Post Graduate Program in Data Science and Business Analytics

McCombs School of Business

in partnership with Great Learning

McCombs School of Business

in partnership with Great Learning

Disclaimer

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Full disclaimer.

Who should attend

The program is for you if, you:

  • Like solving problems in a structured manner.
  • Love extracting insights from numbers to create insightful stories.
  • Want to impact business decisions through evidence gathered from data.
  • Want to inculcate 21st century competencies and build a strong career through them.
  • Want to keep pace with a business world that’s becoming increasingly data-driven.

About the course

The program uniquely combines a comprehensive curriculum, covering the most widely-used tools and techniques in the industry, with a hands-on learning approach. A structured learning journey keeps you on track throughout as you achieve your weekly learning milestones with your mentor and benefit from their rich professional experience.

Following a “learn by doing” pedagogy, the program offers you the opportunity to apply your skills and knowledge in real-time every week through interactive mentor-led practice sessions, quizzes, assignments, hands-on projects, culminating in a 6-week-long Capstone Project at the end of the program. As you do so, you come to truly appreciate the nuances of data and build your portfolio in the process.

On a whole, the program empowers you with the skills, body of work, and job market insights you need to find the right career opportunities in data science or lead data science efforts in your current organisation.

Curriculum

Our carefully-crafted curriculum has been designed to provide you with the breadth and depth you need to lead Data Science & Analytics efforts at any organisation. Covering the most widely-used tools and techniques, and a variety of industry examples, it builds your foundations to make you job-ready.

Foundations

The ‘Foundations’ block will empower you with the fundamentals of statistics, Python, and domain-specific business knowledge to set the foundations on which the rest of the course will be built.

Module 0: Pre-work

  • Basics of Programming
  • Introduction to Python

Module 1: Python Foundations

Build the foundational skills for data analysis with Python, such as importing, reading, manipulating, and visualizing data.

  • Introduction to Python Programming
  • NumPy, Pandas
  • Exploratory Data Analysis
  • Matplotlib, Seaborn

Module 2: Business Statistics

Understand the role of statistics in helping organizations take effective decisions, learn its most widely-used tools and learn to solve business problems using analysis, data interpretation and experiments.

  • Probability and Probability Distributions
  • Sampling Distribution and Central Limit Theorem
  • Hypothesis Testing and Associated errors
  • ANOVA and Chi-square test

Techniques

The ‘Techniques’ block will empower you with a thorough grounding in the most widely-used analytics and data science techniques so that you can approach any business problem with ease.

Module 3: Supervised Learning - Foundations

Explore the fundamentals of Supervised Machine Learning, its key concepts and types. You will also learn how to pre-process data to prepare it for modelling.

  • Data Preparation for Modeling
  • Linear Regression - Simple Linear Regression, Multiple Linear Regression, Goodness of Fit, Measures of Regression Fit

Module 4: Supervised Learning - Classification

Learn the conceptual frameworks of building classification models for accurate prediction in business contexts through popular ML approaches such as Logistic Regression and Decision Trees.

  • Logistic Regression
  • Decision Trees
  • Evaluation of Classification Models, ROC and AUC

Module 5: Ensemble Techniques

Ensemble methods help to improve the predictive performance of Machine Learning models. In this course, you will learn about Ensemble methods such as ‘Random Forest’ that combine several Machine Learning techniques into one predictive model in order to decrease variance, bias, or improve predictions.

  • Ensemble Methods - Bagging, Boosting and Stacking
  • Random Forest
  • AdaBoost, GBM, XGM, XGBM

Module 6: Model Tuning

Model building is an iterative process. Employing Feature Engineering techniques along with a careful model selection exercise helps to improve the model. Further, tuning the model is an important step to arrive at the best possible result. This module talks about the steps and processes around these.

  • Feature Engineering
  • Sampling and Smote, Regularization
  • Pipelining
  • Model Performance Measures

Module 7: Unsupervised Learning

Unsupervised Learning finds hidden patterns or intrinsic structures in data. In this course, you will learn about commonly-used clustering techniques like K-Means Clustering and Hierarchical Clustering.

