How do I start studying data science?

Data science is one of the most in-demand fields in the tech world, with organisations clamouring for professionals who can leverage data to make better business decisions and improve their bottom line.

Are you interested in becoming a data scientist, and wondering how to get started? You’re in the right place to find out, because in this article we’ll discuss the basic steps to begin your journey as a data scientist. 

What is data science?

Data science combines the power of mathematics, statistics, computer science, and business knowledge to help organisations across the spectrum make better decisions. 

If you’re looking to become a data scientist, it helps to be proficient in maths before you start. Being aware of the data science basics will also help you know what to expect.

Where to start with data science

It’s a good idea to start by familiarising yourself with the fundamental concepts of data science, such as the data types you can work with, what data visualisation is and why it’s so useful, what algorithms are, as well as the subfields that might interest you, such as big data machine learning, artificial intelligence, and so on. You might find that some aspects interest you more than others, which is likely to help you narrow the focus of your studies as you progress.

Build a strong foundation in maths and statistics 

If you don’t already have a strong foundation in maths, make maths your priority. Follow up with statistics. If necessary, do bridging classes in maths and stats before you sign up for a data science course. Or look for data science courses that kick off with maths and stats. 

Why? Data science involves using a wide range of mathematical and statistical techniques to extract knowledge and insights from data, from cleaning and organising it to performing analysis, creating predictive models, and finding practical solutions to pressing issues.

A good understanding of logic – which stands at the intersection of maths, computer science, and philosophy in data science – is also useful. It’s a powerful tool for understanding complex systems, and to support the architecture of trustworthy data systems.

Learn to use popular tools and technologies

Many tools and technologies are used in data science, including programming languages such as Python, the R language, and SQL and data analysis tools like Tableau and SAS.

Once you’ve got a good grasp of the fundamentals of data science, it’s time to start exploring the most popular tools and technologies that data scientists use. 

Python and R are currently the two most popular programming languages used in data science. You can achieve almost any data science task using Python and R. SQL programming and C++ also remain popular. 

At some point you are going to have to communicate your analysis to others in the workplace. Make sure you are proficient in Tableau, which you can use to create simple visualisations that help even the most committed Luddites in the organisation understand how the analysis led you to the conclusions it did.

Work on data science projects

Start by working on small data science projects, and gradually increase the complexity of the projects you tackle as you gain experience. Working on projects will sharpen your skills and provide hands-on experience that is invaluable when applying for data science jobs.

Choosing the right online course

Now that you know the basics, it’s time to start looking for the best online course to learn data science. There are a lot of data science courses available online, so assess how they will help you to achieve your personal objectives, requirements, and resources beyond simply delivering the knowledge you want. 

Obviously the content of your course matters. But there’s more to it than simply providing you with facts and figures. Here’s a checklist to help work out what you want from your course provider, and the tradeoffs you may be prepared to consider. Make one selection in each row:

Beyond Content: Course Delivery Checklist

Provisions		Non-negotiable “must”	Should have		Nice to have but can live without	Doesn’t matter
Lecture flexibility accommodates work and family time	⦿	⦿	⦿		⦿	⦿
Classes online at set times	⦿	⦿	⦿		⦿	⦿
Suitability of course scope	⦿	⦿	⦿		⦿	⦿
Lecturer available to handle queries	⦿	⦿	⦿		⦿	⦿
Provides manuals	⦿	⦿	⦿		⦿	⦿
Requires group  projects	⦿	⦿	⦿		⦿	⦿
Requires completion of real-life projects	⦿	⦿	⦿		⦿	⦿
Builds community of practice	⦿	⦿	⦿		⦿	⦿
Uses peer review and assessment	⦿	⦿	⦿		⦿	⦿
Assessment by facilitator	⦿	⦿	⦿		⦿	⦿
Assessment automated	⦿	⦿	⦿		⦿	⦿
Updates on changes in field after course is complete	⦿	⦿	⦿		⦿	⦿
Affordability	⦿	⦿	⦿		⦿	⦿
Continuous professional development points	⦿	⦿	⦿		⦿	⦿
Other

Digital Regenesys’ data science courses expose you to pandas, matplotlib, NumPy, seaborn, sci-kit learn, stats models, MySQL, Python, and other data science tools. Explore what’s on offer here.