MicroMasters Program in Business Analytics - ColumbiaX
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Apply methods, tools, and software for acquiring, managing/storing, and accessing structured and unstructured data
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Prepare data for statistical analysis, perform basic exploratory and descriptive analysis, and apply statistical techniques to analyze data
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Apply descriptive, predictive and prescriptive analytics to business modeling and decision-making
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Demonstrate orally, and in writing, the ability to explain complex analytical models and results
Columbia’s MicroMasters program in Business Analytics will empower learners with the skills, insights and understanding to improve business performance using data, statistical and quantitative analysis, and explanatory and predictive modelling to help make actionable decisions.
The curriculum is designed to provide learners with a series of courses that emphasizes the use of statistical analysis, computing tools, and mathematical models to predict the outcomes of various business decisions, and identify the best implementation. These courses are instructional-led and each course has 10-12 weeks of lecture plus an additional final exam week.
BAMM.101x: Analytics in Python
Learn the fundamentals of programming in Python and develop the ability to analyze data and make data-driven decisions.
Data is the lifeblood of an organization. Competency in programming is an essential skill for successfully extracting information and knowledge from data.
The goal of this course is to introduce learners to the basics of programming in Python and to give a working knowledge of how to use programs to deal with data.
In this course, we will first cover the basics of programming and then focus on using Python on the entire data management process from data acquisition to analysis of data big data and small data.
This is an intensive hands-on course that will equip and reward learners with proficiency in data management skills.
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Become familiar with working with relational databases, using SQL based languages such as MySQL, dealing with formatted data (XML, JSON, etc.)
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Use Python to work with and analyze data from databases as well as from the web
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Week 1: Python review
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Week 2: Python review
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Week 3: Data interchange formats: JSON and XML
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Week 4: Web scraping and web crawling
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Week 5: Database basics: Relational databases
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Week 6: SQL
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Week 7: Data analysis and visualization I
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Week 8: Data analysis and visualization II
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Week 9: Text mining
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Week 10: Analysis of networks
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Week 11: Machine learning: Part 1
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Week 12: Machine learning: Part 2
We will review basic Python programming concepts in week 1 and 2 and no prior programming experience is necessary. But, if you have had some exposure to programming you will get more out of this class.
No specific math capability is required though it will be helpful if you are familiar with the basic concepts of algebra, set theory, and probability.
8 - 10 hours per week for 12 weeks
BAMM.102x: Data, Models and Decisions in Business Analytics
Learn fundamental tools and techniques for using data towards making business decisions in the face of uncertainty.
In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.
The main objectives of this course are the following:
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Introduce fundamental techniques towards a principled approach for data-driven decision-making
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Quantitative modeling of dynamic nature of decision problems using historical data
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Learn various approaches for decision-making in the face of uncertainty
Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.
Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice.
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Fundamental concepts from probability, statistics, stochastic modeling, and optimization to develop systematic frameworks for decision-making in a dynamic setting
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How to use historical data to learn underlying models and patterns
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Optimization methods and software such as Gurobi to solve decision problems under uncertainty in business applications
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Introduction to Probability: Random variables; Normal, Binomial, Exponential distributions; applications
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Estimation: sampling; confidence intervals; hypothesis testing
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Regression: linear regression; dummy variables; applications
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Linear Optimization; Non-linear optimization; Discrete Optimization; applications
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Dynamic Optimization; decision trees
Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.
8 - 10 hours per week for 12 weeks
BAMM.103x: Demand and Supply Analytics
Learn how to use data to develop insights and predictive capabilities to make better business decisions.
How do airlines decide when to increase ticket prices? Should a hotel charge less per night for a long stay than a short one? Why do some software companies bundle very different products together? How should a fashion retailer decide when do start discounting clothes? Why do so many discounted rates end in ".99"? How should a company balance the risk of holding too much inventory on hand and the risk of turning away customers? Does it ever make sense for retailers to lie to suppliers about how much they will need to order? Should retailers with multiple locations hold most of their inventory in a central warehouse or at the individual locations?
These are only a small sample of the operational and pricing challenges all businesses regularly face. These challenges are often addressed individually and in isolation but, in reality, all of these decisions interact with each other. This class looks at the demand and supply management challenges faced by companies in various industries and provides an introduction to the tools that can be used to address these challenges.
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To identify, evaluate, and capture business analytic opportunities that create business value
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Build models to support and help make managerial and business decisions
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Basic analytical methods and their applications
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Analyze case studies on organizations that successfully deployed analytical techniques
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Week 1: Introduction
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Week 2: Static price optimization
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Week 3: Dynamic price optimization
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Week 4: Price differentiation
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Week 5: Quantity based revenue management
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Week 6: Network revenue management & overbooking
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Week 7: Customized pricing and consumer choice models
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Week 8: Markdown management and behavioral issues in pricing
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Week 9: Introduction to inventory management
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Week 10: Stochastic inventory management
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Week 11: Miscellaneous topics in inventory management
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Week 12: Final review
Undergraduate probability, statistics, linear algebra and calculus. Students should have familiarity with basic programming concepts in some procedural programming language.
8 - 10 hours per week for 12 weeks
BAMM.104x: Marketing Analytics
Develop quantitative models that leverage business data to forecast sales and support important marketing decisions.
Marketers want to understand and forecast how customers purchase products and services and how they respond to marketing initiatives.
Learn how analytics help businesses drive marketing to maximize its effectiveness and optimize return on investment (ROI).
In this course, part of the Business Analytics MicroMasters program, discover how to develop quantitative models that leverage business data, statistical computation, and machine learning to forecast sales and marketing impact for:
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customer relationship management
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market segmentation
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value creation
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communication
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monetization
You will learn how to use probabilistic models and optimization tools to model customer demand forecasts, pricing sensitivity, lifetime value and how to leverage such data to make optimal decisions on designing new products, marketing segmentation and strategy.
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Demand forecasting using customer-base models and statistical approaches
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Market segmentation methods and best practices for identifying potential customer segments and focused targeting
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Computation of Customer Lifetime Value for analyzing customer, brand loyalty and forecasting revenue in the short and long run
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Factors to consider while designing and introducing new products to the market
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Calculating Optimal Pricing for products and services to get the best ROI
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Assessing Marketing ROI for making better and data-driven decisions
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Week 1: Introduction to Marketing Analytics and Customer Analysis
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Week 2: Market Segmentation
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Week 3: Preference measurement
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Week 4: Consumer Choice Models
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Week 5: Customer Lifetime Value
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Week 6: New Product Decisions
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Week 7: New Product Decisions
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Week 8: New Product Decisions
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Week 9: Pricing Analytics and Optimization
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Week 10: Pricing Analytics and Optimization
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Week 11: Advertising
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Week 12: Sales Promotions and Course Review
Undergraduate probability, statistics and calculus. Familiarity with R or a similar programming language.
8 - 10 hours per week for 12 weeks.