Guide to starting a career in datascience

Get familiar with the basics.

There are a lot of tools used in data science. However there are some fundamentals needed. You would need to get familiar with statistics, calculus, linear algebra, an object oriented programming language of choice (Python, R or Java) and Structured Query Language.

Apply the basics.

It is one thing to read/know about something and another to put it n practice. The next step would be to learn and actively apply the tools you've gotten familiar with, learn their use cases and methods of application. This would be in the form of data analysis, data visualization, and/or data cleaning.

Learn Intermediate datascience concepts.

You should familiarize yourself with concepts such as machine learning, processing leading to machine learning and the do's and don'ts. Learn how to preprocess and create machine learning algorithms/models, improve created models by hyperparameter tuning and/or selecting important or relevant features for the models.

Participate in competitions or learning forums.

Sites such as Kaggle, Zindi and others hold various competitions which are a good platform to polish your acquired skills and also identify areas of improvement.

Learn advanced datascience concepts

You would need to learn advanced machine learning techniques. These include, deep learning techniques, computer vision, natural language processing(NLP), keras and cloud computing.

Keep Learning!

The data field is always growing, you should join data communities and platforms not only for networking (which is important) but for keeping in touch with the current trends in order to keep your skills on toes. These communities can be found on Kaggle, Zindi, LinkedIn, and Twitter.

Godspeed on your data science journey!!