How to Get Started in a Career in Data Science: 5 Tips to Help You Succeed

This is the second article in our series from Data Science for Anyone that covers ways in which ordinary people can develop skills in data science to apply in their day job, advance in their career or excel in educational pursuits. Don’t miss the first featured article in our series which reviews the learning resources on our website and discusses how Anyone Can Learn Data Science and Statistics!

If you are looking to switch careers or start a new career in data science, you have come to the right place. Currently, data science is one of the most in demand job positions and with this demand comes substantial salaries.

Recent estimates from GlassDoor show that the average annual income for a data scientist is $113,309 a year. Even better, it is a job that allows for considerable flexibility for remote working and telecommuting situations, which can be a great benefit for many people.

Data science is also an exciting and intellectually challenging profession, that draws from several existing fields of study to analyze, visualized and interpret the vast quantities of data that exist in the modern world.

Data science = programming + computer science + statistics + creativity

Despite this diverse collection of benefits, you might encounter challenges along the way as you learn new concepts in data science, programming and statistics, while trying to apply this content to practical, real-world applications in an academic or industry setting.

To make sure you understand the material and master the fundamentals, there are several steps you can take that will help you to succeed in your learning and beyond in your professional career as a data scientist. We have assembled a list of five tips below that you can apply to make your journey as smooth as possible.

How to succeed in a career in data science

1. Make sure to learn and master the fundamentals before moving to more complex material. This means that you should learn basic concepts including data cleaning, working with data frames, running vector operations, fundamental logical operators, as well as steps to running statistical analyses, including descriptive and inferential statistics. If you are interested in getting into machine learning you should learn the conceptual frameworks and key functions for classification and regression, which are required to understand more complicated material.

2. Don’t be afraid to ask for help on online message boards and forums if you encounter a data science or statistics problem that you can’t solve on your own. Stack Overflow is an excellent resource for posting code for feedback. The best part is it is free and includes thousands of expert programmers and data scientists, often including package author’s themselves. For a particularly cool and helpful page on Stack Overflow, check out Hadley Wickham, developer of the tidyverse, a wildly-popular and influential group of packages to analyze, manipulate and visualize data.

3. (Related to 3) Expect to make mistakes and don’t worry about spending time debugging your code! Everyone makes mistakes, even the pros, so don’t worry. Make sure to double check your code and don’t hesitate to reach out to more experienced programmers online or in your professional network. Most people are more than happy to help and it might even lead you to a job or co-author on a script or research publication!

4. Make sure you have a functioning and ideally high-performance laptop and/or desktop computer made for data science programs. By ensuring that you have the most up-to-date technology with multi-core processors and (ideally) 32 or even 64 GB of RAM, you will be able to run most or all of the existing types of models, which also future-proofing your hardware for advances in algorithm development, machine learning and AI.

5. Find a mentor to help you in your data science journey and keep you motivated to continue learning and growing your knowledge. The benefits of having a great professional and personal mentor to guide you and answer questions cannot be understated. If you don’t know anyone locally, check on places like Reddit‘s r/programming or r/datascience subreddit message boards, where thousands of people are open to help debug code and answer specific questions.

The bottom line: There as never been a better time to get into a career in data science

If you apply these five tips, you will be far ahead of the competition and will be equipped with an extensive collection of skills in data science and statistics!

If you learned something new in this article and want to check out more of our resources, take a look at our page that covers installing statistics packages, our collection of guides to running basic statistics in R, Python, Stata and SPSS, and expert reviews of data science technology to maximize your productivity.

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