I guess it’s safe to assume you’re here because you are interested in starting a career in data science? Earlier I wrote a post on why everyone should be interested in data science.
Starting a career in data science can be one scary experience. I should know – getting my head into the statistics required for my PhD research was one crazy nightmare. But the experience taught me something important – your motivation to take up your career of choice depends a lot on your source of learning. There are so many inaccessible statistics lessons out there which will scare the average man away!
Another key issue people experience when starting out is what I’d call ‘technical confusion.’ – ‘Which technique should I use?’, ‘Should I start with Python or R’, ‘do I need to learn how to code?’, ‘Do I need a degree in statistics?’ These questions that can bog you down early.
I wrote this post to help those people who are interested in a career in data science but are not sure how to start out their journey. My aim is to enlighten as well as further motivate you in making your career choices.
So here are some key tips to get you started in the right direction!
Tip #1: Clarify your reasons for going into data science
This can be taken for granted but you need to know exactly why you are going into data science. According to inc.com, the top reason why people switch jobs is because they are bored and need a new challenge. I would say this is the same reason why people switch careers as well.
Do you view this career option as a life-time career commitment you’re willing to take?
Are you only attracted to the quality of opportunities and income the sector presents or you just love the world of data?
A huge paycheck doesn’t equal job satisfaction. People do much better at something they genuinely love and if you don’t really enjoy data science, this will likely reflect in your salary.
No answer to any of these questions is wrong but your responses should guide your actions. You may realise that after all you might only need a passing introduction to data science and not a career commitment.
Tip #2: Start your path based on an ideal role
There are so many roles within data science to consider. If you do a basic search of the term ‘data science’ in a job engine like Indeed, you’ll come up with over 11000 results with about 500 unique roles. Some examples of such roles include ‘data scientist’, ‘data visualisation expert’, ‘BI developer’, and so on.
Carry out some research and find out what each of these roles entail and the core competencies that can be transferred to similar roles. Talk to people already working in related roles. If you’re still in school, talk with a career adviser. I rely a lot on online research personally. For example, if you are interested in becoming a business intelligence analyst, one key requirement is knowledge of data visualisation tools such as PowerBI (and a Microsoft certification in the same) as well as proficiency in MS Excel and SQL. Some other roles have essential requirements such as a degree in a numerate course and certain niche skills.
What this does is to streamline your learning and development. There are many different technologies in data science but employers usually require only a few of these technologies. Take a look at the screenshot of a real job post below:
There are a few key things to take away from here:
- The main technology required here is R.
- A knowledge of statistics is a must evidently.
- Have an aptitude for data science since you need to learn several new skill sets in data analytics
This particular role offers opportunities for you to learn and grow in data science. So even if you’re fresh to data science, you can be considered for such a high paying position. Obviously this is likely to be a competitive role and a background in data science would be a huge leverage.
Tip #3: Plan and start a learning program
Create a learning program based on the core competencies required in your ideal role. As mentioned previously, selecting the right sources of knowledge is important because this will determine your pace, understanding and motivation when it comes to the course. After a few weeks of grappling unsuccessfully with statistics, I came across the statistics book series by Andy Fields. It really simplified things for me, and with a healthy dose of humour to boot!
If you prefer online audio/video courses, eDX (free) and Udemy (paid) are my favourite choices. Both offer excellent course options but careful research is needed to find out which specific instructors suit you best. I found out that course reviews are a good way to tell which instructors to gravitate to. Both offer certificates of completion (the former at a price) which are tangible proof of knowledge.
Sometimes the best option for you might be to join a graduate training scheme that enables you to learn while working alongside professionals in the industry towards a particular role . In this way, your training is lean and tailored efficiently to industry standards.
Tip #5: Practice Practice Practice!
Take assignments, quizzes and every opportunity to practice your knowledge seriously during your learning. When going through a course, I repeat every procedure the instructor teaches. This helps solidify your learning, boosts your motivation and also helps you identify gaps in your learning. When things don’t go for you as have been demonstrated, repeat them or raise a question for clarification to the instructor.
Tip #5: Join a professional ‘techie’ community
I can begin to explain how invaluable a discussion platform like stackExchange has been to my growth as a data scientist. Peer groups comprise physical meeting of people who can help you stay motivated, and help tackle practical issues related to your field. Such communities are usually the main source of up-to-date industry information. Joining such a community is one of the top means through which you can grow fast in your field.