Solutions-oriented Analysis possessing a unique combination of Analytical skills includes data-science, machine learning, business analysis, big-data technology like Hadoop and application development experience in top-tier Retail organizations.
Solutions-oriented Analysis possessing a unique combination of skills, including business analysis, quality assurance testing and applications development experience in top-tier Retail organizations.
Shiban SQ has a dual masters in business analytics and MBA with 3+ years of experience in analytics consulting, business creation, and project management. They led teams and managed client relationships. Research projects included machine learning tools like R, SAS, and Python, resulting in two co-authored papers. They have education in business analytics, marketing, and computer engineering. Relevant skills include predictive modeling, business intelligence, data visualization, project management, and languages like R, Python, SAS, and SQL. Professional experience includes research associate conducting machine learning research, creating models as a corporate manager, sales executive, and intern developing a business model.
The document is a curriculum vitae for Sudheera. It summarizes their professional experience in statistical data analysis and machine learning for clients in retail, consumer packaged goods, and healthcare. They have over 7 years of experience developing analytical solutions including demand forecasting, product assortment selection, and customer segmentation using SAS, SPSS, R, and other tools. Currently they work as a Module Lead at Mindtree developing recommendation engines for retailers using algorithms like logistic regression and random forests.
Executive Degree In Business Analytics-Kelley School of BusinessGopi Reddy
The document outlines the course topics for a business analytics program which includes defining the business analytics process, performing analytics tasks with SAS and Excel, constructing and interpreting predictive models using large datasets, applying various data mining techniques, performing data exploration, and computing predictive accuracy measures. Additionally, the program covers unsupervised modeling, data visualization, data warehousing, dimensional modeling, and other related topics to support business decisions and manage risks.
This document discusses business analytics. It defines business analytics as using data, statistical and quantitative analysis, explanatory and predictive models to gain insights and support decision-making. The document outlines the typical business analytics process, including understanding the business objectives, assessing the situation, collecting and preparing data, developing analytic models, evaluating and reporting results, and deploying the outcomes. It provides examples of how analytics can be used to drive personalized customer services, optimize people management decisions, and conduct real-time sentiment analysis of social media data for an FMCG company. The document concludes with lessons learned, emphasizing the importance of continuous learning, gaining experience through projects and mentoring, and having confidence in one's abilities.
This document discusses using big data for tourism demand forecasting and identifies some issues and challenges. It outlines how predictive analytics and data mining techniques can help understand tourists better and improve tourism competitiveness. Traditional time series models can be combined with mixed frequency models to forecast low frequency tourism demand using high frequency big data. However, challenges include generalizing patterns from big data analysis, finding talent skilled in both new technologies and data interpretation, overcoming data access issues, and identifying new ways to leverage big data for tourism forecasting.
The document describes the key features of a customer analytics platform called Quiterian Analytics. It allows users to integrate customer data from multiple sources, explore and visualize the data, enrich and cleanse the data, perform advanced analytics and data mining, create dashboards, and automate marketing campaigns. The platform aims to provide a complete view of customers and help companies gain insights, improve strategic decision making, and anticipate customer behavior.
The document advertises a Business Analytics program that teaches skills for data-driven decision making. It covers topics like business analytics, marketing analytics, operations analytics, and data mining. Successful alumni get a degree from IIM Calcutta. The program benefits participants by improving job performance, teaching relevant skills, and providing career development and networking opportunities through interactions with faculty and peers. It provides a solid foundation in analytical tools and techniques to help professionals adapt to new competitive environments.
Solutions-oriented Analysis possessing a unique combination of skills, including business analysis, quality assurance testing and applications development experience in top-tier Retail organizations.
Shiban SQ has a dual masters in business analytics and MBA with 3+ years of experience in analytics consulting, business creation, and project management. They led teams and managed client relationships. Research projects included machine learning tools like R, SAS, and Python, resulting in two co-authored papers. They have education in business analytics, marketing, and computer engineering. Relevant skills include predictive modeling, business intelligence, data visualization, project management, and languages like R, Python, SAS, and SQL. Professional experience includes research associate conducting machine learning research, creating models as a corporate manager, sales executive, and intern developing a business model.
