Gain an interdisciplinary understanding of the essential fundamentals of analytics, including analysis methods, analytical tools, such as R, Python and SQL, and business applications.
Using common analytics software and tools, statistical and machine learning methods, and data-intensive computing and visualization techniques, learners will gain the experience necessary to integrate all of these parts for maximum impact.
Project experience is also included as part of the MicroMasters program. Through these projects, learners will hone their skills with data collection, storage, analysis, and visualization tools, as well as gain instincts for how and when each tool should be used.
These projects provide hands-on experience with real-world business applications of analytics and a deeper understanding of how to apply analytics skills to make the biggest difference.
Courses under this program: Course 1: Introduction to Analytics Modeling
Learn essential analytics models and methods and how to appropriately apply them, using tools such as R, to retrieve desired insights.
Course 2: Computing for Data Analysis
A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning.
Course 3: Data Analytics for Business
This course prepares students to understand business analytics and become leaders in these areas in business organizations.
Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.
In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.
You will learn how to use statistical models and machine learning as well as models for:
Today, businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses.
This course prepares students to understand business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to business problems. The use of examples places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques.
The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.
The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.
In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.
The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.
Frederic Bien, Joel Sokol, Richard (Rich) Vuduc and Charles Turnitsa