference of a product key form the table, can
consist of product name, product type, size, color, description, etc.
Tableau Interview Questions & Answers | Edureka
6. What is the difference between .twb and .twbx extension?
A .twb is an xml document which contains all the selections and layout made you have made
in your Tableau workbook. It does not contain any data.
A .twbx is a ‘zipped’ archive containing a .twb and any external files such as extracts and
background images.
7. What are the different types of joins in Tableau?
The joins in Tableau are same as SQL joins. Take a look at the diagram below to understand it.
8. How many maximum tables can you join in Tableau?
You can join a maximum of 32 tables in Tableau.
9. What are the different connections you can make with your dataset?
11. We can either connect live to our data set or extract data onto Tableau.
Live: Connecting live to a data set leverages its computational processing and storage. New
queries will go to the database and will be reflected as new or updated within the data.
Extract: An extract will make a static snapshot of the data to be used by Tableau’s data
engine. The snapshot of the data can be refreshed on a recurring schedule as a whole or
incrementally append data. One way to set up these schedules is via the Tableau server.
The benefit of Tableau extract over live connection is that extract can be used anywhere without
any connection and you can build your own visualization without connecting to database.
10. What are shelves?
They are Named areas to the left and top of the view. You build views by placing fields onto the
shelves. Some shelves are available only when you select certain mark types.
11. What are sets?
12. Sets are custom fields that define a subset of data based on some conditions. A set can be based
on a computed condition, for example, a set may contain customers with sales over a certain
threshold. Computed sets update as your data changes. Alternatively, a set can be based on
specific data point in your view.
12. What are groups?
A group is a combination of dimension members that make higher level categories. For example,
if you are working with a view that shows average test scores by major, you may want to group
certain majors together to create major categories.
13. What is a hierarchical field?
A hierarchical field in tableau is used for drilling down data. It means viewing your data in a more
granular level.
14. What is Tableau Data Server?
Tableau server acts a middle man between Tableau users and the data. Tableau Data Server allows
you to upload and share data extracts, preserve database connections, as well as reuse calculations
and field metadata. This means any changes you make to the data-set, calculated fields,
parameters, aliases, or definitions, can be saved and shared with others, allowing for a secure,
centrally managed and standardized dataset. Additionally, you can leverage your server’s
resources to run queries on extracts without having to first transfer them to your local machine.
Learn Tableau From Experts
Intermediate Level Tableau Interview Questions
15. What is Tableau Data Engine?
Tableau Data Engine is a really cool feature in Tableau. Its an analytical database designed to
achieve instant query response, predictive performance, integrate seamlessly into existing data
infrastructure and is not limited to load entire data sets into memory.
If you work with a large amount of data, it does takes some time to import, create indexes and
sort data but after that everything speeds up. Tableau Data Engine is not really in-memory
technology. The data is stored in disk after it is imported and the RAM is hardly utilized.
16. What are the different filters in Tableau and how are they different from
each other?
In Tableau, filters are used to restrict the data from database.
The different filters in Tableau are: Quick , Context and Normal/Traditional filter are:
13. o Normal Filter is used to restrict the data from database based on selected dimension or
measure. A Traditional Filter can be created by simply dragging a field onto the ‘Filters’
shelf.
o Quick filter is used to view the filtering options and filter each worksheet on a dashboard
while changing the values dynamically (within the range defined) during the run time.
o Context Filter is used to filter the data that is transferred to each individual worksheet.
When a worksheet queries the data source, it creates a temporary, flat table that is uses to
compute the chart. This temporary table includes all values that are not filtered out by either
the Custom SQL or the Context Filter.
17. How to create a calculated field in Tableau?
Click the drop down to the right of Dimensions on the Data pane and select “Create >
Calculated Field” to open the calculation editor.
Name the new field and create a formula.
Take a look at the example below:
18. What is a dual axis?
Dual Axis is an excellent phenomenon supported by Tableau that helps users view two scales of
two measures in the same graph. Many websites like Indeed.com and other make use of dual axis
to show the comparison between two measures and their growth rate in a septic set of years. Dual
axes let you compare multiple measures at once, having two independent axes layered on top of
one another. This is how it looks like:
14. 19. What is the difference between a tree map and heat map?