The document is a curriculum vitae for Sudheera. It summarizes their professional experience in statistical data analysis and machine learning for clients in retail, consumer packaged goods, and healthcare. They have over 7 years of experience developing analytical solutions including demand forecasting, product assortment selection, and customer segmentation using SAS, SPSS, R, and other tools. Currently they work as a Module Lead at Mindtree developing recommendation engines for retailers using algorithms like logistic regression and random forests.
Executive Degree In Business Analytics-Kelley School of BusinessGopi Reddy
The document outlines the course topics for a business analytics program which includes defining the business analytics process, performing analytics tasks with SAS and Excel, constructing and interpreting predictive models using large datasets, applying various data mining techniques, performing data exploration, and computing predictive accuracy measures. Additionally, the program covers unsupervised modeling, data visualization, data warehousing, dimensional modeling, and other related topics to support business decisions and manage risks.
This document discusses business analytics. It defines business analytics as using data, statistical and quantitative analysis, explanatory and predictive models to gain insights and support decision-making. The document outlines the typical business analytics process, including understanding the business objectives, assessing the situation, collecting and preparing data, developing analytic models, evaluating and reporting results, and deploying the outcomes. It provides examples of how analytics can be used to drive personalized customer services, optimize people management decisions, and conduct real-time sentiment analysis of social media data for an FMCG company. The document concludes with lessons learned, emphasizing the importance of continuous learning, gaining experience through projects and mentoring, and having confidence in one's abilities.
This document discusses using big data for tourism demand forecasting and identifies some issues and challenges. It outlines how predictive analytics and data mining techniques can help understand tourists better and improve tourism competitiveness. Traditional time series models can be combined with mixed frequency models to forecast low frequency tourism demand using high frequency big data. However, challenges include generalizing patterns from big data analysis, finding talent skilled in both new technologies and data interpretation, overcoming data access issues, and identifying new ways to leverage big data for tourism forecasting.
The document describes the key features of a customer analytics platform called Quiterian Analytics. It allows users to integrate customer data from multiple sources, explore and visualize the data, enrich and cleanse the data, perform advanced analytics and data mining, create dashboards, and automate marketing campaigns. The platform aims to provide a complete view of customers and help companies gain insights, improve strategic decision making, and anticipate customer behavior.
The document advertises a Business Analytics program that teaches skills for data-driven decision making. It covers topics like business analytics, marketing analytics, operations analytics, and data mining. Successful alumni get a degree from IIM Calcutta. The program benefits participants by improving job performance, teaching relevant skills, and providing career development and networking opportunities through interactions with faculty and peers. It provides a solid foundation in analytical tools and techniques to help professionals adapt to new competitive environments.
This document is a resume for Amanda Ran Yang, who has a Master's degree in Business Analytics and experience in data analysis, machine learning, and software development. She currently works as a Data Scientist at Facts and Measures, LLC, where her responsibilities include data visualization, hypothesis testing, model building, and database design using tools like R, SQL, and Tableau. Previously she interned at a marketing department and led a career planning organization while obtaining dual Bachelor's degrees in China.
Industry researchers at Gartner announced in April 2012 that the worldwide business intelligence, analytics, and performance management software market surpassed the US$12 Billion level in 2011, a 16.4% increase over the previous year. This statistic is among many pointing to the need for both groups to apply what management guru Peter Senge proclaimed decades ago in The Fifth Discipline: the need for a learning organization. This presentation focuses on three learning areas for anyone in the business analytics profession. First, we analysts need to learn what the markets and industries are saying today. We discuss recent trends which show how analytics will shape the future. Second, we need to learn what group learning options are available. From industry conferences (such as the PASS BA Conference, and virtual PASS sessions) to free MOOCs (massive open online courses), we have more options available to improve our knowledge. Finally, we need to learn what leadership roles our groups can have. We can leverage social networks (including PASS) and social media -- both individually and as organizations -- to communicate passion.
