Introduction
Hi my name is Kelly Adams, I'm a full time data analyst. I recently made the career switch from paralegal to data analyst in July of 2023. I didn't hire any career services or take any bootcamps. I stuck with free or low cost resources. While my career transition took me longer than I thought, I learned a lot along the way. I wanted to share a comprehensive guide on how to become a data analyst for free. In this guide we'll go through every step of the data analytic journey, from the hard and soft skills, where to learn these skills, how and where to apply along with a few interview tips. I'll cover networking strategies, where and how to build a portfolio.
If you're looking for resources check out this article: Data Analytics Resources.
Disclaimers: Please note the following disclaimers.
Doing all this isn't a guarantee that you will become a data analyst. This is simple a guide/roadmap to help people out. You don't need to follow every piece of advice here to get a data analyst job. Everyone's journey is different.
There is also no set timeline. It took me 8 months of applying and 8 months of learning before that. Everyone has a different timeline.
I am also not a hiring manager nor do I have experience in HR. This is all from my experience and stories I've heard from other data analysts who've transitioned careers.
Note: All this job information is for trying to find a job in the United States job market. Other countries may have different practices that you need to be aware of.
Table of Contents:
Order
Now what's the order to do everything? Well it depends. If we're talking about the technical skills you want to learn one skill at a time. Then afterwards build a portfolio project for it. So here's an example:
Learn Excel -> Build Excel project
Learn SQL -> Build SQL project
Learn Tableau -> Build Tableau project
But if we're talking about everything in this guide. Then below is a general flow/roadmap of what I'd recommend:
Learning Skills
Complete Projects
Build a Portfolio
Network
Apply to Jobs
While networking is #4, in reality you always want to be networking whether you're looking for a job or not. But the time you spend can be adjusted. For example, while you're job searching it's important to have a robust network and dedicate time to it. If you already have a job it's still important to keep networking but you can dial it back.
Skills
First, before getting into the specific skills needed to become a data analyst, below are the two broad types of important skills:
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.
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.
There's also another important but often forgotten about skill: business knowledge. While this is technically a soft skill it's so vital that I made a separate section for it.
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 the skills below, I learned them in the following order below.
Spreadsheets - Microsoft Excel
Communicate with databases - SQL (Structured Query Language)
Data visualization software - Tableau or Power BI
Programming Language - R or Python
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
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. 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
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
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 usually not necessary for entry-level analysts. It depends on the company and job. You should focus on the three skills above first. 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. 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
Soft Skills
Being a data analyst isn't all about the technical skills and knowledge. You need soft skills as well to work with others. Below are common soft skills used in the field of data analysis. You've probably used all these skills in your current or previous roles.
Communication
Communication is the act of transferring information from one person to another (or a group). It's a necessary skill for data analysts. Because the main role of an analyst is to help a company make informed business decisions with data. To make an informed business decision analysts need to understand the business context. They have to communicate with their co-workers, supervisors or stakeholders. The data the analyst needs to present these findings and recommendations to other people. Being able to explain complex ideas to people with a non-technical background is vital.
Problem Solving
This is the ability to find solutions to difficult or complex issues. Someone who's good at this can identify reasons why a problem exists then execute a plant to resolve it. Problem solving is vital for analysts. Analyzing and cleaning data isn't easy. Things can go wrong in the database or code and an analyst must be able to troubleshoot the problem so they can continue their work. Other problems may come up and it's good to be able to think on your feet and be innovative.
Critical Thinking
Critical thinking is the ability to think clearly and rationally to understand connections between ideas and/or facts. People who are critical thinkers will often think deeply about an issue, ensuring that an idea, policy, or product is thought through. As a data analyst you need to be able to ask the right questions to get the right information. Most of the time the results won't be clear. Being able to analyze a problem and look at it at different angles is vital.
Teamwork
Teamwork is the ability to work well with others. A person who's good at teamwork: supports their team, motivates others, and both giving and receiving feedback. Data analysts collaborate with a variety of people. Whether that's other analysts or people in other departments. These people can be web developers, engineers, data scientists, along with internal and external stakeholders. Being able to work in a team with other people is a necessary skill for any analyst.
These aren't the only soft skills necessary for data analysts but these are the most common.
Business Knowledge
Another important but overlooked skill is: business knowledge. As a data analyst, the primary objective is not just to analyze data but to get insights from it that can drive business decisions. Essentially, you are a problem solver for business challenges. This context is fundamental before diving into data.
Note: This is considered a soft skill but it's so important I wanted to give it it's own section.
Examples:
Below are some examples as why business knowledge matters for data analysts.