A heat map can be used for comparing categories with color and size. With heat maps, you can
compare two different measures together.
A tree map also does the same except it is considered a very powerful visualization as it can be
used for illustrating hierarchical data and part-to-whole relationships.
15. 20. What is disaggregation and aggregation of data?
The process of viewing numeric values or measures at higher and more summarized levels of the
data is called aggregation. When you place a measure on a shelf, Tableau automatically aggregates
the data, usually by summing it. You can easily determine the aggregation applied to a field
because the function always appears in front of the field’s name when it is placed on a shelf. For
example, Sales becomes SUM(Sales). You can aggregate measures using Tableau only for
16. relational data sources. Multidimensional data sources contain aggregated data only. In Tableau,
multidimensional data sources are supported only in Windows.
According to Tableau, Disaggregating your data allows you to view every row of the data source
which can be useful when you are analyzing measures that you may want to use both
independently and dependently in the view. For example, you may be analyzing the results from
a product satisfaction survey with the Age of participants along one axis. You can aggregate the
Age field to determine the average age of participants or disaggregate the data to determine at
what age participants were most satisfied with the product.
21. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example,
worksheet in an Excel file or tables in Oracle database
While blending requires two completely defined data sources in your report.
22. What are Extracts and Schedules in Tableau server?
Data extracts are the first copies or subdivisions of the actual data from original data sources. The
workbooks using data extracts instead of those using live DB connections are faster since the
extracted data is imported in Tableau Engine.After this extraction of data, users can publish the
workbook, which also publishes the extracts in Tableau Server. However, the workbook and
extracts won’t refresh unless users apply a scheduled refresh on the extract. Scheduled Refreshes
are the scheduling tasks set for data extract refresh so that they get refreshed automatically while
publishing a workbook with data extract. This also removes the burden of republishing the
workbook every time the concerned data gets updated.
23. How to view underlying SQL Queries in Tableau?
Viewing underlying SQL Queries in Tableau provides two options:
Create a Performance Recording to record performance information about the main
events you interact with workbook. Users can view the performance metrics in a workbook
created by Tableau.
Help -> Settings and Performance -> Start Performance Recording
Help -> Setting and Performance -> Stop Performance Recording.
Reviewing the Tableau Desktop Logs located at C:UsersMy DocumentsMy Tableau
Repository. For live connection to data source, you can check log.txt and tabprotosrv.txt
files. For an extract, check tdeserver.txt file.
24. How to do Performance Testing in Tableau?
Performance testing is again an important part of implementing tableau. This can be done by
loading Testing Tableau Server with TabJolt, which is a “Point and Run” load generator created to
perform QA. While TabJolt is not supported by tableau directly, it has to be installed using other
open source products.
17. 25. Name the components of a Dashboard.
Horizontal – Horizontal layout containers allow the designer to group worksheets and
dashboard components left to right across your page and edit the height of all elements at
once.
Vertical – Vertical containers allow the user to group worksheets and dashboard
components top to bottom down your page and edit the width of all elements at once.
Text – All textual fields.
Image Extract – A Tableau workbook is in XML format. In order to extracts images,
Tableau applies some codes to extract an image which can be stored in XML.
Web [URL ACTION] – A URL action is a hyperlink that points to a Web page, file, or other
web-based resource outside of Tableau. You can use URL actions to link to more information
about your data that may be hosted outside of your data source. To make the link relevant
to your data, you can substitute field values of a selection into the URL as parameters.
26. How to remove ‘All’ options from a Tableau auto-filter?
The auto-filter provides a feature of removing ‘All’ options by simply clicking the down arrow in the
auto-filter heading. You can scroll down to ‘Customize’ in the dropdown and then uncheck the
‘Show “All” Value’ attribute. It can be activated by checking the field again.
27. How to add Custom Color to Tableau?
Adding a Custom Color refers to a power tool in Tableau. Restart you Tableau desktop once you
save .tps file. From the Measures pane, drag the one you want to add color to Color. From the
color legend menu arrow, select Edit Colors. When a dialog box opens, select the palette drop-
down list and customize as per requirement.
