Get Practical Understanding of Various Analytical Tools with this Popular edX Capsule

The Analytics Edge by edX is an online instructor-paced certification course provided by the Massachusetts Institute of Technology (MIT) that covers analytical tools and techniques. The lead instructor is Dimitris Bertsimas from the Department of Operations Research at MIT. Other instructors include Velivor Misic from the University of California, Los Angeles (UCLA) and Anderson John Silberholz from Michigan Ross. 

The teaching style is very engaging, breaking down complex Machine Learning (ML) concepts, analytical tools and techniques into simple and easy-to-understand chunks with lots of real-world practical examples. Overall, the lessons are explained in a simple and engaging fashion, sticking to the fundamentals without overcomplicating any lesson. It focuses on implementing various ML algorithms without going into too much detail of the underlying theory or mathematics. 

The course introduced a number of data analysis techniques that have already been implemented in R packages. Machine learning techniques covered include linear regression, logistic regression, decision trees (random forests, etc.), and clustering. There is a lot of variety in data sets and problems introduced in the course. They were very interesting, diverse, stimulating and taken from the real world. 

The visualization chapter is incredibly stimulating as well. One feature that stands this course out from other analytics Massive Open Online Courses (MOOCs) out there is its hands-on nature from day one.

It takes around 13 weeks to complete the course. The class consists of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After multiple lecture snippets, a quick question is asked to assess the understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. 

In general, it takes around 8 to 10 hours per week to watch video content, practice all the exercises and do the weekly assignments. That depends a lot on what kind of content is being taught and your familiarity with that week's content. The practice problems can be attempted multiple times, some of which are graded. Learners can instantly check the answers to know if they have answered correctly or not. 

For weekly assignments and end-term exams, there would be 1 to 2 attempts per question and the answers would be told after the exam deadline has passed. We can refer to the study notes and lecture presentations for all exams.

Each course has a discussion forum that brings learners together. This is very beneficial as one can ask help from their peers with questions about the lessons. In fact, those answering questions often learn more by helping others. Also, the forum offers the opportunity to hear about issues from learners around the globe. 

Each unit will have a homework assignment that involves working in R or LibreOffice with various data sets. In the middle of the course, there will be an analytics competition, and at the end of the class there will be a final exam, which will be similar to the homework assignments.

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My role is primarily to help startups solve their operations-related challenges with a focus on organization design, people engines, governance mechanisms, operations design, and cost transformations.

Understanding data analytics is crucial to making key business decisions in the digital economy space. Understanding different data analysis models, the underlying mathematics behind them and how to interpret the results is becoming a crucial skill set. 

With respect to that, this course lays the foundational groundwork for anyone wishing to learn more about analytics and various machine learning algorithms and approaches. 

This course helped me sharpen my domain understanding with more data background and develop machine learning approaches in solving business problems. Analytics is one of the hottest fields currently and the demand for data science professionals is increasing yearly. 

Various estimates indicate the supply of data science practitioners is nowhere close to the industry demand, making data science a lucrative field from a compensation point of view. Also, as the digital economy increases in emerging markets, the need for data science expertise is only going to increase.