Business analytics course with NSE India CertificationIMS Proschool
The document provides information about a Business Analytics certification course offered by IMS Proschool. The 3-month course covers topics such as statistical techniques, data mining, Excel, R, and SAS. Students learn to apply analytics to domains like finance, marketing, and supply chain. The course can be taken online, in a live virtual classroom, or in person. Graduates will be equipped to work as business analysts, data scientists, and in other roles requiring data analytics skills.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
This document discusses how business intelligence (BI) can be used to strategically manage human resources. It defines BI as collecting and analyzing large amounts of customer, vendor, market and internal data. Choosing a BI tool requires defining a strategy aligned with business goals and requirements. Human resource management aims to maximize employee performance to achieve organizational objectives through policies and systems. The relationship between BI and HR management is explored, as is how BI can influence business performance through efficiency, identifying opportunities, and empowering decision makers. The conclusion suggests that using BI can provide a competitive advantage by analyzing data and ensuring the insights reach all parts of an organization.
This presentation is based on the article Simplify Your Analytics Strategy by Narendra Mulani.I have made this presentation
as a part of my data internship course
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Michele Vincent has over 10 years of experience in business and marketing analytics across various industries. She currently works as a Manager of Marketing Analytics and Strategy Insights at Smile Brands, Inc., where she spearheads marketing analytics initiatives to improve business performance and increase revenues. Previously, she held analytics roles at DIRECTV and Experian, focusing on predictive modeling, campaign optimization, and data-driven decision making. Vincent has technical skills in SAS, SQL, Excel, and other analytics tools. She holds an M.S. in Computer Science and a B.S. in Mathematics with a Statistics major.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.
Define, describe, deploy how to build an analytical framework Peter Spangler
This document discusses building an analytical framework with three steps: define, describe, and deploy. It provides examples from Lyft of how they used data science to grow rides by defining problems like delays in driver pickups, describing data through exploration and visualization, and deploying solutions through experimentation. The framework emphasizes clear problem definitions, learning from data, and activating insights through experiment design and measurement to inform business decisions.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
This document provides an overview of analytics systems and dimensional modeling. It defines key concepts like operational vs analytics systems, measures and dimensions, star schemas, and common analytical operations. Operational systems focus on transaction execution while analytics systems measure processes and support historical analysis. Dimensional modeling organizes data by facts (measures) and dimensions (context) to enable easy retrieval. Star schemas consist of a fact table linked to dimension tables and support slicing, dicing, drilling, and rolling up data. SQL and MDX are query languages used for analytics in relational and multi-dimensional databases respectively.
Mainak Bhattacharjee has experience with SAS, Excel, and SPSS from coursework. He has undertaken projects analyzing retail business performance using Excel macros and designing a credit scorecard using SAS logistic regression. His technical skills include advanced Excel, SAS, SPSS, and R. He has a postgraduate degree in economics from Jadavpur University with specializations in econometrics, game theory, and international economics. He published papers on banking sector reforms in India and the impact of devaluation on India's current account deficit.
Marketing analytics
PREDICTIVE ANALYTICS AND DATA SCIENCECONFERENCE (MAY 27-28)
Surat Teerakapibal, Ph.D.
Lecturer, Department of Marketing
Program Director, Doctor of Philosophy Program in Business Administration
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Analytics Staffing Models of Health Systems That Compete Well Using DataThotWave
This document discusses how healthcare organizations can succeed in the new healthcare economy by continually improving how they manage data, develop insights, and operationalize analytics. It outlines various industry trends driving changes, such as the transition to value-based care and increased consumerism. The document then presents different staffing models for analytics functions: centralized, decentralized, and center of excellence. It provides examples of organizations using each model and discusses the advantages and challenges of each. It emphasizes that the most important thing is finding the right model for each organization's unique situation.
