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 TypeTypical RoleSkills Needed
Data AnalystVisualization and Communication of DataExcel, SQL, Visualization (Tableau, Visier, etc.)
Data ScientistModeling Data and AutomationStatistics, SQL, Python, R
Data EngineerDatabase Administrator, Data ArchitectAdvanced 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

Skills Needed

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!

Graduate Programs

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

Certifications (MOOCs)

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.

MOOCSuggested CoursesCost
CourseraPython for Data Science and AI, SQL Basics for Data ScienceFee
DataCampIntro to R, Intro to TableauAlways Free
edXProgramming for EverybodyFree; Fee for Certificates
General AssemblyData Analytics, Free WorkshopsFee and Free Options
KaggleData Cleaning, PythonAlways Free
Khan AcademyComputer Programming ResourcesAlways Free
LinkedIn LearningPandas for Data Science, Data Visualization ConceptsFee; Free for Students
UdacityIntro to SQL, Data Analysis with Python and SQLFee
UdemyPeople Analytics 101, Python for Data Analysis and VisualizationFee

General Advice

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.

Mariah Norell
Mariah Norell
Data Scientist & Lecturer

My research interests include pay equity, diversity and inclusion, and women in leadership.

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