E-commerce Company: Imagine being an analyst for an online computer vendor. You're assigned to assess sales data. Without business context, you might overlook key insights. For instance, recognizing that the company recently launched a loyalty program can help you understand the uptick in sales among both new and returning customers.
Subscription-Based Service: Tasked with studying customer loss for a subscription service? Not being aware of recent changes in pricing or marketing strategies might lead you to misinterpret an increase in churn rates.
Healthcare Data: When analyzing patient satisfaction data for a healthcare provider, being informed about recent policy changes or regulations becomes invaluable. A policy shift in insurance, for example, could influence patient opinions. Lacking this knowledge could alter your conclusions.
5 Reasons to Prioritize Business Understanding:
If you're still not convinced, here are five more reasons to always consider the business context:
Problem Framing: Understand the objectives, KPIs (key performance indicators), and overarching goals of the business. This clarity ensures your efforts resonate with company needs.
Data Relevance: Business knowledge helps you discern valuable data from the noise. This discernment, which I once overlooked, can save time and resources.
Data Interpretation: Business acumen is crucial for interpreting data. After your analysis, can you understand how it can help the business? This new way of thinking highlights important insights.
Effective Communication: Analysis often needs translation for a non-technical audience. Solid business context allows you to explain the 'why' and the 'how' of your findings.
Solution Recommendations: Initial analyses usually sets the stage for actionable strategies. A deep business understanding ensures these strategies are both viable and align with the organization's goals.
How to Develop Business Knowledge
Here are a strategies on how to do it:
Read Industry News: Stay updated with the latest trends, challenges, and breakthroughs in your industry. You can use resources like websites, magazines, and industry reports.
Take Business Courses: Consider enrolling in business courses or workshops. More on which platforms and where to learn in the section below.
Feedback: Ask for feedback on your projects. One way to do this is to post your project on LinkedIn to get critique from other analysts. This can help your future analyses and understand where you can improve.
Network: Have conversations with professional data analysts from a variety of industries. I'm a fan of using LinkedIn for this but you can also attend conferences or local meetups.
Always Ask 'Why?': In your personal projects, always ask why it would be important to the business. Understanding the bigger picture helps your analysis to be more impactful.
Where to Learn
To learn or practice these skills (specifically the hard skills and learning business knowledge) 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. I've listed the types of resources below with some examples, the general cost ($ - $$$), time it takes to complete, a basic overview and a too long didn't read (TLDR) at the end.
I also have a post where I link all of my resources for learning data analytics including course websites, specific courses and podcasts. Check it out here.
Below is an overview:
Online Certificate Courses
Bootcamps from Universities
Online Bootcamps from Individual Companies
Individual Online Courses
Books
Free Resources
Online Certificate Courses
Examples: Google Data Analytics Professional Certificate (which I've taken) or IBM Data Science Professional Certificate
Cost: $$, 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 of an online bootcamp with a clear learning path and instructions on what skills to develop. But the price is more like 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 relied on.
TLDR: Clear learning path, instructions to learn, not regulated so employers may not value the certificate, moderate price
Bootcamps from Universities
Cost: $$$, Costly anywhere from $10,000 to $13,000
Time: Part-time: 6 months and Full-time: 4 months
Overview: Bootcamps from universities are expensive and time consuming but it provides a clear layout of the skills you need to learn and how to do it. They also help build your network to meet other aspiring data analysts and current professionals in the field. Because these types of bootcamps are provided by universities it may give more credibility to your skills/knowledge than an online bootcamp from an unknown institution. But even these bootcamps are not formally regulated and therefore not accredited.
TLDR: Clear learning path, access to instructors and professional network, expensive, time-consuming, not accredited but may be more credible
Online Bootcamps from Individual Companies
Cost: $$$, costly anywhere from $6,000 - $12,000
Time: Part-time: 6 months, Full-time: 4 months
Overview: Online bootcamps are like 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, build a network, expensive, time-consuming, not accredited
Individual Online Courses
Examples: Complete Python Bootcamp From Zero to Hero course from Udemy or Learn SQL Basics for Data Science Specialization from Coursera
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
Examples: Storytelling with Data by Cole Nussbaumer Knaflic or Data Science for Business by Foster Provost or The Art of Data Science by Roger Peng
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. 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
Examples: Youtube - Luke Barousse, Tina Huang; Websites - Data Camp, freeCodeCamp; Podcasts - Ken's Nearest Neighbors, SuperDataScience Podcast
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. 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 Skills (aka get experience)
Next, how do you showcase these skills? There are a few ways. But two of the ones I recommend are:
Building a portfolio
Through your current role
Portfolio
One common and accesible way to showcase your skills is having a portfolio. A place where you can display your personal projects (3-5 projects). 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 my post: A Guide to Creating a Well Rounded Data Analytics Portfolio.