Our Journey to Self-Discovery: Synthesizing 30 Years of Data Pipelines with K...Neo4j
The document discusses using a knowledge graph to improve data pipelines at LPL Financial. It describes the author's history with knowledge graphs at previous companies and how LPL is using Neo4j to model account, client, and household relationships as well as financial concepts. It then presents the problem of increasing data volumes straining current pipelines. The document proposes mapping data sources, logical models, processing details, and more to a knowledge graph to gain benefits like optimizing pipelines, engaging stakeholders, and moving closer to intent-based data access. Potential enterprise benefits include better decisions, risk management, and becoming more knowledge-driven.
T Siva Rama Sarma has 14 years of experience in data mining and mathematical modeling. He currently works as an Associate Consultant at Tata Consultancy Services, where he has developed recommendation systems and data quality analytics. His areas of expertise include statistical analysis, machine learning algorithms, and scientific programming. He has experience applying these skills to projects in domains like retail, customer segmentation, drug development, and collections.
Joji Antony Zacharias is an experienced data analyst based in Abu Dhabi, UAE. He currently works for Etisalat as part of their data modelling team, where he creates models to predict customer churn and segmentation. Previously he worked for Absolutdata Research analyzing clients' business needs. He has strong skills in SAS, Teradata, Excel, VBA and R.
Kaviraj Nair has over 10 years of experience in financial analytics, reporting, and sales. He has expertise in SAS programming, statistical modeling, and database marketing. His experience includes roles managing partner relationships, analyzing customer behavior, and creating reports. He holds degrees from IIM Calcutta and Mumbai University.
This document is a resume for Amanda Ran Yang, who has a Master's degree in Business Analytics and experience in data analysis, machine learning, and software development. She currently works as a Data Scientist at Facts and Measures, LLC, where her responsibilities include data visualization, hypothesis testing, model building, and database design using tools like R, SQL, and Tableau. Previously she interned at a marketing department and led a career planning organization while obtaining dual Bachelor's degrees in China.
Industry researchers at Gartner announced in April 2012 that the worldwide business intelligence, analytics, and performance management software market surpassed the US$12 Billion level in 2011, a 16.4% increase over the previous year. This statistic is among many pointing to the need for both groups to apply what management guru Peter Senge proclaimed decades ago in The Fifth Discipline: the need for a learning organization. This presentation focuses on three learning areas for anyone in the business analytics profession. First, we analysts need to learn what the markets and industries are saying today. We discuss recent trends which show how analytics will shape the future. Second, we need to learn what group learning options are available. From industry conferences (such as the PASS BA Conference, and virtual PASS sessions) to free MOOCs (massive open online courses), we have more options available to improve our knowledge. Finally, we need to learn what leadership roles our groups can have. We can leverage social networks (including PASS) and social media -- both individually and as organizations -- to communicate passion.
Business analytics course with NSE India CertificationIMS Proschool
The document provides information about a Business Analytics certification course offered by IMS Proschool. The 3-month course covers topics such as statistical techniques, data mining, Excel, R, and SAS. Students learn to apply analytics to domains like finance, marketing, and supply chain. The course can be taken online, in a live virtual classroom, or in person. Graduates will be equipped to work as business analysts, data scientists, and in other roles requiring data analytics skills.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
This document discusses how business intelligence (BI) can be used to strategically manage human resources. It defines BI as collecting and analyzing large amounts of customer, vendor, market and internal data. Choosing a BI tool requires defining a strategy aligned with business goals and requirements. Human resource management aims to maximize employee performance to achieve organizational objectives through policies and systems. The relationship between BI and HR management is explored, as is how BI can influence business performance through efficiency, identifying opportunities, and empowering decision makers. The conclusion suggests that using BI can provide a competitive advantage by analyzing data and ensuring the insights reach all parts of an organization.
