Business Analytics Question Bank
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This document contains 26 questions about various topics related to business analytics. The questions cover definitions of key terms like analytics, data center, MIS, outlier, and machine learning. They also ask about tracing the evolution of business analytics, examining the components and relationship to business strategy. Additional questions cover descriptive and diagnostic analytics, plotting line graphs of sales data, schema, marketing analytics, structured vs unstructured data, applications of analytics, and components of data warehousing.
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Introduction to data analytics
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
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This document provides an introduction to business analytics. It defines business analytics as combining data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with information and scenarios to make well-researched decisions. The document discusses the evolution of business analytics and its significance and uses, including making data-driven decisions and improving profitability. It also outlines some challenges of business analytics and describes the scopes of descriptive analysis, predictive analysis, and prescriptive analysis.
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by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
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Please like and comment for more slides.
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Data mining :Concepts and Techniques Chapter 2, data
slides contain:
Data Objects and Attribute Types,
Basic Statistical Descriptions of Data,
Data Visualization,
Measuring Data Similarity and Dissimilarity,
Summary,
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
Introduction to Data Visualization
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Digital data
This document discusses different types of digital data including structured, unstructured, and semi-structured data. It provides examples and characteristics of each type of data. Structured data is organized in rows and columns, like in a database. Unstructured data lacks a predefined structure or organization, like text documents, images, and videos. Semi-structured data has some structure but not a rigid schema, like XML files. The majority of organizational data is unstructured. Big data is also discussed, which is high-volume, high-velocity, and high-variety data that requires new technologies to capture, store, manage and analyze.
OLAP
Online analytical processing (OLAP) allows users to easily extract and analyze data from different perspectives. It originated in the 1970s and was formalized in 1993, with OLAP cubes organizing numeric facts by dimensions to enable fast analysis. OLAP provides operations like roll-up, drill-down, slice, and dice to analyze aggregated data across multiple systems. It offers advantages over relational databases for consistent reporting and analysis.
Decision Support System in MIS.pptx
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
Business Intelligence
The Institution's Innovation Council (Ministry of HRD initiative) and the Institution of Electronics and Telecommunication Engineers (IETE) invited me to grace "World Telecommunication & Information Society Day" on 18 May 2020.
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Project for System Analysis and Design (IS-6410).
By performing customer segmentation following are the three objectives which can be achieved
with the implementation of this new analytics system:
1. We can track the difference between loyal customers vs visitors, perform heat map
analysis of their browsing patterns.
2. Understanding customer demographics and to focus on high profitable segments.
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Introduction to Data Mining
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
Data mining techniques unit 1
This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.
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Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
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Introduction on Data and Information.
Also, this power-point includes:
1. Meaning of Data
2. Date Processing
3. What is Information ??
4. Difference between Data and Information
5. Information system
6. Characteristics of Information System
7. Need of Information system
Please like and comment for more slides.
- Uttar Tamang
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This document discusses data visualization, including why it is useful, techniques for visualizing big data, common data visualization techniques like bar charts and maps, tools for data visualization like Tableau and D3.js, and how InsideView uses data visualization. It notes that visualization is important because images can convey large amounts of information more easily than text, and that visualizing data allows people to see patterns, correlations, and geographic relationships in the data. Big data brings new challenges to visualization due to the speed, size, and diversity of large datasets.
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Data mining :Concepts and Techniques Chapter 2, data
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Xibms answer sheets mba , emba aravind 9901366442
ARAVIND
09901366442 – 09902787224
• XAVIER ANSWER SHEETS
• XIBMS ANSWER SHEETS
• XAVIER SOLUTION PAPERS
• MBA XAVIER ANSWER SHEETS
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The document discusses a market research report on the global retail analytics market. It provides an overview of the key findings of the report, including that the global retail analytics market is expected to grow at a CAGR of 21.47% until 2025. The market is segmented by component, deployment model, end-user, business function, application, and region. Some of the major players in the retail analytics market include SAP, Bridgei2i, SAS Institute, Qlik, Angoss, IBM, Oracle, Microsoft, Manthan, and Fujitsu.
