Getting on the Right Path for a Data Science / Machine Learning Career: The Courses and Sources to Get You There

March 25, 2020

Last year, I wrote a blog highlighting and suggesting a path to take for a career as a machine learning engineer. Some things have changed since then but many still remain the same. Data scientists, machine learning engineers or other AI-related careers still dominate the list of high paying jobs in demand. Unfortunately, what also remains is that the number of companies taking the AI plunge and integrating it into their processes is increasing while the number of qualified experts continues to be limited.

I couldn’t imagine a better time to become a data scientist. Data is the oil of the fourth industrial revolution. Everything from education to science is data driven.

Clients and colleagues continue to ask for guidance on how to start a career in machine learning or what skills are needed to augment and train existing personnel while AI projects are underway. Here is my updated review based on my experience growing our outstanding team of data scientists part of Wovenware’s AI Consultancy group.

The Required Course of Studies

So how do you get the advanced skills? There are many specializations in the AI and machine learning fields, and along with them come specific prerequisite studies, such as:

  • Analysis, Reading and Writing.  Data scientists must be story tellers. They must be able to insightfully read academic and industry papers.  They also must be good writers, understanding how to write for academia but most importantly, to communicate results effectively. This knowledge can be difficult to impart on someone, since it often comes through experience. This guide, however, can be a good start for understanding how to read academic papers.
  • Math and Statistics.  This includes courses, such as numerical analysis & forecasting, linear algebra, multivariate calculus, probability, regressions, and central limit theorem. Good resources include:
  • Programming.  The preferred programming language for machine learning and data science is Python, and some of the most popular libraries include, Pandas, Numpy, Matplotlib and Scikitlearn.  R continues to be the preferred language for statistics and exploratory data analysis, and some of the popular Comprehensive R Archive Network (CRAN) packages, are Caret, RandomForest and e1071.  Meanwhile, SQL is still a very relevant language, as relational databases are a big part of the enterprise. Data scientists must be fluent in these languages, while continuing to stay up-to-speed on new frameworks that continue to evolve.

Hitting the Books for Machine Learning Specialization

In order to gain expertise in specific segments of machine learning, it’s important to take specialized courses. A  popular and growing specialization right now is Deep Learning Specialization by Andrew Ng, one of the most influential and reputable experts in the field.  The course is offered in Coursera a learning platform that he co-founded.

A more general course in machine learning is Stanford’s ML class also by Dr. Ng. This one is more general but very insightful.

There are many others in Coursera. Another one is Udemy’s Intro. To Machine Learning lectured by no other than Sebastian Thrun and Katie Malone. This is a fantastic course in Udacity that offers nano degrees hosted by experts in academia and the private sector as well.

Machine learning offers many specialized areas and it’s important to select the field that best fits with your skills and interests. For instance, if you are interested in creating models for image processing, natural language processing and speech recognition, deep learning is probably the route to take. On the other hand, if you are looking to make predictive models to identify churn and customer tendencies, expertise in regression toolkits could be best for you.

I look forward to 2020, since it provides opportunities, as well as challenges related to deploying AI into production. These challenges will raise the need for new controls that enforce security and ethical policies, and this will require that even new skillsets be added to data scientists’ already bulging toolbelt. Stay tuned for an exciting year.

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