For instance, in the synthetic example for E2ST model shown in Section 5.1,ˆβl can573 be estimated forβl since the edge, 2Star and triangle statistics are specified.

This online training provides a comprehensive list of analytical skills designed for students and researchers interested to learn applied statistics and data science to tackle common and complex real world research problems. This training covers end-to-end guide from basic statistics such as Chi-square test and multi-factorial ANOVA, to multivariate statistics such as Structural equation modeling and Multilevel modeling. Similarly, you will also learn powerful unsupervised machine learning techniques such as Apriori algorithm and tSNE, to more complex supervised machine learning such as Deep Learning and Transfer Learning. Whether you are a beginner or advanced researcher, we believe there is something for you! This workshop helps you better understand complex constructs by demystifying data science and statistical concepts and techniques for you.

Finally, you need to have practical experience. Nothing better than connecting with a community that has the same interests as you. You can also face competitions. Most of this answer comes from a previous answers 1 and 2 that I gave to this (Brazilian) site. I must have forgotten lots of great references... Sorry about that.

A few years ago, Drew Conway came up with and shared his now-ubiquitous Data Science Venn Diagram. It was helpful, and we all were enlightened. A number of variants followed, and I am here suggesting my own enhancements. Hopefully they will be enlightening as well. I am proposing two additions: the differentiating of statistical applications (multivariate vs. non-multivariate) and the addition of discipline essences (i.e., the main contribution or function of each skill set).

I've seen a dichotomy between "analytics" vs. "data science" (or "statistics) in several teams of web marketing, because people may feel analytics is simple, fast and work well while statistics is hard to learn, complicated and time-consuming. In the latest post, I argued about the dichotomy from a viewpoint of analytic accuracy and pointed out a pitfall of simple and fast analytics. Essentially, any kind of multivariate data may contain third-party effect: but I feel not a few people neglect it. To avoid such a pitfall, multivariate statistics is important.