28. What is TDE file?
TDE is a Tableau desktop file that contains a .tde extension. It refers to the file that contains data
extracted from external sources like MS Excel, MS Access or CSV file.
There are two aspects of TDE design that make them ideal for supporting analytics and data
discovery.
Firstly, TDE is a columnar store.
The second is how they are structured which impacts how they are loaded into memory and
used by Tableau. This is an important aspect of how TDEs are “architecture aware”.
Architecture-awareness means that TDEs use all parts of your computer memory, from RAM
to hard disk, and put each part to work what best fits its characteristics.
29. Mention whether you can create relational joins in Tableau without
creating a new table?
Yes, one can create relational joins in tableau without creating a new table.
30. How to automate reports?
You need to publish report to tableau server, while publishing you will find one option to schedule
reports.You just need to select the time when you want to refresh data.
31. What is Assume referential integrity?
18. In some cases, you can improve query performance by selecting the option to Assume Referential
Integrity from the Datamenu. When you use this option, Tableau will include the joined table in
the query only if it is specifically referenced by fields in the view.
32. Explain when would you use Joins vs. Blending in Tableau?
If data resides in a single source, it is always desirable to use Joins. When your data is not in one
place blending is the most viable way to create a left join like the connection between your primary
and secondary data sources.
33. What is default Data Blending Join?
Data blending is the ability to bring data from multiple data sources into one Tableau view,
without the need for any special coding. A default blend is equivalent to a left outer join.
However, by switching which data source is primary, or by filtering nulls, it is possible to emulate
left, right and inner joins.
34. What do you understand by blended axis?
In Tableau, measures can share a single axis so that all the marks are shown in a single pane.
Instead of adding rows and columns to the view, when you blend measures there is a single row
or column and all of the values for each measure is shown along one continuous axis. We can
blend multiple measures by simply dragging one measure or axis and dropping it onto an ex isting
axis.
35. What is story in Tableau?
A story is a sheet that contains a sequence of worksheets or dashboards that work together to
convey information. You can create stories to show how facts are connected, provide context,
demonstrate how decisions relate to outcomes, or simply make a compelling case. Each individual
sheet in a story is called a story point.
36. What is the difference between discrete and continuous in Tableau?
There are two types of data roles in Tableau – discrete and continuous dimension.
Discrete data roles are values that are counted as distinct and separate and can only take
individual values within a range. Examples: number of threads in a sheet, customer name
or row ID or State. Discrete values are shown as blue pills on the shelves and blue icons in
the data window.
Continuous data roles are used to measure continuous data and can take on any value
within a finite or infinite interval. Examples: unit price, time and profit or order quantity.
Continuous variables behave in a similar way in that they can take on any value. Continuous
values are shown as green pills.
37.How to create stories in Tableau?
There are many ways to create story in Tableau. Each story point can be based on a different view
or dashboard, or the entire story can be based on the same visualization, just seen at different
stages, with different marks filtered and annotations added. You can use stories to make a business
case or to simply narrate a sequence of events.
Click the New Story tab.
In the lower-left corner of the screen, choose a size for your story. Choose from one of the
predefined sizes, or set a custom size, in pixels.
By default, your story gets its title from its sheet name. To edit it, double-click the title. You
can also change your title’s font, color, and alignment. Click Apply to view your changes.
19. To start building your story, drag a sheet from the Story tab on the left and drop it into the
center of the view
Click Add a caption to summarize the story point.
To highlight a key takeaway for your viewers, drag a text object over to the story worksheet
and type your comment.
To further highlight the main idea of this story point, you can change a filter or sort on a
field in the view, then save your changes by clicking Update above the navigator box.
38. What is the DRIVE Program Methodology?
Tableau Drive is a methodology for scaling out self-service analytics. Drive is based on best
practices from successful enterprise deployments. The methodology relies on iterative, agile
methods that are faster and more effective than traditional long-cycle deployment.
A cornerstone of this approach is a new model of partnership between business and IT.
39. How to use group in calculated field?
By adding the same calculation to ‘Group By’ clause in SQL query or creating a Calculated Field in
the Data Window and using that field whenever you want to group the fields.
Using groups in a calculation. You cannot reference ad-hoc groups in a calculation.
