My Plan To Become A Data Scientist In 1 Year

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Last Updated on January 31, 2022 by Jay

I want to share my plan to become a data scientist in 1 year.

My definition of “becoming a data scientist” is that I find a job in the data science field. Not really a job with “data scientist” title (because of the title misuse), but rather a role that actually uses the data science techniques on the job.

In this post, I’m going to share a little background about myself, why I want to become a data scientist, my detailed study & job-hunting plan, and the future plan of this blog.

By making my goals and plans public, I’m hoping to:

1. Inspire my readers and encourage you to take action and start whatever that you have been putting on hold for. “Just Do It”.
2. Provide a roadmap that others can use as a reference.
3. Hold myself accountable.

A little background about me if you are new here

Free free to skip this section if you are not interested, and scroll down to see my detailed plans below.

Personally…

The real reason that I learned Python was to satisfy my hobby & desire to analyze stocks and options. After learning the basics of Python, I realized that using it for work will be 1000x more efficient than Excel. That’s when I started to bring Python into my day job.

I use Python almost daily even outside of work. I got a few daily Python tasks that are run automatically on my PCs & RaspberryPi. At the time of writing (Jan 24, 2022), I’m developing a back-testing tool for stock options. I hope to publish both the tool and the tutorial (on how to make it) in the near future.

Overall, I just enjoy coding in Python. In 2020 I decided to start this blog to share my passion for Python, and also help other like-minded friends learn the programming language to make their jobs easier.

Professionally…

I’m an actuary working in the life insurance industry. Actuaries use data to analyze the past and try to predict the future (sounds familiar?). We manage the risks of insurance policies to make sure insurance companies stay solvent and have enough money to pay for your claims 🙂 Here’s a link to Wiki if you want to know more.

Most actuaries in the insurance industry rely heavily on MS Excel, probably 96.69% of our job is done inside Excel.

That said, my day job requires that I go above and beyond the capabilities of MS Excel, so our friend Python is here to the rescue 🙂 However, I didn’t learn Python to do my current job – my job & team actually benefited from my Python knowledge.

Why Data Science?

This has been on my “wishlist” for a while now since maybe 2015-2016, I kept finding excuses for not starting – family, job duties, you name it.

One lesson I learned from my previous job was that staying inside the comfort zone isn’t a good thing for both personal and professional development. I got too comfortable with the job and stayed there for 4-5 years basically didn’t learn much.

My current job is again becoming comfortable, and no longer satisfying, so I figured that I should take action now and try to do something new.

Python also plays an important role in my decision. The programming language has become the #1 choice for data science in recent years. I love Python and work with lots of data every day, so why not give data science a try?

It’s Not About Money

I’m studying data science not because “data scientists make good money”.

Actuaries make okay money – I wouldn’t call it good compared to our software engineering friends, but the salary is enough for a comfortable lifestyle. To give some context, below is a salary reference from a reputable actuarial headhunter.

In fact, given my experience in the insurance industry, I’ll probably have to take a pay cut if I try to switch to data science as an “entry-level”.

That said, I think it’s worth trying. Doing something interesting and exciting is what I value most, money will come later once I become good at what I do.

My Plans

Study…

The study period is going to last 1 year long.

I will be uploading my detailed study plan here, stay tuned for that. Also, I’m still figuring things out, so I will be updating this section in the future when appropriate.

Here are the three main resources I’m using for the study:

Coursera Classes

Not going to pretend that I know what I’m doing… So I’m really standing on the shoulders of giants. I’m using a modified version of a study plan by Daniel Bourke, who created a very nice outline in Notions to help guide the journey. I like to have a detailed plan, like daily or weekly goals. My own study plan is still under development, and I will post it when it’s finalized.

Kaggle Competitions

I’m no stranger to Kaggle, actually, my account was registered a while back…

There are lots of interesting datasets and problems in Kaggle. The best thing is people publish their code & problem-solving techniques, so we can learn from the experts. Make sure you check out Kaggle.com if you are also interested in data science.

Books

I bought a few data science books in 2021 and only looked through the first few pages. My plan is to finish them this year. I will let you guys know if I find any book really good.

Job-hunting…

I intend to start looking for data science related jobs at the 6-7 months mark. Although I don’t expect to find anything meaningful at this stage, it’s important to start the process early.

By 6-7 months into my study journey, I should be at least somewhat familiar with the data science field, their jargon, techniques, etc. At this point, I hope to at least be able to solve basic data science problems like simple regression or classifications, etc.

Future Plan Of This Blog

I’m very excited about the future of this blog. Making a commitment to becoming a data scientist doesn’t mean that I’ll cut back my time from this blog. In fact, it’s the opposite.

There are still a ton of Python and Excel related things I can’t wait to share with you, in addition, I will also share new tips and tricks as I go on the learning journey. Data science mainly uses our favorite Python library – pandas, along with several other scientific computing modules. Therefore, I’m confident that I’ll pick up something new about pandas, which I’ll be sharing here as well.

I really should’ve taken action a few years ago, but we can’t travel back in time. It’s better late than never. I’d like to end this post with one of my favorite quotes:

Stay Hungry, Stay Foolish.