7 Things You Must Know If You’re Trying to Enter Data Science
Tags: Jobs, Machine Learning, Interview, Data Science, Python

I found this really funny. lol. Image source: procured via Pinterest

No way. No freaking way to enter data science any time soon…That is exactly what I thought a year back.

A little bit about my data science story: I am a complete beginner in the Data Science field and I was desperately looking for a switch from digital marketing to data science exactly 6 months back. I assume you may want to ask..why desperately? Well, Because I became over confident in my job hunting abilities and resigned my ex-job without a backup. I started panicking during the last few days of my notice period. All the courses and tutorials available online and just the vast number of topics I had to cover to get started in data science was overwhelming for me. They say time flies and boy do I agree! It has already been half a year into my first data science job. I cannot wait to share all the learnings and experiences with you. If you are currently in the same shoes as I was, go on and keep reading for insights and motivation.

1. Practice more than you read:

I remember going through every single data science boot camp course available in Udemy and buying a couple of top rated courses that covered Python, SQL, Tableau and Machine Learning topics (Pro tip: Don’t go for generic “Data Science boot camps”. These courses don’t cover important topics in depth. Instead, try tool-specific boot camps like python boot camp, SQL boot camp, Deep Learning boot camp etc.). The courses were all detailed and honestly very helpful. But even after all the 50+ hours of lectures and many assignments, I was still someone with no data science experience. Even the basic analysis tasks in the first month of my job were relatively difficult for me. I was absolutely struggling to meet deadlines.

Image source: procured via Pinterest

Looking back, I feel that I focused more on learning and less on practicing. I listened to all the lectures which covered new topics in every lecture, did some teeny tiny assignments and thought I am doing it all the right way. However, I think of it all very differently now. Learning should be through practicing and implementing new ideas. That is when you make mistakes, observe new things, research on how to code the solution in a better way and you know..really learn. This certainly happened after starting my latest job as I had to work on new ideas every day and implement them. Trust me, that is when I picked up actual skills. If you are in the online course phase, spare some time to build projects and implement the topics you learned.

2. Coding skills:

Image source: procured via https://changhsinlee.com/

Most people who try to enter this field have a slight misconception that data science involves relatively less coding than software engineering. There is a little bit of truth to it. Because if you take Python which is the widely used language in data science, there are built-in libraries for almost all types of algorithms and operations. Though these libraries are very helpful, there is only so much they can do. I for one thought that data science is all about data analysis, plots, model fitting, prediction and accuracy metrics. These things are of course a part of it but software engineering is another huge part too. For example, when you want to build a production level product recommendation engine pipeline, you will have to work on many things like SQL scripts, data sync, training, tuning, prediction, evaluation frameworks, unit testing, logging, dashboards, admin panel, model deployment, version control and so much more. All of this combined involves a hell lot of critical thinking and coding. This is the kind of stuff you will work in the long run or maybe in your first few months! I am not saying that you need to know everything about coding everything but some level of proficiency in coding will be needed and also useful for you.

3. No pressure to learn every single data science tool:

There are way too many data science tools in the market and it can be quite confusing to find where to start. The best option is to learn one data science friendly coding language, one database tool and one visualization tool. This is a good way to begin with and is like the basic requirement for many entry level roles. When you are just laying the foundation, don’t pressure yourself to learn too many tools. Instead, take things slowly. Understand the basics and explore topics in depth in whatever tool you learn. You will eventually learn many tools when you are in the job due to project requirements or just while working on your passion projects.

Image source: procured via Udemy

I started with Python, SQL and Tableau when I was searching for a job. Nothing more. Now I know to work on a couple of other tools like Spark, Hbase, Kibana, Dash, Elasticsearch and Logstash. I am sure I will have to learn new tools in the coming days. The point is, learn a tool with utmost clarity of how it will be useful for your requirement.

4. You are ready to take interviews:

Tell that to yourself whenever you feel like skipping an interview call or meeting because your brain is telling you that you are not going to make it. I cannot remember the number of times I learned something new while attending an interview. It is either about the data science industry or new products or just a concept. I am not suggesting you to attend interviews randomly to learn stuff. It would be an obvious waste of time for the poor interviewer. Data science is a vague term and so are the job requirements for every data science role. You might never feel ready if you want to tick every single job requirement before attending an interview.

Image source: procured via Giphy

The preparation phase can be a long one too. It depends on your learning speed and prior knowledge. It is very easy to get stuck in that phase because there are too many topics to cover. Set goals during interview preparation and as you achieve those goals, start looking for interview opportunities. Every time you fail an interview, you will find the need to improve on a particular area or learn a new market requirement. And that my friend will help you in the next interviews.

5. Ideal companies to apply for data science roles:

Usually, people are flexible about roles and companies when applying for interviews as beginners. But if you are wondering what is the type of company in which you should apply for a data science role, it is completely subjective. Let us talk about product-based and service-based companies from a data science perspective. Service companies usually work on one-time data analysis or prototype whereas product companies involve rigorous software development and data analysis is just a part of it. Python, R. Powerpoint and Excel will do the job for you most of the days in service companies whereas product companies will want you to work on whatever tool is required to do the job. Basically, product companies will involve a lot of software engineering in addition to data analysis.

They work on projects that will help them to improve their products by incorporating data science in them or they make new data based products like product recommendation engine, AI-based chatbots etc. or they just use analytics to make better decisions in the organization. Service companies work on analytics projects purely based on client requirements. So like I said it is up to your interests. Choose wisely!

6. Data Science can be frustrating:

Data-based problems are very interesting to work on but some can be equally frustrating too. One of the difficult aspects of your work will be just to patiently wait for good results. Often you might not know whether you are going in the right direction. There are too many unknowns and a lot of things in your project will require plain trial and error to arrive at an optimal solution. Like they say it is all fun and games till you reach the hyper-parameter tuning part of your model :)

Most of us do a Proof of Concept before implementing any solution. But sometimes even POCs fail to give insights about certain hiccups you might face during the actual task. For example, once at work, we spent an entire month researching and implementing a solution for our pipeline. It eventually didn’t work out. We had to start all over again and this caused a huge progress lag in the supposedly well-performing project. The key take away from a couple of incidents like this is that always set clear goals, evaluate your POC thoroughly and when stuck at a point for too long, just remember to try fast, fail fast, evaluate fast and try again fast. Being fast is super important for good progress.

7. Your storytelling skills will matter a lot:

You will most likely be dealing with customers from non-technical backgrounds. Your organization leaders may not be data scientists. Your own teammates might be from diverse backgrounds (pure mathematicians, some API users etc.). These are the people who will recognize your work and will add value to your work.

It is so important that you communicate your thoughts, ideas, analyses and results in an interactive and understandable way to your audience. I clearly remember struggling in my first team meeting with the CEO where we had to explain the progress in projects, discuss use cases and future AI goals. That is when it hit me that sticking to numbers and just analytical skills are not enough. A good story explaining the analysis can interest your manager. A story explaining how a particular data science solution can solve the pain point of a problem can interest your customer. Different stories have different impacts on different people. Frame your story carefully with data science elements like visualizations, dashboards, reports etc. and put your everything in it while delivering it.

Final Thoughts:

Data Science is no rocket science. If I can do it, then you can do it too! There is no good time as now to enter this fast-growing field. That being said, it definitely gets a little bit tough to keep up with all the new things happening in this field and the competition. But, what matters is that we learn, implement, make mistakes and grow consistently. Happy analyzing:)