  • K-means Clustering
  • Hierarchical Clustering

Self-Paced Module: Time Series Forecasting

Time Series Analysis is used for prediction problems that involve a time component. In this module, you will build foundational knowledge of Time Series Analysis in Python and its applications in business contexts.

  • Introduction to Time Series
  • Seasonality
  • Decomposition

Visualization and Insights

The ‘Visualization and Insights’ block will help you in representing data in a visual format for easy consumption and quick derivation of insights.

Module 8: Data Visualization with Tableau

Master the fundamentals of communicating information efficiently to business users via information graphics. Learn to recognize visual characteristics of data, choose appropriate display mechanisms, and transform data into actionable insights through Data Visualization with Tableau.

  • Essential Design Principles Of Tableau
  • Creating Visualizations With Tableau
  • Telling Stories With Visualization

Domain exposure

The ‘Domain Exposure’ block will provide a gateway into real-life problems from varied domains and teach you how to solve these problems using principles of data science and analytics.

Self-paced: Marketing and Retail Analytics

Learn the applications of data analytics to Marketing and Retail. Understand how marketing analytics can be utilized to further marketing objectives and measure, improve, and predict performance.

  • Marketing and Retail Terminologies: Review
  • Customer Analytics
  • Retail Dashboards
  • Customer Churn
  • Association Rules Mining

Self-paced: Web and Social Media Analytics

Learn how the data collected from websites and social media can be used to make business decisions through different types of web and social media analytics.

  • Web Analytics: Understanding the Metrics
  • Basic & Advanced Web Metrics
  • Google Analytics: Demo and Hands-on Supply Chain & Logistics Analytics
  • Text Mining

Self-paced: Supply Chain and Logistics Analysis

Learn how supply chain analytics can help businesses predict future demand, decide on inventory, understand customer needs, and optimise business costs.

  • Introduction to Supply Chain
  • Demand Uncertainty
  • Inventory Control & Management
  • Inventory Classification Methods
  • Procurement Analytics
  • Inventory Modeling (Reorder Point, Safety Stock)
  • Advanced Forecasting Methods

Self-paced: Finance And Risk Analytics

Learn the applications of data analytics in Finance and Risk Management such as fraud detection, credit risk, probability of default modeling, etc.

  • Why Credit Risk-Using a Market Case Study
  • Comparison of Credit Risk Models
  • Overview of Probability of Default (PD) Modeling
  • Fraud Detection
  • PD Models, Types of Models, Steps to Making a Good Model
  • Market Risk
  • Value at Risk- Using Stock Case Study Project

Module 9: Capstone Project

Career Support Services: Resume and LinkedIn Profile Review, Interview Preparation, 1 : 1 Mentorship and ePortfolio

Post Graduate Certificate from The University of Texas at Austin

Experts

Kumar Muthuraman

Biography Kumar Muthuraman is the H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk and Operations Management and the Department of Finance. He received his Ph.D. from Stanford University. Dr. Muthuraman’s research focuses on decision making under uncertainty. A...

Abhinanda Sarkar

Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at ...

R Vivekanand

Vivek Anand is a data visualization consultant with 10 years of experience. His area of specialization includes Marketing and Econometrics. Vivek has an MBA from Monash University Melbourne Vic. He has worked as Sales & Marketing professional handling teams of leading Indian hospitality brand...

Raghavshyam Ramamurthy

Raghavshyam (Shaam) Ramamurthy is a data visualization consultant with 15 years of experience across the globe. He worked in the US for 10 years across a variety of industries like manufacturing, chemical processing, and utilities. He consults on Visual analytics, KPI management, Dashboard develo...

Dan Mitchell

Dan received his PhD in Information, Risk, & Operations Management from the University of Texas. He then was an assistant professor at Singapore University of Technology and Design in the Engineering Systems and Design pillar. His main research interests are focused on financial engineering, ...

Videos and materials

Post Graduate Program in Data Science and Business Analytics at McCombs School of Business

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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