Blend data using groups created in the secondary data source: Only calculated
groups can be used in data blending if the group was created in the secondary data source.
Use a group in another workbook. You can easily replicate a group in another workbook
by copy and pasting a calculation.
40. Mention what is the difference between published data sources and
embedded data sources in Tableau?
The difference between published data source and embedded data source is that,
Published data source: It contains connection information that is independent of any
workbook and can be used by multiple workbooks.
Embedded data source: It contains connection information and is associated with a
workbook.
41. Mention what are different Tableau files?
Different Tableau files include:
Workbooks: Workbooks hold one or more worksheets and dashboards
Bookmarks: It contains a single worksheet and its an easy way to quickly share your work
Packaged Workbooks: It contains a workbook along with any supporting local file data
and background images
Data Extraction Files: Extract files are a local copy of a subset or entire data source
Data Connection Files: It’s a small XML file with various connection information
Expert level Tableau Interview Questions
42. How to embed views onto Webpages?
You can embed interactive Tableau views and dashboards into web pages, blogs, wiki pages, web
applications, and intranet portals. Embedded views update as the underlying data changes, or as
their workbooks are updated on Tableau Server. Embedded views follow the same licensing and
permission restrictions used on Tableau Server. That is, to see a Tableau view that’s embedded in
a web page, the person accessing the view must also have an account on Tableau Server.
20. Alternatively, if your organization uses a core-based license on Tableau Server, a Guest account is
available. This allows people in your organization to view and interact with Tableau views
embedded in web pages without having to sign in to the server. Contact your server or site
administrator to find out if the Guest user is enabled for the site you publish to.
You can do the following to embed views and adjust their default appearance:
Get the embed code provided with a view: The Share button at the top of each view includes
embed code that you can copy and paste into your webpage. (The Share button doesn’t
appear in embedded views if you change theshowShareOptions parameter to false in the
code.)
Customize the embed code: You can customize the embed code using parameters that
control the toolbar, tabs, and more. For more information, see Parameters for Embed Code.
Use the Tableau JavaScript API: Web developers can use Tableau JavaScript objects in web
applications. To get access to the API, documentation, code examples, and the Tableau
developer community, see the Tableau Developer Portal.
43. Design a view in a map such that if user selects any state, the cities
under that state has to show profit and sales.
According to your question you must have state, city, profit and sales fields in your dataset.
Step 1: Double click on the state field
Step 2: Drag the city and drop it into Marks card.
Step 3: Drag the sales and drop it into size.
Step 4: Drag profit and drop it into color.
Step 5: Click on size legend and increase the size.
Step 6: Right click on state field and select show quick filter.
Step 7: Select any state now and check the view.
44. Think that I am using Tableau Desktop & have a live connection to
Cloudera Hadoop data. I need to press F5 to refresh the visualization. Is
there anyway to automatically refresh visualization every ‘x’ seconds
instead of pressing F5?
Here is an example of refreshing the dashboard for every 5 seconds.
All you need to do is replace the api src and server url with yours.
<!DOCTYPE html>
<html lang="en">
<head>
<title>Tableau JavaScript API </title>
<script type="text/javascript" src="http://servername/javascripts/api/tableau_v8.js"></script>
21. </head>
<div id="tableau Viz"></div>
<script type='text/javascript'>
var placeholderDiv = document.getElementById("tableau Viz");
var url = "http://servername/t/311/views/Mayorscreenv5/Mayorscreenv2";
var options={
hideTabs:True,
width:"100%",
height:"1000px"
};
var viz= new tableauSoftware.Viz(placeholderDiv,url,options);
setInterval (function() {viz.refreshDataAsync()},5000);
</script>
</body>
</html>
Some Additional Tricky Tableau Interview Questions
45. Suppose my license expires today, will users be able to view dashboards
or workbooks which I published in the server earlier?
If your server license expires today, your username on the server will have the role ‘unlicensed’
which means you cannot access but others can. The site admin can change the ownership to
another person so that the extracts do not fail.
46. Is Tableau software good for strategic acquisition?
22. Yes! For sure. It gives you data insight to the extent that other tools can’t. Moreover, it also helps
you to plan and point the anomalies and improvise your process for betterment of your company.
