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Business Analytics

Offered By: Columbia University via edX

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Business Intelligence Courses Data Analysis Courses Statistical Modeling Courses Statistical Analysis Courses Marketing Analytics Courses

Course Description

Overview

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. If you are interested in completing the full MicroMasters program on edX, there is no time limit in which you must complete all the courses in the program. Currently, the courses are offered twice a year, in the spring and fall term.

For those who are interested in applying for an M.S. degree at Columbia University, you must complete this MicroMasters program within 2 years of the start date of the first course in order to include MicroMasters certificate in your application package. For example, if you aim to apply for 2021 Fall M.S. program, you have to earn the MicroMasters Program certificate by 2021 February admission deadline.


Syllabus

Courses under this program:
Course 1: Analytics in Python

Learn the fundamental of programming in Python and develop the ability to analyze data and make data-driven decisions.



Course 2: Data, Models and Decisions in Business Analytics

Learn fundamental tools and techniques for using data towards making business decisions in the face of uncertainty.



Course 3: Marketing Analytics

Develop quantitative models that leverage business data to forecast sales and support important marketing decisions.



Course 4: Demand and Supply Analytics

Learn how to use data to develop insights and predictive capabilities to make better business decisions.




Courses

  • 0 reviews

    12 weeks, 8-10 hours a week, 8-10 hours a week

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    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.

  • 4 reviews

    12 weeks, 8-10 hours a week, 8-10 hours a week

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    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.

  • 0 reviews

    12 weeks, 8-10 hours a week, 8-10 hours a week

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    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:

    • Introduce fundamental techniques towards a principled approach for data-driven decision-making.
    • Quantitative modeling of dynamic nature of decision problems using historical data, and
    • 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.


Taught by

Daniel Guetta, Hardeep Johar, Vineet Goyal, Kamel Jedidi, Asim Ansari and Costis Maglaras

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