How do I become a Data Scientist and how do I excel in Data Science?

September 14, 2018

This is by far the most common I am asked, but also the trickiest question to answer.

 

The answer is difficult, depends on the individual and depends on your passion, skills and interests.

 

My book “The Data Scientist’s Journey: The Guide for Aspiring Data Scientists” goes into much more detail, but here is a summary if you didn’t feel like reading the 220+ pages in the book.

 

 

 

Step 1: The Motivation for Data Science

 

The first question you have to ask is:

 

“Why do I even want to become a Data Scientist?”

This is important to establish your motivation, work out your passion and what you are likely to excel at. You’ll be working a long time, so you might as well enjoy what you do.

 

This motivation will be your North Star guiding you onwards to your Data Science destiny. Write it down, you’ll need to come back to this when things get tough. The process will help you work out what kind of job you would like.

 

Next set up your own personal plan of attack and put aside the time you need to learn data science. Work backwards from the skills you need to get a job.

 

 

Step 2: How to Skill Up for Data Science

 

Ok, so we have established your motivation. Now you are pumped and motivated, what do you need to learn and how are you going to learn it?

The internet is your best friend, you can use Google to find resources for everything you need. There are a couple caveats though, don’t learn “all the things”, be specific and learn the skills you need to get your chosen job or enough to complete your own personal learning projects.

 

There is a real problem aspiring data scientists have with doing too many tutorials for too long. Jump off that tutorial wheel and start building things!

 

The other issue we have is many learning resources focus on model development, however you really need to think of the entire data science pipeline. Make sure you are across everything from version control to SQL queries, to data manipulation in python or R, plotting, modelling, reproducible research and documentation.

 

 

Step 3: How to Get a Job as a Data Scientist

 

Now it’s time to put that melon of yours, bursting with data science knowledge to use. It’s time to land that first job!

 

This will be painful, but you will get through it!

For many data science positions the company will receive many hundreds or even thousands of resumes. Throwing your resume on a pile with the many other resumes will leave you with a small chance of success. You need to do something different, so pound that pavement, get out and meet people and just network the heck out of everyone else. Hopefully then the job offers will come to you and you can tap into the “untapped job market” where roles aren’t even advertised but are based on personal recommendation.

 

 

Step 4: How to Succeed in Your First Data Science Job

 

Awesome, we’re in! We have our first job, but now we have to kick some serious data butt!

Feel free to ask lots of questions, but make sure you ask them early and don’t ask the same question multiple times. In your first role you are there to learn the ropes, in particular to understand the data science life cycle, the Agile methodology for building software/ data products and how to work with other people such as developers, technical business analysts and managers.

 

You will see straight away how important communication skills (both written and verbal) are to executing data science projects.

 

 

Step 5: How to Become a Senior Data Scientist

 

Cool, we’ve been working as a Data Scientist for a while, but now it would be nice to get that promotion.

Data Science is about adding value to the business, If you would prefer to do research without the pressure of making money a research-based PhD would be the plan. However time is money and feedback and communication is critical in project execution, so it is important to understand how the various parts of the business fit together to deliver data products. You should also make sure you understand the entire tech stack in the business.

 

If you have opportunities to mentor new data scientists, or to lead projects you should definitely take that up! You should also further develop your written and presentation skills. As a technical expert who can manage, lead and communicate you really will be someone very special in your business!

 

 

Step 6: Giving Back to the Data Science Community

 

We are winning! It’s been a journey, but we made it. Now, how do we attain the enviable position where people seek us out for jobs? How do we future-proof our career and become someone well known in the field?

You should at this stage try to share your knowledge with others in this great data science community we have. Being able to help other people out in their journey is an incredible thing to do. However there will be unexpected benefits, people may no longer want a ‘data scientist’ to join their company they may actually want you specifically. That’s an enviable position! Being able to have the freedom to negotiate for the kind of role you want is very powerful. You will be able to meet other amazing data scientists, be invited to attend or speak at conferences and just generally have a heck of fun!

 

 

Step 7: Preparing for the Future of Data Science

 

The only constant is change, it sounds lame but it is true. We need to be continually upskilling as Data Scientists and we need to be aware of what’s coming on the horizon.

I see plenty of opportunities for data scientists in the next generation of AI startups, many startups in the next 10 years or so will just take a brick and mortar business, set up an equivalent online business and apply algorithms to optimize the heck out of the business. Data scientists will be needed!

 

I see plenty of opportunities in businesses building solutions to information asymmetries in business using blockchain technologies, also there are opportunities in the push towards smart cities. There will be plenty of new opportunities, but what is best for an established data scientist will be the freedom to choose to live and work where you are happiest or even to try your own thing as a solopreneur or with a group of your buddies.

 

 

 

Reviews

 

“As an aspiring data scientist, your path into the field does not have to be a lonely one. This book is a piece of its own and should be read by anybody who wants to learn more about Data Science. You will learn everything you need to know and be put onto the path of Nic Ryan's amazing data journey. Packed with insights and resources, I highly recommend this book to any data aspirants.”

 

- Randy Lao

Mentor @ Data Science Dream Job | Data Technician @ Facebook Reality Lab

 

 

“The Data Scientist’s Journey: The Guide for Aspiring Data Scientists could not have come into this world at a better time. With the growing excitement about the data science space, there is a large number of aspiring data scientists that can use this book as a guide. Nic Ryan shares the steps he took to get to his role, and provides insights into how to get your first job, how to thrive on the job, as well as how to prepare for the quickly changing future of data science. Nic provides all of this information in an easy-to-read format and includes an immense amount of relevant advice that can be implemented right away!”

 

- Kate Strachnyi

📊 Data-cated | ✍ Author | 📈 Tableau Certified | Project Mgmt | Runner

 

 

“The book is helping me organize my thoughts and set an attack plan to reach my goals. I feel very motivated! There is a lot of passion on the way Nic writes. It’s very clear that we are learning DS from someone who loves what does and shows us the benefit of ‘doing what you love’! It's like having a coffee with a mentor or an 1 to 1 coaching with a very experienced friend. It’s so personal while at the same time it can be read by anyone, as it delivers great insights about the DS journey that are good for you individually and the to the DS community as well.”

 

- Paulo Maciel

Customer Insights Manager | Statistician | Data Insights | Marketing Intelligence

 

 

 

 

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