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  • Writer's pictureKelly Adams

Learning Path for Data Analysts

Updated: Jun 30, 2023


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In a post a few weeks ago, What is Data Analytics?, I went over the field of data analysis and the general skills necessary to become a data analyst. But I didn't go into detail on how to become a data analyst. I will be diving into the learning path for the skills needed to become a data analyst.


I am not a professional data analyst (yet), but I have curated the advice from other data analysts such as Alex the Analyst, Luke Barousse and data scientists like Tina Huang. Along with advice from podcasts like Data Career Podcast or How to Get an Analytics Job, or websites like Datacamp, and freeCodeCamp to create this article. See the resources section for the full list of resources I used. This learning path doesn't require any formal education like obtaining a degree from a university; all you will need is a computer with internet access and a willingness to learn.

First, before getting into the specific skills needed to become a data analyst, I below are the two broad types of skills employer's reference:

  1. Hard Skills - abilities that let you tackle job-specific duties and responsibilities (e.g. being able to program in Python, draft legal documents, create a business plan), these are easy to measurable, you either have the skill or don't.

  2. Soft Skills - interpersonal skills that determine how well a person is able to work with others (e.g. being able to communicate effectively with others, work well in a team, adapt well to your surroundings), these skills are harder to measure.

Then I will list and define the specific skills needed for data analysts, both the soft and hard skills. And where to learn these skills along with the recommended resources and a general overview. Finally I will discuss what to do after you've obtained a basic/intermediate level of competence with each of those technical skills and a variety of soft skills: create projects and showcase your work using a portfolio.


Table of Contents:

 

Hard Skills:

There are specific technical skills you need to become a data analyst. If you're looking for an entry level data analyst job you should at least have a beginner/intermediate understanding of at least two of the below skills (SQL is highly recommended as one of those). But the goal is to be competent in all of the skills, I learned them in the following order below.

  1. Spreadsheets - Microsoft Excel

  2. Programming Language - R or Python

  3. Communicate with databases - SQL (Structured Query Language)

  4. Data visualization software - Tableau or Power BI

This isn't necessarily the order they should be learned in but it was how I learned it. I began with having an intermediate level of knowledge in Excel, then learned the basics of SQL, Tableau and finally Python (I already had experience with R in college). I would personally recommend an understanding in at least two of these skills. A programming language may not be necessary for a data analyst job but if you have the time it's good to learn.


Spreadsheets

Arguably one of the most common ways to analyze data, Excel is still a popular tool today. It can be used to create simple charts and conduct an intermediate level of analysis on a small dataset. You should aim to be competent/knowledge with the concepts listed below:

  • VLOOKUP

  • Charts

  • AVG, MIN, MAX, SUM, COUNT

  • COUNTIF

  • LEN

  • LEFT, RIGHT, MID

  • CONCATENATE, TRIM

  • DISTINCT

  • COUNTA

  • SUMIF

  • Pivot tables

  • Sorting and filtering data

SQL

SQL is a standardized programming language that's used to manage relational databases, particularly useful for handling structured data. Most data analysis and data science jobs use SQL frequently. It is used to manipulate and change data in databases. You should aim to be competent/knowledge with the concepts listed below:

  • SELECT, FROM, WHERE

  • GROUP BY, ORDER BY, FILTER

  • UPDATE

  • DISTINCT

  • SUBSTR, TRIM

  • CAST, COALESCE

  • CONCAT

  • EXTRACT

  • LEN, FIND, RIGHT, LEFT

  • Joins - INNER JOIN, LEFT JOIN, RIGHT JOIN, OUTER JOIN

  • COUNT, COUNT DISTINCT

Programming Languages

A programming language is a formula language used to talk to computers and give it instructions on what to do. Knowing how to use a programming language to analyze data is not always necessary for entry-level analysts. It depends on the company and job. But if you have the time and want to stand out you can learn one of the programming languages below.


The most popular is Python, but R is used as well for data analysis, specifically in the academic field. If you would like a breakdown of which programming language to use it depends on where you are applying but here are a few articles describing the difference between R and Python: Choosing Python or R for Data Analysis? An Infographic or Python vs. R for Data Science and why you are wasting your time. I have listed below the concepts and packages/libraries you should be competent/knowledgeable for each language. Pick one language and commit to learning it, once you've done that you can move onto other programming languages, most suggest beginning with Python.


