Congratulations! The fact that you’re here means you’re probably trying to figure out what career path is right for you. Maybe you are about to graduate or maybe you’re contemplating a career change. Either way, this is an exciting journey, so hang on tight!

In recent years, jobs in data science have become increasingly popular. We have more data than we know what to do with and problems we want to solve. Whether in business, healthcare, space exploration, or another industry, there are endless opportunities to harness the power of data! Successful data scientists use data to tell a story that captivates their audience.

“If data is ‘the new oil’, then the data scientist functions much like an oil refinery, converting data into insights that can both save money and generate capital.” — Eva Short

What is a data scientist?

A data scientist is someone who uses computer programming, statistics, and mathematics to derive meaningful insights from large quantities of data. For example, a data scientist might conduct a cluster analysis of customer characteristics to inform a marketing campaign or build a machine learning model to diagnose cancer.

Some cool things that data scientists can do:

• Analyse patterns of movie-watching to understand viewer preferences and inform the creation of new content
• Identify characteristics and unique shopping behaviors of different customer bases to guide targeted marketing campaigns
• Utilize time series models to predict future demand and plan production levels accordingly
• Create machine learning tools to detect disease in radiologic images
• Build a recommendation engine that suggests items for customers to buy (just like the “Recommended” items on Amazon)
• Develop an algorithm that filters incoming email to delete spam
• Apply machine learning to artificial intelligence projects like creating self-driving cars that can recognize traffic lights, other cars on the road, and pedestrians

How much are data scientists paid?

Data science is a lucrative career path. The May 2018 edition of the BurtchWorks Data Science Salary Survey reported the following salary figures:

Here are some questions to ask yourself before you decide to become a data scientist.

• Do you love number crunching and solving puzzles?
• Do you enjoy tackling unstructured problems?
• Do you enjoy research and working with data?
• Do you enjoy building and presenting evidence-based stories?
• Do you thrive on intellectual challenges?

If you answered yes to these questions, it means being a data scientist could be a good fit for you!

How to get started on your path to becoming a data scientist:

There are multiple ways to learn data science, so you can pick the option that works best for you. Here are some examples of learning paths you can take, complete with a discussion of the pros and cons of each approach.

• Earn an undergraduate and/or advanced degree in computer science, statistics, or mathematics
  • Pros: Provides a solid education that will help you get interviews and job offers; also provides access to a network of alumni from your school
  • Cons: Can be expensive and time-consuming
• Attend a data science bootcamp
  • Pros: Can be less expensive that getting a degree; less time-consuming than a degree, you will graduate with enough skills to be employed as an entry level data analyst/scientist; you will also make new friends and have access to a network of alumni from your bootcamp
  • Cons: These programs are intensive, meaning they go from 9am to 6pm every day and you’ll be working very hard till late night to learn as well as apply what you learned to real projects; you will get more out of the program if you come into it with a technical background
• Participate in MOOCs
  • Pros: Free and abundantly available
  • Cons: Time-consuming and requires discipline to sit down in front of your computer and watch online videos or go through a textbook on your own

What should I learn? Here’s a list of skills that you should have in order to break into the data science world.

• Python and/or R (Python: pandas, numpy, scikit-learn, R: dplyr)
• SQL
• Differential Calculus
• Integral Calculus
• Multivariable Calculus
• Linear Algebra
• Probability
• Statistics

What’s next?

Once you have graduated from a program – be it an MOOC, a graduate degree, or a bootcamp data science program – it will be time to start looking for a job. The best way to prepare for data science interviews is to work on personal projects. There are tons of free datasets available online, so pick a topic that is interesting to you, find a dataset, and start applying your skills. This will help you practice your coding ability while strengthening your resume/portfolio.

Conclusion

As you can see, there are many ways to start your career in data science. But making the career choice that is right for you is important. The bootcamp really helped me to learn a lot from statistics to computer programming in a short period of three to four months. What matters at the end is that you create a plan that best fits YOU and follow it through until the end. Good luck!

This article is contributed by Rifat Yuce Dincer, a graduate of the 16th cohort at NYC Data Science Academy. He spent the first 9 years of his career in business development partnering with c-suite executives solving their business problems with technology, and now he’s a certified data scientist who’s an expert on python, machine learning and visualization.

NYC Data Science Academy offers in-person, live online, and remote self-paced bootcamps. Apply to the next bootcamp early to reserve your seat. The next in-person bootcamp starts on September 23, 2019. Visit NYC Data Science Academy’s blog to review student projects and get updates.

This post was sponsored by NYC Data Science Academy. To learn more about NYC Data Science Academy, visit nycdatascience.com or check out their reviews on SwitchUp.