Overview:

The Data Analytics for Business Results is a 15-hour live, virtual, and practical training. This training will equip participants with key data analytics concepts and skills across Data analytics domains.

Though the discipline of data and analytics has existed for many decades, the analytical models, data management, and software applications underpinning these analytics have evolved significantly. The way data is collected and used in business mean that enterprises are looking for professionals who can understand, analyze, and use the data for improved business performance. In this backdrop, this training is designed for experienced Business Professionals and builds on one’s technical and managerial competencies. Participants will learn real life examples on how data analytics can be applied to various areas of business. This training has a strong focus on the application of data and insights for business performance.

This training uses field-tested business analytics tools and techniques. The training material is constantly updated to match the latest trends and industry best practices. This training uses a variety of approaches including live lessons, classroom discussions, videos, group activities, pre-class readings, case studies, individual exercises, and guest speaker(s). Post training, the instructor is available for any short discussion with the students via email, LinkedIn, phone, or video conferencing tools.

What You Will Learn:

At the completion of the program, students will be able to:

• Identify business opportunities and use-cases for data and analytics
• Improve the adoption of data and analytics solutions in the organization by collaborating with Business, IT, and Data teams based on proven best practices
• Map the process flow and identify activities that add value
• Improve technical competency and decision-making using data based on trade-offs
  
  

Livestream Schedule

Sept. 5, 2023, 10:00 AM-12:00 PM ET
Sept. 7, 2023, 10:00 AM-12:00 PM ET
Sept. 12, 2023, 10:00 AM-12:00 PM ET
Sept. 14, 2023, 10:00 AM-12:00 PM ET
Sept. 19, 2023, 10:00 AM-12:00 PM ET
Sept. 21, 2023, 10:00 AM-12:00 PM ET
Sept. 26, 2023, 10:00 AM-12:00 PM ET
Sept. 28, 2023, 10:00 AM-12:00 PM ET

Course Outline

Session 1: Introduction To Data Analytics

• Introduction to Data Analytics
• Competitive Advantage with Data Analytics
• Drivers for Data Analytics
• Types of Analytics and Data Science Techniques Taxonomy
• Data Analytics Lifecycle
• Business Data, Characteristics and Types
• IT Systems and types
• Data Lifecyle and Data Quality
   

Session 2: Descriptive Analytics – Part 1 (Exploratory Descriptive Analytics)

• Introduction to Statistics
• Measuring Business Performance with Exploratory Data Analytics
• Measures of Central Tendency and Variation
• Exploratory Data Analytics
• Data Profiling for Data Quality (EDA)
  
  

Session 3: Descriptive Analytics – Part 2 (Associative & Inferential Descriptive Analytics)

• Introduction to Associative Data Analytics
• Correlation – Pearson and Spearman
• Apriori Techniques
• Strategic Data Acquisition for Analytics
• Fundamentals of Inferential Data Analytics
• Hypothesis Testing
• Inferential Data Analytics (T-Test, A/B Testing, & ANOVA)
  
  

Session 4: Predictive Analytics

• Fundamentals of Predictive Analytics
• Hypothesis formulation
• 5 Types of variables in Predictive analytics
• Regression Models – Simple Linear Regression and Multiple Linear Regression
• Predictive Data Analytics Techniques
• Evaluating Analytics Models
• Exercise on Multiple Linear Regression (MLR)
  
  

Session 5: Essentials of Machine Learning

• Fundamentals of ML (Machine Learning)
• Key characteristics of ML Models
• Supervised & Unsupervised ML Algorithms
• Statistical Paradoxes
  
  

Session 6: Prescriptive Analytics

• Introduction to Prescriptive Analytics
• Prescriptive Analytics for Business Optimization
• Applying Prescriptive Analytics Techniques for Optimal Results
• Prescriptive Data Analytics in Solver
  
  

Session 7: Data Visualization and Decision Science

• Overview of Data Visualization & MAD Framework
• Data Visualization principles of Edward Tufte
• Introduction to Decision Making
• Decision Making Types and Process
• Decision Making Models (Maximax, Maximin, Minimax, EMV, and EVPI)
  
  

Session 8: Other Data Analytics Topics

• Data Products and Data Monetization
• Data Storytelling including Gestalt Principles
• Good Analytics v/s Bad Analytics
• Data Analytics Case Studies – Energy, Engineering, and Retail/CPG
• Managing your careers in Data Analytics
• Summary and Wrap-up