Python

Python is a popular high-level general purpose programming language. Its focus is on code readability and the ability to easily start creating programs. Python is used by a wide variety of people from developers to data scientists and in many different fields like Technology or in Academia.

  • Data-scraping

  • Jupyter Notebooks

  • Reading Data

  • Cleaning Data

  • Libraries: Pandas, NumPy, SciPy, Matplotlib


R

R is a programming language specifically for statistical computing and graphics. It is used by statisticians and data miners to analyze data. And is quite popular in the academic field but isn't as popular in business analysis. It is also used in the broader field of data science and for machine learning.

  • Reading data

  • Cleaning data

  • Dataframes

  • Data visualization

  • Packages: ggplot2, dplyr, tidyr, data.table


Data Visualization

This is the graphic representation of data. The most common tools are Power BI or Tableau. But recently I've also seen a rise in Google Looker Studio. No matter what the tool is you should be able to create the following charts below. As well as understand which chart to use depending on the data and business objective.

  • Line chart

  • Bar chart

  • Heatmaps

  • Pie chart

  • Scatter plots

  • Histogram

  • Density plot

Soft Skills:

Being a data analyst isn't all about the technical skills and knowledge. You need soft skills as well to work effectively with others. Below are common soft skills used in the field of data analysis.

  1. Communication - being able to communicate effectively with others

  2. Problem-solving - able to solve complex problems

  3. Research - find the answers to a problem

  4. Attention to detail - have a high level of attention to detail

  5. Teamwork - able to work with others

  6. Critical Thinking - able to think critically about problems you may face

You probably already use some of these skills in your professional life now. Think about what skills you already use in your professional and non-professional (e.g. volunteer experience, non-profit organization, local clubs) settings. Then determine how your use of skills now relates to a data analyst position. For example, I'm currently a paralegal so I have an eye for detail that is necessary to complete legal documents, and can translate well to the detail needed to analyze a dataset.

To further improve on these skills you could join a local professional association, obtain a leadership position, or attend a conference or webinar. As stated before, these skills are harder to quantify and measure, but are as important as technical fields. Unless you're in the top of your field, generally if you're difficult to work with (lacking in soft skills), it will be challenging finding a job.


If you want to read more about transferrable soft skills for data analytics check out my article here.

Where to Learn

To learn or practice the hard skills there are several options besides a formal degree: online certificate courses, bootcamps offered by universities, online bootcamps offered by individual companies, individual courses, books, and free resources. While utilizing these resources you're not only practicing your technical skills (e.g. Python, SQL, Tableau), but your soft skills as well like problem-solving, research, or critical thinking. I've listed the types of resources below with some examples, the general cost ($ - $$$), time it takes to complete, a basic overview and a too long didn't read (TLDR) at the end.


I also have a post where I link all of my resources for learning data analytics including course websites, specific courses and podcasts. Check it out here.


Below is an overview:

  1. Online Certificate Courses

  2. Bootcamps from Universities

  3. Online Bootcamps from Individual Companies

  4. Individual Online Courses

  5. Books

  6. Free Resources


Online Certificate Courses


Cost: $$, depending on the course $100-$300


Time: 6-8 months


Overview: This is more of the middle path between an online bootcamp and taking individual courses by yourself. It has the benefits similar to an online bootcamp with a clear learning path on what skills to develop and instructions on how to do so. But the price is more similar to buying several individual courses, and is definitely cheaper than a bootcamp. While a certificate course is a great introductory resource for new data analysts it is not formally regulated like a university, there is no standard for students who graduate the program. Meaning two people could both have the same certificate: one person skipped through the material and kept hitting next, the other dedicated more time to studying the material but have a different level of skill. Having a certificate can help in the job search but shouldn't be solely relied on.

TLDR: Clear learning path, instructions to learn, not regulated so employers may not value the certificate, moderate price


Bootcamps from Universities


Cost: $$$, Costly anywhere from $10,000 to $13,000


Time: Part-time: 6 months and Full-time: 4 months


Overview: Bootcamps from universities are expensive and time consuming but it provides a clear layout of the skills you need to learn and how to do it. They also help build your network to meet other aspiring data analysis and current professionals in the field. Because these types of bootcamps are provided by universities it may give more credibility to your skills/knowledge than an online bootcamp from an unknown institution but even these bootcamps are not formally regulated and therefore not accredited.