This presentation is based on the article Simplify Your Analytics Strategy by Narendra Mulani.I have made this presentation
as a part of my data internship course
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Michele Vincent has over 10 years of experience in business and marketing analytics across various industries. She currently works as a Manager of Marketing Analytics and Strategy Insights at Smile Brands, Inc., where she spearheads marketing analytics initiatives to improve business performance and increase revenues. Previously, she held analytics roles at DIRECTV and Experian, focusing on predictive modeling, campaign optimization, and data-driven decision making. Vincent has technical skills in SAS, SQL, Excel, and other analytics tools. She holds an M.S. in Computer Science and a B.S. in Mathematics with a Statistics major.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.
Define, describe, deploy how to build an analytical framework Peter Spangler
This document discusses building an analytical framework with three steps: define, describe, and deploy. It provides examples from Lyft of how they used data science to grow rides by defining problems like delays in driver pickups, describing data through exploration and visualization, and deploying solutions through experimentation. The framework emphasizes clear problem definitions, learning from data, and activating insights through experiment design and measurement to inform business decisions.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
This document provides an overview of analytics systems and dimensional modeling. It defines key concepts like operational vs analytics systems, measures and dimensions, star schemas, and common analytical operations. Operational systems focus on transaction execution while analytics systems measure processes and support historical analysis. Dimensional modeling organizes data by facts (measures) and dimensions (context) to enable easy retrieval. Star schemas consist of a fact table linked to dimension tables and support slicing, dicing, drilling, and rolling up data. SQL and MDX are query languages used for analytics in relational and multi-dimensional databases respectively.
Mainak Bhattacharjee has experience with SAS, Excel, and SPSS from coursework. He has undertaken projects analyzing retail business performance using Excel macros and designing a credit scorecard using SAS logistic regression. His technical skills include advanced Excel, SAS, SPSS, and R. He has a postgraduate degree in economics from Jadavpur University with specializations in econometrics, game theory, and international economics. He published papers on banking sector reforms in India and the impact of devaluation on India's current account deficit.
Marketing analytics
PREDICTIVE ANALYTICS AND DATA SCIENCECONFERENCE (MAY 27-28)
Surat Teerakapibal, Ph.D.
Lecturer, Department of Marketing
Program Director, Doctor of Philosophy Program in Business Administration
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Analytics Staffing Models of Health Systems That Compete Well Using DataThotWave
This document discusses how healthcare organizations can succeed in the new healthcare economy by continually improving how they manage data, develop insights, and operationalize analytics. It outlines various industry trends driving changes, such as the transition to value-based care and increased consumerism. The document then presents different staffing models for analytics functions: centralized, decentralized, and center of excellence. It provides examples of organizations using each model and discusses the advantages and challenges of each. It emphasizes that the most important thing is finding the right model for each organization's unique situation.
Our Journey to Self-Discovery: Synthesizing 30 Years of Data Pipelines with K...Neo4j
The document discusses using a knowledge graph to improve data pipelines at LPL Financial. It describes the author's history with knowledge graphs at previous companies and how LPL is using Neo4j to model account, client, and household relationships as well as financial concepts. It then presents the problem of increasing data volumes straining current pipelines. The document proposes mapping data sources, logical models, processing details, and more to a knowledge graph to gain benefits like optimizing pipelines, engaging stakeholders, and moving closer to intent-based data access. Potential enterprise benefits include better decisions, risk management, and becoming more knowledge-driven.
T Siva Rama Sarma has 14 years of experience in data mining and mathematical modeling. He currently works as an Associate Consultant at Tata Consultancy Services, where he has developed recommendation systems and data quality analytics. His areas of expertise include statistical analysis, machine learning algorithms, and scientific programming. He has experience applying these skills to projects in domains like retail, customer segmentation, drug development, and collections.