For each project, depending on the platform I'd recommend having the following:
Title of the Project
Brief Description
Image - this makes it more appealing and eye catching
Link to the project or option to download
What kind or 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.
Need ideas? Check out these articles
Current Role
The main idea behind making your role data-driven is to collect and analyze data relevant to your job and use the insights to make informed decisions. It also shows potential employers your transferrable skills (e.g. problem solving, communication) and your data analysis ability.
Example
In my part-time tutoring job, I decided to monitor my students' performance in tests, quizzes, and assignments. I recorded all their grades in a spreadsheet to analyze their performance. If I noticed they didn’t do well on quizzes or tests, I tried to determine whether it was due to a lack of test-taking skills or a misunderstanding of the concepts. Depending on the cause, I changed my tutoring approach to address the areas they needed to improve. As a result, my students’ grades improved significantly. On average, I helped raise grades from an F to a C.
This approach essentially involved analyzing student performance data, extracting insights on how they did, and accordingly adapting my tutoring methods, which contributed to raising their grades.
Other Options
Below are some other options to get more experience. I'll touch on those briefly. If you want more details check out my article: How to Get Experience in Data Analytics without a Full-Time Job.
Freelance - you can become a freelancer and help businesses improve with your data analytics skills
Volunteer - find a non-profit who needs help wrangling and analyzing their data
Part-time job - find a part-time data analyst job, it lets you gain real work experience and get an income
Content creation - create content on sites like LinkedIn, Youtube or Twitter
Networking
You should always be networking. Even if you're not actively looking for a job. You never know when your next connection could lead you to a job. I used to set a goal to reach out to 5 people a week. One every workday. It could be through coffee chats, through LinkedIn direct messages, or by adding a new connection.
Why You Should Network
Besides the possibility of landing a job with your network. Here's are some other reasons: learn something from others or make new friends. The benefits are numerous, but beyond building a network, what you need is a closer group of people who can significantly impact your career.
Who to Network With
You need to build a Jedi counsel. I won't get too nerdy but it's a professional group of people who will help you with your data career. They can offer advice, support you when you're struggling, and provide you with a source of motivation, and education. Think of it as building a team. Everyone has unique and individual talents.
It will have 4 main types of people:
Jedi Master - These people who have already enjoyed the type of success you're after and are willing to share their wisdom. These are people who are a master of their craft, always learning and improving, and is willing to mentor you.
Fellow Jedi - These are peers, people who are at the same level as you are. You can work together with them to accomplish your goals because you understand each other. It's great to have someone else who can help keep us accountable.
The Padawan - These individuals who are a few steps behind you. Even if you just started there's always someone who can learn from you. By teaching someone else not only are you helping them. You're helping yourself because the best way to learn is by teaching others.
The Wildcard - The people who push you out of your comfort zone, and helps you overcome your fears. These people help push you to accomplish things you'd never thought you could do.
Want a specific example of who you could have on your team if you're an analyst? Check out the detailed article on this topic: Transform Your Network: Create a Team for a Successful Data Career.
How to Network
Well you can either do it in person or online. For in person it could be attending conferences and joining local professional organizations. Or you could volunteer with a non profit or sit on a committee. When I was a paralegal I joined a professional association of paralegals. It let me learn more about my local law firms and meet others in my field. Now, I volunteer at a local non-profit.
The other way is online. Which is where I do most of my networking . The main benefits are: it isn't limited to who's in your area. Which is useful for those who live in more rural areas. It's also easier for shy people to get started since you're not meeting anyone face to face.
How do you network on LinkedIn?
My connection, Ashley Zacharias, gave some tips on it in her LinkedIn Post. Here's a few things:
Comment on other people's posts
Build relationships before asking for help
Sending personalized connection requests to others who work in the role you want
Reach out to recruiters, hiring managers, and people in the roles you are working toward
Posting content and engage with others in the comments
How to Become Active
Another way to network is to simple become active on LinkedIn and build connections that way. Below are three simple and easy ways to become active on LinkedIn.
Comment on other posts - Write comments that drive conversation or talk about how this post help you.
Connect with other people - The best way to build your network is to send out connection requests. When I first started out I was very open about accepting and sending connection requests. I usually sent a personalized message with my connections especially if the person add a lot of followers.
Optimize your profile - While this tip is less about engaging with others, it's still vital for anyone who is active on LinkedIn. It makes easier for someone who's looking at your profile to determine if they want to add you to their network. Or if it's a recruiter or hiring manager they can get a better sense of who you are professionally.
