- 24th July, 2018
The self taught data scientist
By Eyram Adjaku
Data science even though old, is still regarded as a relatively new field. It combines so many skills that it is almost impossible to have learned all you need to know from college or other professional courses. To become a competent data scientist, you will invariably have to learn in your own time, picking up these skills gradually. It’s no surprise therefore that a large percentage of data scientists are self taught.
When embarking anew on the data science journey, you’ll first have to identify the skills you will need. This will depend on how deep you want to venture in the field. Are you looking to become a full blown data scientist, acquire a new skill set for your work or just looking to appreciate data more? The following tips will help you.
- Find profiles of people who are are in the field. A quick search on LinkedIn for Machine Learning Engineer, Data Engineer or Data Analyst will reveal a lot. Collate the skills these people have listed on their profiles and see which ones are best suited to what you want to do.
- Look through a couple of Data science related jobs and see what companies and employers require. This will ensure you build yourself to be competitive in the job market.
- Finally, make a list of all the necessary skills and map these against the gaps in your own skills. Rank them by the most popular and start working from the top.
Next you want to find the resources to fill the skills gaps you’ve identified.
There are countless resources available. Identify the types of resources (videos, audios, reading text) that enable you to learn best and stick to those. You will find some of the best beginner and intermediate courses here;
Other resources such as blogs and tutorials can be found here:
Last of all and perhaps the most important is practice. Data science is an exceedingly hands-on field. It doesn’t matter how much you’ve read or watched others do it. You will not know how to work through a typical problem unless you’ve practiced doing it yourself. There’s no way around practice, practice and more practice.
Not all of these may work for you. But I hope this gives you a place to start as you think about defining your own path into data science.