Joji Antony Zacharias is an experienced data analyst based in Abu Dhabi, UAE. He currently works for Etisalat as part of their data modelling team, where he creates models to predict customer churn and segmentation. Previously he worked for Absolutdata Research analyzing clients' business needs. He has strong skills in SAS, Teradata, Excel, VBA and R.
Kaviraj Nair has over 10 years of experience in financial analytics, reporting, and sales. He has expertise in SAS programming, statistical modeling, and database marketing. His experience includes roles managing partner relationships, analyzing customer behavior, and creating reports. He holds degrees from IIM Calcutta and Mumbai University.
Sabina Sanjel is seeking a position that utilizes her 8+ years of experience in marketing data analytics. She has a Master's Degree in Statistics and is proficient in SAS programming and statistical modeling techniques. Currently she is an Associate Manager at Best Buy where she develops analytical models, performs data analysis, and interprets results to inform business decisions. Previously she held data analysis roles at Deluxe Corporation and UnitedHealth Group where she specialized in marketing analytics, predictive modeling, and reporting.
Karan Dhapade is a data science professional with experience in big data, data exploration, manipulation, and analysis of large datasets. He currently works at Mu Sigma Business Solutions, where he leads a team providing analytical services to a major online search advertising client. Some of the projects he has worked on include analyzing the impact of tool providers on advertiser performance, identifying preferred brand combinations and user characteristics to improve targeting, and measuring the effect of budget subsidies. Karan has skills in tools like Excel, Power BI, SQL, R and programming languages like SCOPE and SAS. He has a B.Tech in electronics from NIT Raipur and experience managing teams and clients in agile work environments.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
This document contains the resume of Manish C. Dhamange. It summarizes his professional experience as a Business Analyst for Tata Consultancy Services, where he performs marketing mix modeling and provides insights to clients. It also outlines his prior experience as a Database Analyst at eClerx Services Ltd. His educational qualifications include an MBA and B.Sc. in IT. Technical skills and personal details are also included.
Deepika Nayak is a senior consultant and delivery lead with over 7 years of experience in market research for the retail industry. She has strong skills in analytics tools like Spotfire, Xcelsius, Alteryx, KNIME, Microsoft Excel, SQL, R, and PowerPoint. Currently she manages a team of 16 people providing consulting services and analytical solutions to clients. Previously she has worked on projects involving web analytics, price analysis, consumption forecasting, and creating reports using retail audit and syndicated data.
Rupesh Kumar is a senior consultant with over 3 years of experience in market research, business insights, and data analysis for the CPG and retail industries. He has a strong technical background working with tools like IRI databases, MS Excel, Tableau, and R. He is seeking a role that allows him to further develop his quantitative and qualitative skills through exposure to various business analysis aspects within dynamic companies.
Kevin Yang is a data analyst seeking a position. He has a Master's degree in Marketing Analytics from the University of Maryland and a Bachelor's degree in Business Administration from Yanbian University in China. He has 2 years of work experience conducting data analysis and reporting for various companies in China. His technical skills include SAS, R, SQL, Excel, Hadoop and Tableau. He is fluent in English and Mandarin.
This document contains the resume of Jisu Behera, who has over 15 years of experience in data science and analytics roles. She has extensive experience building machine learning models for credit risk assessment, fraud detection, and other domains. Her technical skills include Python, machine learning algorithms like random forest and neural networks, and tools like TensorFlow, Keras, and Spark. She is currently a Data Science Manager at HCL Technologies, where she builds credit risk models and provides analytics support.
Jamaluddin has over 7 years of experience as a Business Analyst in retail, ecommerce, and digital media. He has expertise in requirements gathering, documentation, and testing. Some of his skills include ecommerce CMS, Oracle ATG, MicroStrategy dashboards, digital marketing analytics, and Agile methodologies like Scrum. He has worked on projects for companies like Gap Inc, Amazon, and Target, focusing on areas like online ordering, payments, inventory management, and website optimization.
