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

About this course

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.

What you'll learn

• Become familiar with working with relational databases, using SQL based languages such as MySql, dealing with formatted data (XML, JSON, etc.)
• Use Python to work with and analyze data from databases as well as from the web
• Use Big Data processing frameworks like Hadoop and MapReduce

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

About this course

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.

What you'll learn

• Fundamental concepts from probability, statistics, stochastic modeling, and optimization to develop systematic frameworks for decision-making in a dynamic setting
• How to use historical data to learn the underlying model and pattern
• Optimization methods and software to solve decision problems under uncertainty in business applications

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

About this course

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:

• customer relationship management;
• market segmentation;
• value creation;
• communication;
• 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.

What you'll learn

Skills acquired after the course:

• Demand forecasting using customer-base models and statistical approaches
• Market segmentation methods and best practices for identifying potential customer segments and focused targeting
• Computation of Customer Lifetime Value for analyzing customer, brand loyalty and forecasting revenue in the short and long run
• Factors to consider while designing and introducing new products to the market
• Calculating Optimal Pricing for products and services to get the best ROI
• Assessing Marketing ROI for making better and data-driven decisions

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

About this course

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.

What you'll learn

• To identify, evaluate, and capture business analytic opportunities that create business value
• Build models to support and help make managerial and business decisions
• Basic analytical methods and their applications
• Analyze case studies on organizations that successfully deployed analytical techniques

This dataset includes a heterogeneous set of features about Facebook posts published by on a renowned cosmetic's brand Facebook page.
How well can you analyze this data to predict the impact of posts prior to publication?

Amazon's Information Security is finding many new ways to leverage customer access data to increase platform sales. But at the same time, it wishes to avoid compromising the personal information of any customers. Note: This data set represents an anonymized sample of access provisioned within the company.
Can you help them utilize personal information more effectively while reducing risk of breaches?

The current life insurance application process is antiquated, with customers having to provide extensive information and undergo medical examinations. The process takes an average of 30 days just to get a quotation.
Can you make buying life insurance easier? By developing a robust predictive model to classify risk, you can reduce the time and effort required by customers to get an accurate insurance quote.

Earn the MicroMasters^® Credential by completing and successfully earning a verified certificate in all four business analytics courses.

MicroMasters^® programs are a series of graduate level courses offered on the edX^® platform from top universities to advance your career. They provide deep learning in a specific career field and are recognized by employers for their real job relevancy. Students may apply to the university offering credit for the MicroMasters^® certificate and, if accepted, can pursue an accelerated and less expensive Master's Degree.

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