Learning Timeline

The first question that I am always asked is how much time does it take. I’ll answer it in two ways.

1. Assuming you’re a working professional– Anything between 10–12 hrs a week stretched for continuous 18–20 weeks should be enough. Remember consistency is the key, remaining consistent can be a big challenge and this where most learners break.
2. Assuming you’re a full-time learner –In this case, you don’t have the burden of working 6–8hrs a day and you can devote more time to learning. If you can manage 20+ hrs week 10–12 weeks you should be ready and if you are choosing a training program select one that fits this timeline.

Data Science skillset

This is where there lies a big gap in information for the learners. I’ve seen 90% of data science course generally focuses on only three things i.e. Python, statistics, algorithms(including ML/DL) while this is certainly the essentials to of being a data scientists but we need more. Once you are hired your role actually fluctuates. For the outsiders, the tag “Data Scientists” misleads people and sometimes unfairly assumes that it’s only about predictive models. You will find me writing SQL queries, working in excel, building dashboards, and at times even documenting my work in PowerPoint slides.

Here’s the list of skills you should acquire before applying for a Data Science job:

• Python
• Stats & ML
• One Data Visualization tool – Power BI/Tableau
• SQL
• Exposure to at least one cloud platforms from AWS/Azure/GCP
• Python web frameworks like Flask/Django (good to have)

One thing I’ve learned working in the Industry is Data Science is just a process where you use data to add value to the business. Sticking just to algorithms is sometimes is not enough. Additionally, the analysis you do will not be sitting in your local machine, it has to be deployed somewhere so explore #5 and #6 and learn how to deploy/productionize your ML models.

Why have I not mentioned R? R is cool but Python is now lightyears ahead of R. Also, out of every 10 jobs, you’ll find 8 of them would require Python. Python is a scripting language that goes beyond just Data Science. I cannot say the same for R. I recommend learning just one out of R and Python and hence the latter would be a wise pick.

Application

The best way to learn something is implementation. Don’t just limit your learning to exercise and assignments in online/offline classes. Make sure you are building a full-fledged project which explains to the recruiter your ability to work as a data scientist. I recommend building an end-to-end project, I will recommend one in the resource section. Avoid drafting projects like titanic datasets, house price prediction, etc.

When you work on a project always keep these three points in your mind:

1. What is the objective of the project?
   2. What was your role in the project?
   3. What impact will your project make to the business/clients/anybody that you are working for?

As a recruiter I am always asking what challenges has the candidate faced and how did he overcome them.

Sample Project: https://www.youtube.com/watch?v=MpF9HENQjDo&list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t

Tip: If you are watching the tutorial just try emulating the steps with a different dataset and also make sure to give credit to the actual creator. Avoid Plagiarism.

How to combat the inexperience

This is by far the biggest challenge that people face. The harsh reality is there are very limited companies appoints fresher. The easiest route in today’s day is to get an internship at any firm and work your way to a full-time job. Managers will prefer in-house interns over external inexperienced candidates. That’s how I see people getting hired in my firm and others.

But what to do if you’re already working in a non-data science or non-IT job with 5+ years of experience or even less?

All those years of experience will be irrelevant when you go for a Data Science job. So before jumping ships please ask yourself the following two questions.

1. Why do you want to be a data scientist?
   2. What is your expectation in terms of pay if you land a full-time job?

Swapping from one domain to other has many disadvantages and you should have good reasons for that. I was teaching a candidate who had 5 years of experience as a java developer and was already earning something around 10 LPA which is very good in terms of Indian standards. When you learn a new skill you are starting fresh. So don’t expect high numbers.

I’m sure you can relate it to other professions. If you are a footballer and you want to play tennis you should not expect right away to play in the same field as the grand slam winners or earn like them. Data Science is no different.

That being said you can still make it to Data Science. I hear success stories every day of people transitioning from non-IT fields to Analytics and Machine Learning. I am one of them. I was a Civil Engineer with no coding experience and here I am working as a Data Scientist for one of the biggest consulting firms in the globe.

So the main question If I’m a civil engineer with 5+ years of work ex. how do I utilize my experience?

Step 1 — Fabricate your experience. Split it into 3+2. Sell a story like this. “I worked as full time … for the past three years and then I was heavily involved in analytics of raw materials/reinforcements/topography etc”

Step 2 — Back it up with working with some relevant projects. Here are few examples.
1. If you are working as transport/roadways designing a project where you’re trying to predict the vehicle density based on past data collected would be a nice one.
2. If you are involved in structural designing then predicting the compressive strength of the concrete based on features like cement, slag, ash, water, fine aggregates, etc.

I can go on with more examples but I hope you get the point. And for people belonging to any other industry, the aim is to show a project relevant to your current line of profession which can be used to make your current process more effective by increasing revenue or limiting losses, or by saving time.

Step 3 — Reach out to someone who is actively working in the field maybe someone like me and ask them to interview you. Ask them to grill with questions regarding the projects mentioned in step 2.

Tip — Data cleaning is an integral aspect of Data Science. Talk to any data scientist they will tell you that 70–80% of their time goes into structuring their data. You can always express your love and desire to clean messy data.
This is something that does not need any experience and works well for beginners and experts and will certainly be appreciated by your interviewer.

Building your own brand

We’re in 2021 and every individual or business needs a professional presence. You should show your presence as well.

LinkedIn is a great way to help launch your career. It’s cheap and effective — if you have a strategy. Over the course of the last 3 years, I have grown my own brand a lot on LinkedIn and people reach out to me to check if I’m looking for a change or interested in taking up their job or be a part of their ventures.

This week my LinkedIn profile exploded and I also received job invites from people from my Network.

I’m at a place where I’m confident enough that if I make myself available to my network I will be getting a call from someone in my network. This is the modern way of job hunting.

The aim is to make people recognize you by your work. Engage with the community. Send connection requests to Data Scientists who are actively working in the field. Be polite and don’t randomly approach them to help with your questions or ask for a referral.

Note: Building a good LinkedIn profile takes time it does not happen overnight so consistency is the key.

I also recommend all my mentees write blogs as they learn. Writing is the best way to validate your learning. And it also a trick to attract your recruiter to your point of strength. Wonder how? Let’s say you have written 5 blogs on Ensemble learning. Chances are you would be asked questions on the same. Projects and blogs are the 1st level of cross-questioning you can expect from a recruiter, they want to first test if you are confident about your own work and you should be able to answer them assuming you must be knowing about the topics you blog.

Lastly, do keep a track of all your exercises and projects in a GitHub repository. It acts like proof that you’ve actually worked on the project that you are talking about. Please note learning will be gradual and hence uploads in GitHub should also be timed.

LinkedIn, Blogpost, and GitHub should be the 1st three things that go in your resume.

Roadmap I recommend

Get yourself enrolled in a course. If possible offline. They say if you’re climbing a mountain, it’s better to do it with someone who has already done it. Keep in mind the following things before choosing a course.

1. Trainers Profile — Always learn from some who has Industry Experience.
   2. Course duration — Maximum 6 months.
   3. Cost — Make sure you are not making a hole in the pocket.
   4. Syllabus — Get the contents reviewed with someone who’s working in the field.

I learned data science while working full time. I had opted for a course that at the time charged me 2.25 lacs INR (3025 USD). Back in the day’s resources and tutorials were very limited, so I don’t regret the decision. But, since I had invested big, I was determined to work hard and make something out of it. So I was able to remain consistent.

It is too much money for someone who is learning data science. I would recommend not investing anything more than 30k-35k INR (500–550 USD) in any course.

Hope reading this blog was worth your time.

Good Luck on your journey to Data Scientist!