Rajan Kumar is a senior business analyst with over 4 years of experience in analytics for pharmaceutical, retail, and financial industries. He has strong skills in SQL, Excel, VBA, SAS, and Tableau for data analysis and insights. His experience includes incentive compensation planning, sales alignment, digital marketing analytics, and automating repetitive processes. He provides strategic recommendations and dashboards to clients to support business operations and goals.
The document describes course descriptions for the Master of Science in Business Analytics program at ASU. The courses cover topics such as introduction to enterprise analytics, introduction to applied analytics, data mining, applied regression models, data-driven quality management, analytical decision modeling, business analytics strategy, marketing analytics, and an applied project course. The applied project course challenges students to understand business contexts, identify relevant analytics tools and frameworks, and apply their data science skills to real-world team projects.
Lakshmana Das Behara is an analytics professional with over 10 years of experience in data science, solution design, and R&D. He has expertise in statistical modeling, machine learning, and designing analytics products for industries such as hospitality, telecom, retail, and hi-tech. Behara has worked on predictive modeling projects for time sheet violations, payment delays, and sales returns. Currently he is an Assistant Manager at Tata Consultancy Services in Hyderabad focusing on analytics and insights.
This curriculum vitae summarizes Chirag Shah's experience in business intelligence and data analysis. He has over 15 years of experience in roles managing BI teams and developing reports and dashboards. His technical skills include SAS, SQL, Excel, and Microsoft BI tools. He holds a Bachelor's degree in Commerce and certifications in Visual Basic and Agile fundamentals. His most recent role was as Manager of Business Intelligence at Bankwest Australia, where he led a team of analysts and automated many reporting processes.
Ashwath Sivalingam is seeking a challenging career utilizing his skills in consumer insights and research. He has over 6 years of experience as a senior business analyst at TCS, where he utilized statistical tools and databases to analyze consumer behavior and provide analytical solutions to clients in the retail sector. He also has 2 years of previous experience at TCS analyzing creditworthiness and preparing financial reports for a bank. He has an MBA in marketing and finance and is proficient in various software programs, databases, and data visualization tools relevant to consumer insights.
Syeda Shamema Sultana is a professionally experienced data analyst seeking a data analyst position. She has over 10 years of experience in data analysis, reporting, segmentation, and dashboard building. She is proficient in various tools including MS Excel, MS SQL, Salesforce, and Tableau.
Pradip Krishnamoorthy - Experienced Business and Project Managerpradip krishnamoorthy
Pradip Krishnamoorthy has over 10 years of experience in management roles across various industries. He has a proven track record of implementing projects, optimizing processes, improving profitability and managing customer relationships. He is proficient with various tools including MS Office, Salesforce, SQL and has experience managing teams.
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
1. YASHWANTH M.R
# 191, 13th cross, Near Giber tools, Kariyanapalya, St. Thomas town post, Bangalore, Karnataka-560084
Cell : 9731919456 | Email ID : yashwanth8890@gmail.com, yashu_150@yahoo.co.in
Career Objectives
To involve myself in an organization, with highly motivated, and challenging attitude
to reach scalable achievements in my chosen arena through value creation and
generation at every phase of career.
Passionate about explaining data science to non-technical business audiences.
Summary
Solutions-oriented Analysis possessing a unique combination of Analytical skills
includes data-science,machine learning, business analysis,big-data technology like
Hadoop and application development experience in top-tier Retail organizations.
Software Skills andTools
R, Revolution R Enterprise, R-Studio
MS SQL
Tableau
Excel(Advance)
Hadoop(Base)
SPSS
SAS(Base)
Python(Base)
Latex
Accomplishments
Crowned Data-Science toolbox course from Coursera.
Additional course comprises Getting & Cleaning data, R-Programming, Big-data,
Business Analytics and Machine learning.
Seldom developed data products using data-science functionality named R-Shiny.
Won “Best Performance Team Award” for Holiday Season project, 2014.