47. Can we place an excel file in a shared location and and use it to develop
a report and refresh it in regular intervals?
Yes, we can do it. But for better performance we should use Extract.
48. Can Tableau be installed on MacOS?
Yes, Tableau Desktop can be installed on both on Mac and Windows Operating System.
49. What is the maximum no. of rows Tableau can utilize at one time?
Tableau is not restricted by the no. of rows in the table. Customers use Tableau to access petabytes
of data because it only retrieves the rows and columns needed to answer your questions.
50. When publishing workbooks on Tableau online, sometimes a error about
needing to extract appears. Why does it happen occasionally?
This happens when a user is trying to publish a workbook that is connected to an internal server
or a file stored on a local drive, such as a SQL server that is within a company’s network.
1. A Reference Band cannot be based on two fixed points.
False
True
Explanation:
a)
2. A Reference Distribution plot cannot be along a continuous axis.
True
False
Explanation:
A Reference Distribution plot can be along a continuous axis.
3. Which of the following is not a Trend Line model
Linear Trend Line
Exponential Trend Line
Binomial Trend Line
Logarithmic Trend Line
Explanation:
Binomial Trend Line is not a Trend Line model.
4. The image below uses which map visualization?
23. Filled maps
Layered maps
WMS server maps
Symbol maps
Explanation:
d
5. Is it possible to deploy a URL action on a dashboard object to open a Web Page
within a dashboard rather than opening the system's web browser?
True, with the use of Tableau Server
True, with the use of a Web Page object
False, not possible
True, requires a plug-in
Explanation:
True, with the use of a Web Page object it is possible to deploy a URL action on a dashboard object to open a web page
within a dashboard rather than opening the system's web browser.
6. The Highlighting action can be disabled for the entire workbook.
True
False
Explanation:
From the toolbar the Highlighting action can be disabled for the entire workbook.
7. A sheet cannot be used within a story directly. Either sheets should be used within a
dashboard, or a dashboard should be used within a story.
True
False
Explanation:
A sheet can be used within a story directly.
8. How do you identify a continuous field in Tableau?
It is identified by a blue pill in the visualization.
It is identified by a green pill in a visualization.
It is preceded by a # symbol in the data window.
When added to the visualization, it produces distinct values.
Explanation:
b) It is identified by a green pill in a visualization
9. Is it possible to use measures in the same view multiple times (e.g. SUM of the
measure and AVG of the measure)?
24. No
Yes
Explanation:
Yes, measures can be used multiple times in the same view.
10. Sets can be created on Measures.
False
True
Explanation:
Sets can be created on dimensions.
11. For creating variable size bins we use _____________.
Sets
Groups
Calculated fields
Table Calculations
Explanation:
For creating variable size bins we use Calculated Fields.
12. A good reason to use a bullet graph.
Analyzing the trend for a time period
Comparing the actual against the target sales
Adding data to bins and calculating count measure
Displaying the sales growth for a particular year
Explanation:
d
13. The line shown in the image below is a Reference Line. True or False?
true
false
Explanation:
The line shown in the image is a Trend Line.
14. Disaggregation returns all records in the underlying data source.
True
False
Explanation:
Disaggregation returns all records in the underlying data sources.
15. By definition, Tableau displays measures over time as a ____________.
Bar
25. Line
Histogram
Scatter Plots
Explanation:
By definition, Tableau displays measures over time as a Lines.
16. The icon associated with the field that has been grouped is a ______________.
Paper Clip
Set
Hash
Equal To
Explanation:
The icon associated with the field that has been grouped is a paper clip.
17. In the West region, which state’s sales fall within the Reference Band starting from
average sales of that region till median of sales? (Perform the below questions in
Tableau 9.0 and connect to the Saved Sample - Superstore dataset)
California
Colorado
Montana
New Mexico
Explanation:
d
18. Create a simple bar chart with Region and Total Expenses from the Sample-
Superstore dataset and Sample -Coffee Chain dataset, respectively. (Establish the link
on State). Identify the budgeted profit for the region having the 2nd highest total
expenditure. (Connect to the Sample- Coffee Chain access file using the CoffeeChain
Query table)
84850
87680
80231
84823
Explanation:
d
19. In 2012, what is the percent contribution of sales for Decaf in the East market?
(Perform all the questions in Tableau 9.0 and connect to the Saved Sample-
Superstore dataset)
48.942%
26. 54.765%
51.231%
55.875%
Explanation:
48.942% is the percent contribution of sales of Decaf in 2012 in the East market.
