Pursuing an Analytics Career
If you’re just getting started, or decided to pivot your career into analytics, this guide is here to serve as the building blocks for your next steps.
Choosing a Path
The type of analyst you become is pretty universal among the various fields and industries. Here is a list of the types of jobs you could have and their typical duties. I recommend first choosing one of the three options below to begin your journey of landing a role on an analytics team.
|Job Type||Typical Role||Skills Needed|
|Data Analyst||Visualization and Communication of Data||Excel, SQL, Visualization (Tableau, Visier, etc.)|
|Data Scientist||Modeling Data and Automation||Statistics, SQL, Python, R|
|Data Engineer||Database Administrator, Data Architect||Advanced Programming, ETL|
Common Job Titles
With most analyst positions, the naming convention is pretty straightforward. Looking up any of the job types from above will provide a multitude of jobs, but I also recommend that you always review the job description to see if the position is in alignment with your goals and skills before applying.
For those specifically interested in the People Analytics field, there are a variety of job titles that stand out from the typical ‘Data Analyst’ naming structure.
Here’s a list of some common job titles for those pursuing a career in People Analytics to help with your search:
- Business Architect/Specialist
- HR Analyst/Associate
- People Analytics Associate
- Talent Acquisition Analyst
- Workforce Analyst
Regardless of the type of analyst you decide to become, there are many important skills you’ll need in order to be a competitive candidate.
These five skills are what employers are looking for in an analyst:
- Data Visualization
- Curious, Inquisitive Mindset
- Attention to Detail
- HR and Business Acumen
- Knowing a Programming Language
Essentially, it comes down to being able to combine quantitative and qualitative judgement, and apply the knowledge and skills you’ve learned along the way. Whichever path you decide to take, make sure you are applying the skills to real projects and not just memorizing terms.
Bonus Tips: (1) Find yourself a mentor, and (2) create a GitHub account to create your very own online portfolio.
Where to Get Started
Now that you know some of the general skills needed and types of career paths, it’s time to make a game plan for yourself to start learning the material. The two major choices you should consider is (1) continue your educational career by going to graduate school, or (2) go at your own pace and enroll in online certifications.
Graduate Programs vs. Certifications
Typically, Data Engineers and Data Scientists have advanced degrees in a STEM-related field and Data Analysts have undergraduate degrees with relevant experience. Don’t feel pressured into getting a graduate degree; you can always work your way up through gaining on-the-job experience!
If you’re considering graduate school, I recommend paying close attention to the name of the program and review the actual courses taught to see if it meets your expectations. Take it a step further by requesting access to the syllabus to see what content is covered in the curriculum and assess your own skillset. Lastly, be wary of the entry requirements (some graduate programs require STEM courses like calculus to be completed before entry) and most importantly, determine whether you can afford it.
Note: Many programs can cost between $50K-270K and take 1-4 years to complete. It’s crucial to do your research!
Here are some of the most common Master of Science and PhD graduate programs for an analytics career:
- Business Analytics
- Computer Science
- Data Analytics
- Data Science
- HR Analytics
- I/O Psychology
- Machine Learning
MOOCs, or Massive Open Online Courses, are extremely popular for continued education resources, and for many great reasons - they’re cheaper and more flexible. While many of the MOOCs provide a badge of completion or a certification after passing an assessment, some are even offering full-fledged degrees!
Review the following table for a list of my favorite MOOCs and suggested courses on building an analytical skillset that employers are looking for.
|Coursera||Python for Data Science and AI, SQL Basics for Data Science||Fee|
|DataCamp||Intro to R, Intro to Tableau||Always Free|
|edX||Programming for Everybody||Free; Fee for Certificates|
|General Assembly||Data Analytics, Free Workshops||Fee and Free Options|
|Kaggle||Data Cleaning, Python||Always Free|
|Khan Academy||Computer Programming Resources||Always Free|
|LinkedIn Learning||Pandas for Data Science, Data Visualization Concepts||Fee; Free for Students|
|Udacity||Intro to SQL, Data Analysis with Python and SQL||Fee|
|Udemy||People Analytics 101, Python for Data Analysis and Visualization||Fee|
One of the best pieces of advice given to me was to start small. It can be overwhelming trying to learn everything - you don’t need to become an expert overnight!
Everyone’s path is different. If you want more advice, I’d be happy to hop on a call with you. You can schedule time directly with me here: calendly.com/mariahnorell.