Educational Qualifications
Master ofScience - Statistics 2011
Manasa Gangotri – Mysore, Karnataka
With an aggregate of 62.45% from Mysore University.
Bachelor ofScience - Mathematics, Statistics,Computer Science 2009
Yuvaraja’s College – Mysore, Karnataka
With an aggregate of 72.11% from Mysore University.
Curriculum Vitae
2. Experience
Sr. Analyst Oct 2013 to Present
Ugam Solutions – Bangalore, Karnataka
Developed statistical models ensure business oriented decision makings.
Created strategies to develop and expand existing customers resulted in leveraging
monthly sales.
Worked as a team member to provide the highest level of service to customers.
Shared the platform development knowledge with customers making business
recommendations.
Responsible for implementing all business-building and relationship-building
expectations with uniquely assigned accounts and clients.
Reorganized the sales floor to meet company demands & directed clients in sales and
inventory-maintenance engagement.
Analyzed marketing information and translated it into strategic plans.
Dynamic pricing as a pricing strategy based on algorithms setting highly flexible
prices on current market demands.
Pricing Intelligence technique offering optimized price enhancing stakes requirement
for retailers using price sensitivity guidelines.
Incorporating OpenText Content Intelligence & Optimization providing actionable
insights & deployment of low-cost management based on Content Analytical
Solutions.
Jr. Analyst Oct 2011 to Oct 2013
Predictive Analytics Solutions Private Limited – Bangalore, Karnataka
Build statistical models, predictive models under on-core objectives.
Proactively involved in development of Web-Portal that signifies Simulations.
Established market growth strategies resulted in new units generating yearly sales.
Leveraged skills in cementing healthy client relationship geared towards generating
business and leading workforce.
Roles and Responsibilities
Collecting, collating and carrying out complex data analysis in support of
management & client requests.
Involved in reporting statisticalinsights working with colleagues and senior
managers.
Analyzing raw data, drawing conclusions & developing recommendations.
Designing, developing and implementing newly established functionalities.
Advising on the suitability of methodologies and suggesting improvements while
carrying out specified data processing and statistical techniques.
Monitoring the suitability for government organizations namely Airport Authority of
India, NISTADS, CRRI.
Applying skills using R-Studio to develop newest methods into the solutions.
Facilitate meetings with clients to gather and document requirements and explore
potential insights.
Assist in coordinating business analyst tasks on information technology projects and
provide support to team members.
3. Core Competencies andAnalytics/Statistical work
Model specifications includes Simple(Multiple) Linear Regression, Logistic
Regression(Binary, Multinomial) which describes relationships among variables.
Regression diagnostics that confirms the goodness offit of the model and the
statistical significance of the estimated parameters.
Differences between group means and their associated procedure using ANOVA.
Estimating the characteristics of whole population using various Sampling methods.
Discovering the patterns in large datasets using machine learning techniques such as
CART/CHAID, Neural Networks, K-means Clustering, SVM, Random-Forest &
Association Rules.
Correlational methods of describing underlying structures driving data values using
Principal Component Analysis(PCA) & Factor Analysis.
Summarizing a set of data in two-dimensional graphical form assessing
Correspondence Analysis(CA).
Simulating Predictions/Probabilities based on previously observed values using Time
Series, Forecasting and Predictive Models.
Enhancing Price Elasticity(for supply/sales)models through recommendations based
on Dynamic-Pricing.
Further technical experiments using R Studio namely R2HTML, Sweave,
WordCloud, Xtable, Shiny, rCharts & RPubs.
Extra/Co-Curricular Achievements
Represented Mysore district in Weight lifting.
Completed two years of training in NCC (National Cadet Corps).
Personal Details
Date of Birth: 07.04.1988
Nationality: Indian
Marital Status: Single
Languages: Kannada, English, Hindi
Declaration
I, Yashwanth M.R hereby declare that all the above details given by me are true to the best of
my Knowledge.
Best Regards,
Yashwanth M.R