20. In 2013, what is the percentage of total profit for Caffe Mocha falling under Major
Market (Market Size)?(Perform all the questions in Tableau 9.0 and connect to the
Saved Sample-Superstore dataset)
60%
45%
58%
55%
Explanation:
In 2013, the percentage of total profit for Caffe Mocha falling under Major Market is 55%.
21. Create a heat map for Product Type, State, and Profit. Which state in the East
market has the lowest profit for Espresso?(Use the Sample- Coffee Chain dataset for
the following questions)
Florida
Connecticut
New York
New Hampshire
Explanation:
New Hampshire has the lowest profit for Espresso, in the East market.
22. In 2012, what is the difference in budget profit, in Q3 from the previous quarter for
major market (Market Size)? (Use the Sample- Coffee Chain dataset for the following
questions)
630
-287
667
654
Explanation:
d
23. In which month did the running sales cross $30,000 for Decaf in Colorado and
Florida? (Use the Sample- Coffee Chain dataset for the following questions)
November 2013
27. September 2013
May 2013
December 2013
Explanation:
d
24. Create a bar chart with Product Type, Product, and Profit. Identify which of the
following products fall below the overall 99.9% Confidence Interval Distribution (Table
across)? (Use the Sample- Coffee Chain dataset for the following questions)
Decaf Espresso
Green Tea
Caffe Latte
Regular Espresso
Explanation:
d
25. Using quartiles, identify which of the following Espresso product has the highest
distribution of sales? (Use the Sample- Coffee Chain dataset for the following
questions)
Decaf Espresso
Caffe Mocha
Caffe Latte
Regular Espresso
Explanation:
Regular Espresso has the highest distribution of sales in Espresso product.
26. In 2013, identify the state with the highest profit in the West market? (Use the
Sample- Coffee Chain dataset for the following questions)
Utah
Nevada
California
Washington
Explanation:
d
27. Create a scatter plot with State, Sales, and Profit. Identify the Trend Line with ‘R-
Squared’ value between 0.7 to 0.8? (Use the Sample- Coffee Chain dataset for the
following questions)
Linear Trend Line
28. Logarithmic Trend Line
Exponential Trend Line
Polynomial Trend Line with Degree 2
Explanation:
The Trend Line with ‘R-Squared’ value between 0.7 to 0.8 is a Polynomial Trend Line with Degree 2.
28. Identify the total expenses to sales ratio of the state with the lowest profit. (Use the
Sample- Coffee Chain dataset for the following questions)
47.31%
45.58%
41.98%
40.78%
Explanation:
d
29. Create a Combined Field with Product and State. Identify the highest selling
product and its state. (Use the Sample- Coffee Chain dataset for the following
questions)
Colombian, California
Colombian, Texa
Lemon, Neva
Darjeeling, Iowa
Explanation:
d
30. What is the contribution of tea to the overall Profit in 2012? (Use the Sample-
Coffee Chain dataset for the following questions)
24.323%
22.664%
20.416%
21.765%
Explanation:
d
31. What is the average profit ratio for all the products starting with C? (Use the
Sample- Coffee Chain dataset for the following questions)
30%
25%
29. 33%
20%
Explanation:
d
32. What is the distinct count of area codes for the state with the lowest budget margin
in small markets? (Use the Sample- Coffee Chain dataset for the following questions)
3
1
2
6
Explanation:
d
33. Which product type does not have any of its product within the Top 5 Products by
sales? (Use the Sample- Coffee Chain dataset for the following questions)
Tea
Espresso
Coffee
Herbal Tea
Explanation:
d
34. In the Central region, the Top 5 Products by sales contributed _____ % of the total
expenditure. (Use the Sample- Coffee Chain dataset for the following questions)
48.54%
51.66%
69.21%
54.02%
Explanation:
In the Central region, the Top 5 Products by sales contributed 54.02 % of the total expenditure.