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MKTG 229Analytics AssignmentDue in class – Tuesday November .docx
MKTG 229
Analytics Assignment
Due in class – Tuesday November 24th
For all questions, please cite any sources other than your textbook that you’ve used. First 6 questions 15 marks each Question 7 – 10 marks – Total 100
1. Before starting any campaign or project, what are the first 3 questions a Marketer needs to ask and answer? In your own words, why is it important to answer these questions first?
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b) Mobile device type report
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d) Bounce rate
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BONUS: Seasonality of visits
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a) Explain the steps involved in an A/B test.
b) As a Marketer working with a Graphic Designer, you both disagree on which image to use for an ad. How can you use an A/B test to resolve your disagreement?
4. An A/B test has been run comparing two purchase funnel experiences. As the Marketer, you must provide your assessment on which funnel to keep and which to “throw out”. Based on the data, which funnel had the best results? Provide your reasoning.
5. What does this heatmap tell us about Canadians’ engagement with our new Prime Minister?
6. What “story” do these heat maps tell? As a result, what would you recommend to the Marketer working on search engine marketing (SEM)
7. What is the most useful fact(s) you’ve learned from this chapter that will help you in your career? (10)
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Who Am I?
Sunita Patel, Manager, Seeker Experience at Workopolis
BSc Biology, MBA in Marketing
Other places: Trader (AutoTrader.ca, HomeTrader.ca, Buy&Sell.com), Hilroy, Heart & Stroke Foundation, Canadian Tire, Baxter, Federal Gov’t of Canada
I am a ‘scientific’ marketer I love having data to make decisions!
I’m here to discuss how Marketers use web analytics in my job everyday, as it relates to your chapter
Today’s conversation
CH 18: Data Analytics
This is the most important chapter in your book.
Why?
It contains content that will apply to your Marketing world NO MATTER which area of Marketing you go into.
Why?
In the age of technology – everything is measurable and customizable. Data is at the heart of it.
Example:
I am a Product Manager. I use data to make decisions about how to design and evaluate my website every day
My colleagues use data to design and evaluate campaigns
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Where data can sit within an organization
Web Analytics
Product Mktg
Product Mgmt
Adv / Comms / PR
Editorial
Email
UX / UI
Traffic Acq
(PPC, Affiliate)
Develop-ers
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As the analyst, what would y ...
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Why BI is important for C-Level Executives in BFSI
Business intelligence is used to extract insights from data and aid decision-making. Make the most of big data to better understand customers and improve their interactions with financial institutions.
In this article, we looked at how C-level executives can use BI tools to achieve their objectives.
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BUSINESS DEVELOPMENT WITH AI
The document discusses how artificial intelligence (AI) can be used for business development. It explores how AI technologies like data analytics, predictive analytics, machine learning algorithms and computer vision can help businesses optimize operations, improve customer experience and identify new opportunities. While AI has potential benefits, the document also notes challenges like bias in AI systems and potential job displacement. It concludes that AI could significantly impact businesses if developed and used ethically with appropriate safeguards.
PREDICTIVE BUSINESS INTELLIGENCE: CONSUMER GOODS SALES FORECASTING USING ARTI...
Business competition between manufacturing businesses in Indonesia is getting
tighter along with the development of businesses from competing companies that have
similar businesses. One strategy that can be applied by this company is Business
Intelligence, that is by utilizing the data that is already available to help in better
decision making, such as decisions based on facts stored in the data, precisely namely
the lack of errors in the presentation of reports, and fast that is, cut down on the time
for making the usual report. The method proposed by the author is a method that can
be used to predict sales value based on existing sales data (sales forecasting). By
implementing Business Intelligence and data mining, companies can learn from the
data that has been collected, can evaluate the performance of the sales department,
can understand market trends from the products sold, and can predict future sales
levels. In addition, Business Intelligence can display detailed transaction data
recapitulation quickly.
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The Data Economy: 2016 Horizonwatch Trend Brief
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Year 2: CF = Rs. 30,000; PV Factor @ 10% = 0.826; PV = Rs. 30,000 * 0.826 = Rs. 24,780
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Here are the solutions to the numerical problems:
i) BEP in units = Total Fixed Cost / Contribution per unit
= Rs. 1,50,000 / (Rs. 15 - Rs. 10)
= 1,50,000/Rs. 5
= 30,000 units
ii) BEP in amount = Total Fixed Cost / P/V Ratio
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Here are the key steps to solve this problem using EBIT-EPS approach:
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