A lot of my friends ask me how to get into data analytics or data engineering. It’s not surprising—the market for data jobs is booming. As the volume of data grows, so does the need for skilled data analysts and engineers.

If you’re thinking about getting into data, I highly encourage it! Working with data is challenging and allows me to continuously learn, pushing me to grow personally and professionally. I initially got into data by learning structured query language (SQL) and creating dashboards with any data I could find online. I started reading about the psychology behind data visualizations, and that was the hook that drove me to where I am today.

Now, I work as a cloud data engineer at Data Clymer, a data consulting firm that helps organizations unlock the power of their data to solve business problems. Our team is made up of analytics engineers who love nothing more than geeking out over data—and helping others do the same.

Tons of resources are available to kickstart your journey in the data industry, ranging from complimentary data courses and online tutorials to interactive challenges and supportive communities. This article shares a few of the top recommendations from the Data Clymer team.

First, a note on job titles…

The world of data is a rapidly growing and changing job market. New and exciting job titles are constantly emerging in the field of data, often lacking a standardized definition. Therefore, it is important not to fixate too heavily on a single job title, as it may encompass a wide range of responsibilities and vary considerably from one organization to the next.

With that said, here are several job titles worth considering as you explore opportunities in the data industry:

Data Analyst

  • A data analyst identifies, collects, cleans, analyzes, and interprets data in order to extract insights for better business decisions. They have a solid understanding of business needs in order to inform their process.
  • Key skills: Building visualizations and dashboards using a variety of business intelligence tools and SQL

Data Engineer

  • A data engineer is a type of software engineer who designs and builds systems for collecting, storing, and analyzing data. This work often involves building data pipelines to gather data from different sources.
  • Key skills: Python, SQL, ETL, cloud computing, database architecture, data security, and data storage

Analytics Engineer

  • An analytics engineer lives in the world between a data engineer and and a data analyst. A lot of their work involves transforming data to be ready for analysis. They need a strong knowledge of data structures as well as business needs.
  • Key skills: Data modeling, SQL, and software engineering best practices

Data Scientist

  • A data scientist creates algorithms and predictive models using data to solve business problems and make recommendations.
  • Key skills: Programming languages, big data frameworks, machine learning, data cleaning

Resources for Getting Into Data

Whether you want to become a data analyst, data engineer, analytics engineer, data scientist, or have no idea what you want to do, the resources below will come in handy.

SQL Tutorial

SQL is the backbone of the data industry. Since SQL is an essential component of virtually all data analytics roles, learning this language is an excellent starting point regardless of your specific area of focus.

SQLtutorial.org reviews the foundations of SQL. Go through this tutorial before practicing specific skills.


Kaggle has a boatload of stuff, ranging from unstructured data analysis to full-on “find the third cousin of this crime scene hair sample in our DNA database.” They even have paid competitions to give you extra incentive, including some that are perfect for beginners.

It can be overwhelming if you don’t know where to begin, so we recommend jumping in with everyone’s favorite language: SQL. Check out this collection of discussion boards, practice problems, and cheat sheets to help you get started on your journey!


Create an account with CodeWars and specify the languages you want to learn. With daily practice, it’s a great tool for upskilling and challenging yourself!


Whether you’re just getting started or have several years of experience, HackerRank is another creative way to flex your SQL muscles. It offers SQL learning with varying degrees of difficulty, so there’s always something to work on!

8-week SQL Challenge

True to its name, 8-week SQL Challenge has eight weeks of self-guided case studies. It’s a great way to dedicate yourself to learning SQL and can help you get started on your own GitHub Pages personal website and project portfolio.

Bonus tip: Follow the founder, Danny Ma, on LinkedIn. He has a lot of good career advice for people looking to make a jump.

100 Days of SQL

Have we mentioned that SQL is a great place to start? 🙂

Inspired by 100 days of Python, data scientist Harold Rojas decided to start 100 days of SQL on GitHub. Build the habit with the help of challenge practice platforms that review code.

Data in Motion, LLC

Data in Motion, LLC is a great LinkedIn community for those looking to level up in data. You’ll find study halls, live Q&A/coffee chats, mock interviews, and more.

Data Engineering Projects

If you’re looking to land a job in data engineering but don’t yet have experience in the field, get started with a few data engineering projects. You can find a slew of project ideas and helpful tips in 7 Data Engineering Projects to Level Up Your Skills in 2023.


A quick Google search will introduce you to tons of data analytics bootcamps. (19,300,000 results, to be exact.) With all these options, it can be challenging to choose the right one. While most bootcamps aim to prepare you for a range of roles in the industry, it’s worth noting that each program may have its own unique focus or emphasis.

One way to narrow it down and decide if a bootcamp is right for you is to look up their curriculum or ask for a syllabus if one isn’t publicly available. Then, look up free tutorials or low cost beginner courses (like Udemy) to see if you are interested in the topics. You can also ask their admission representatives what types of job titles graduates usually have. Look at job descriptions for openings with the same titles and look for articles or blogs from people describing what they actually do. (Data Clymer has a few!)

Another challenge is, bootcamps can be expensive and may pay off for some but not others. Certain bootcamps offer an income-share agreement (ISA), allowing you to defer payment for the program until after you secure employment, at which point you will gradually repay the cost from your income. This is an interesting model that makes it more accessible. There are also bootcamps that partner with local universities and have similar coursework.

Free and Low Cost Programs for Veterans

If you’re a veteran, take a look at the free and low-cost programs out there. A couple to get you started:

  • Code Platoon offers extremely affordable programs and aims to reduce students’ out-of-pocket costs to almost nothing. Scholarships are also available. If you’re leaning into data engineering, you can sign up for more information on upcoming data engineering bootcamps and programs.
  • GI Bill Coding Boot Camps and Coding Schools: Explore campus, hybrid, and online coding boot camps across the country. The Department of Veterans Affairs maintains a list of approved coding schools that accept GI Bill benefits and other VA benefits programs.

Data Clymer LinkedIn and Blog

Shameless plug: our team at Data Clymer is full of experienced data engineers and data analysts. We love sharing data tips and other helpful resources to educate and guide data professionals through every step of their data journey. Follow us on LinkedIn to stay up to date with all our latest content, or subscribe to get new articles, webinars, and other resources sent straight to your inbox.

P.S. When you’re ready: check out our data analytics jobs and career opportunities.

Good Luck!

Transitioning careers or learning new skills can be overwhelming. The most important thing to do is to start the process. Thanks to all the Data Clymer team members who contributed ideas and recommendations to this article!