TLDR: Clear learning path, access to instructors and professional network, expensive, time-consuming, not accredited but may be more credible


Online Bootcamps from Individual Companies


Cost: $$$, costly anywhere from $6,000 - $12,000


Time: Part-time: 6 months, Full-time: 4 months


Overview: Online bootcamps are similar to bootcamps from university, both have a clear set of skills the person needs to learn, along with a plan on the how to do it. Also, it can help you network with other students and instructors and possible employers as well. But because these are online and are not regulated by the board of education there are no standards for these bootcamps. The quality of your education varies. It is cheaper than a bootcamp associated with a university but still expensive with a price tag of a few thousand dollars.

TLDR: clear learning path, access to instructors, potentially build a network, expensive, time-consuming, not accredited


Individual Online Courses


Cost: $$, roughly $20-$100 per course


Time: Depending on the course 10-30 hours


Overview: Individual courses from online resources like Udemy and Udacity are great for when you need to practice or learn specific skills like SQL or Power BI. With the sheer number of online courses it can be difficult to weed out the bad ones and you can waste time or money on ineffective courses. If you wait for sales on courses you can buy a course for $20-$40, which is relatively low cost and provides clear instructions on how to learn and practice a specific skill. Depending on the length of the course you could finish an individual course anywhere between 10-30 hours. You could also take a course from your local community college but it may be more costly.

TLDR: relatively low cost, great for practicing specific skills, no way to differentiate the good courses from the bad ones


Books


Cost: $, about $20-$50 per book


Time: Depends on how quickly you read


Overview: Similar to individual courses books can be great for learning a specific skill. Because technology is quickly improving, books may be out of date when it comes to the latest information. Books are also a relatively low cost way to learn, with the price varying between $20-$40 per book. There is no clear learning path when buying individual books so it's possible to become lost or focus on unimportant skills. Lastly, there are no deadlines so how quickly you learn depends on your own pace.

TLDR: relatively low cost, time to finish depends, may have out of date information


Free Resources


Cost: free


Time: Depends on your own pace


Overview: Other free resources like Youtube videos, websites, podcasts, webinars, newsletters are great for learning the ins and outs of becoming a data analyst. And can be useful for getting advice from others in the field. But it has its limitations since there's so many resources out there it can be difficult to know which ones are credible. There's also no clear path on what to learn about next, and you can waste time figuring out what to learn next. Lastly, there is no timeline or accountability so how quickly you learn depends on your own pace.

TLDR: no cost, difficult to weed out good resources from bad, no clear learning path, lack of accountability


How to Showcase Your Skill

Once you've obtained a basic to intermediate level of knowledge/competence in these skills you should work on projects to showcase your skill and build a portfolio to display those projects.


Projects

You can either create your own personal project to try to solve a problem and obtaining the data either by webscraping, collecting it yourself or using a free public data source. The other option is to go through guided projects or challenges like on Kaggle or Maven Analytics. This option is great for beginners. Many of my early portfolio projects were guided projects. As you get better it's better to have personal projects. Most professional data analysts suggest the first option, personal project, because it demonstrates you have the ability to work on a project from end-to-end and are comfortable with the data analysis process. It also makes you stand out by working on unique projects you're interested in and it's easier for you to talk about it. While there's no harm in exploring and analyzing popular datasets to practice or share like the Titanic dataset from Kaggle, you should focus on having more personal, unique projects.

Portfolio

After having a few personal projects (3-5) you need a place to showcase it. You can use sites specifically for showcasing your skills like: Tableau Public, Github or Kaggle. Maven Analytics has a platform where you can showcase your portfolio as well. There's also Carrd.co which has been recommended by Matt Mike to create a simple and free landing page. Or you can create your own website and showcase your projects there. This option is the best for projects that use a variety of tools and can't be showcased on one platform alone. But it is time consuming and can cost more (depending on how you host your website). You can view my personal portfolio here for an example of a portfolio. Or check out this article How to Build an Awesome Data Science Portfolio by Harshit Tyagi from freecodecamp as well for a guide on.


Conclusion

Hopefully this has given you an idea of what skills to learn in order to become a data analyst. I'm not a data analyst yet but I'm on my own learning path to becoming one, my last step is to learn Python and improve my SQL course. I didn't go into advice on interviewing and job preparation in this article but I will in a future one. Stay tuned for that.

If you're a data analyst and would like to broaden your knowledge then subscribe to my newsletter, Kelly's Bytes, where I send out 3 bite-sized resources each week. One resource on data analytics, switching careers, and learning.


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