Original article can be found here (source): Artificial Intelligence on Medium
Despite having a Bachelor of Commerce, I’ve finally landed a position as a Data Scientist at Datatron, a goal of mine for the past three years. For you to understand how I got to where I am now, I think it’s important that you know a little bit about my past.
My interest in data sparked from dabbling in financial data in my second year of undergraduate studies. With a bit of analysis and a lot of luck, my stock portfolio ten folded and I was captivated by the idea of extracting immense value out of raw data.
Around the same time, my exposure to my software engineer friends got me interested in learning how to program, specifically in Python. When I eventually heard about the concept of machine learning, I knew that that was something that I wanted to learn more about.
This leads to my first tip…
1. Commit to learning and completing several data science projects on your own
Let me break this down even further into two sub-points:
A) Learn about anything related to data science on a consistent basis
Because ‘Data Science’ is so vaguely defined, there is a wide range of skills that are required to be a well-rounded Data Scientist. Below are some of the most important skills that I worked on honing over the past few years and the resources that I leveraged to do so:
- Intro to Machine Learning (Kaggle): If you know nothing about Machine Learn, I believe that this is the best resource to get started. It starts from square zero and walks you through completing your first machine learning model.
- Intermediate Machine Learning (Kaggle): This is an extension of the course above and also teaches you more concepts in an intuitive manner.
- Machine Learning A-Z (Udemy): Once you get the idea of machine learning, this course covers (almost) all machine learning techniques. It covers the concepts behind each model and walks you through an example with code.
- Machine Learning — Stanford (Coursera): If you really want to strengthen your understanding of the concepts behind each model, this is the best course to do so, and is one of the most popular courses out there.
- Introduction to Python (Datacamp): The best way of learning Python is by completing side projects as opposed to a bunch of boot camps. That being said, if you know nothing about Python, this is a great resource to get started.
- Learn Pandas Tutorial (Kaggle): Pandas is a Python library used for data manipulation and analysis. It is extremely beneficial to know how to use this library, as it will make your life a lot easier when completing personal data science projects!
- Learn SQL (Codecademy): SQL is arguably just as important as Python and it is very easy to learn the basics! Once you complete this course, then you can find practice problems online to hone your skills.
B) Complete a couple of exceptional data science projects.
Once you have a foundation built, the best way to accelerate your learning is by completing some data science projects. The best way to do it is to go on Kaggle, pick a dataset, and creating a prediction model or some data visualizations. Remember that your first few projects aren’t going to be great! But what matters is how you progress over time.
Here are some data science projects that I completed in the past that you can use to get some inspiration!
While you continue to learn and practice your data science skills, there are other things that you can do to make yourself a more valuable data science candidate, and this leads to my next tip:
2. Look for jobs similar to Data Scientist positions
I knew I would be fighting an uphill battle, especially with no previous experience as a Data Scientist. However, finding jobs similar to data scientist positions will significantly increase your chances of becoming a data scientist. The reason for this is that related jobs will give you the opportunity to work with actual data in a business setting.
I first worked as a Business Intelligence Consultant at Bell Canada and this allowed me to work with real-life data for the first time. By the end of this internship, I learned how to use Excel more extensively, how to query large and complex databases using SQL, and how to leverage data to support business decisions.
I then worked as a Data and Operations Associate at Wealthsimple, and here, I had the opportunity to develop its first data manipulation and analysis process as well as create several dashboards.
Lastly, I moved on to working at HelloFresh as a Growth Marketing Analyst. This was the first job where I had the opportunity to develop machine learning models in a business setting, and I developed several models to optimize discounts.
My point is this: despite not having a single data science job, I was able to learn several data science-related skills with each job building off of the previous one.
You don’t need to be a Data Scientist to do ‘Data Science’ work
Here are some data science-related jobs that you can look for:
- Business Intelligence Analyst
- Data Analyst
- Product Analyst
- Growth Marketing Analyst / Marketing Analytics
- Quantitative Analyst
In addition to the two points above, there’s one more tip that significantly improved my reputability as a data scientist…
3. Get a Master’s degree in a quantitative field
Most Data Science job listings require a Master’s degree because it generally requires a high level of technical skill. If you find that you are not finding success with the two pieces of advice above, I recommend looking into a Master’s program in a quantitative field (computer science, statistics, math, analytics, etc…)
Personally, I chose to enroll in Georgia Tech’s Master of Science in Analytics program for a number of reasons:
- It doesn’t require a bachelor’s degree in a quantitative field.
- It has an online program in case you want to work and study at the same time.
- It costs only $10K USD for the whole program.
That being said, there are several options out there and I highly advise that you take the time to explore all of your options before you make a decision!