If you want more details on each of these tips check out my longer article on this topic: 3 Simple Ways to Become Active on LinkedIn.
Applying to Jobs
Next step is to actually apply for jobs. I'd like to preface this again by saying, I am not a hiring manager nor have I ever worked in hiring or recruiting. These are all tips I've gotten from people who do work in these fields. 5 people I would recommend are:
Paden Janney - talks about recruiting, interview tips
Ian Tynan - talks about data careers specifically
Darrel Clack - general recruitment advice
Bonnie Dilber - posts about resumes, interviews and recruiting
Erin Lewber - career changes and development
Those are a few I personally follow and enjoy reading content from. But there's a lot more out there. You can search by the #recruiting or #jobsearch on LinkedIn to find more people who talk about these subjects.
Where to Apply
Where do you actually find data jobs? Below are some popular job sites.
General tips
Below are some general tips when applying:
Tailor your resume and application for each job posting. I used to spend 10-15 minutes for each application. Do your research on the company and include keywords from the job description in your resume. You can use ChatGPT to help you with your resume but don't copy and paste its result. Proofread and edit it. Most recruiters and hiring managers can tell when people use ChatGPT for cover letters.
Try to apply to jobs that have been recently posted (within 24 hours). If a job application has been out for a while likely there are already a number of candidates who've applied. It's better to get your application in as soon as possible.
It's best to get a referral from someone you already know at the company. You have a better chance of landing an interview (but it's not a guarantee).
Cover letters
Cover letters are a divisive topic in job applications. Some people hate them and others find them useful. I'll let you form your own opinion. For me I occasionally write cover letters. I found it especially useful as someone who is transitioning careers. It was easier to tell my story using a cover letter than just having a resume. That being said I didn't take too much time to write my cover letter. I have a template I use every time for my cover letter. Here's a basic outline. If you want more detail check out my Cover Letter Template.
I'd recommend creating your own cover letter template. It'll save you time in the long run if/when you submit a cover letter. While the template you have helps you still need to research the company and job posting and talk about how your experience and skills best suit the role/company. Below is a general outline of what my cover letter looked like. For some paragraphs I already had it written out and I would change a few sentences to make it specific to the job posting.
[Date]
[Company Name]
Address Line 1
Address Line 2
Dear Hiring Manager,
Paragraph 1: Introduction
Tie in the job posting with my background. Have a few sentences explaining why your background, experience, etc. would be fit for this job.
Paragraph 2: Background
This is where I included my path to data analytics, this was useful for me because I was transition to the field. If you're experienced you can include details about your work experience and accomplishments
Paragraph 3: Emphasize a particular soft skill I have
In this paragraph I talked about a particular soft skill I have (e.g. communication, attention to detail) and tie it into the role I'm applying for. Talk about how my previous experience has helped developed this soft skill and how it will benefit the company.
Paragraph 4: List the skills I have and how they can help the company
This is where I listed specific hard and soft skills I have and how it can be beneficial to the company. This was a bulleted list to make it easier for the hiring manager to read.
Conclusion: Wrap it up
Lastly is the conclusion. I only had a sentence or two and it was expressing my interest in the role and how I'd love to learn about how the company is using XYZ to improve their company goal. And I'd always include this as my final sentence "Please let me know if there’s any additional information I can provide and thank you so much for your consideration."
Best,
Kelly Adams
Resumes
I am not in HR nor do I have hiring experience. So I am not comfortable giving specific tips but here are general ones I've heard.
Have an easy to read format
Use standard fonts like Aerial or Times New Roman
Have links to your portfolio and LinkedIn profile at the top (double check these work)
Include your contact information (at least an email and optionally a phone number)
Make sure there's enough white space so everything doesn't look cluttered
Proofread it and double check for spelling errors
Focus on your achievements not responsibilities. Use the Google's XYZ format as a guide.
If you want examples of data resumes check out this resource: Practical Advice for Perfecting the Data Resume (a digital download for $15) from Carly Taylor.
What jobs to apply for?
Don't limit yourself in the job search. Try applying for other analyst roles. These may be easier to get into if you have relevant experience from the same industry. Below are other data related roles you can apply for.
Financial Analyst -gives guidance to companies or individuals on business investment decisions by analyzing financial data (economic trends, current news, company's business strategy)
Healthcare Analyst - help improve healthcare outcomes (e.g. improve patient care, streamlining processes) using data
Marketing Analyst - help companies and organizations decide which products and services to sell, which customers to sell to and at what price, they use various data from marketing conditions, competitor's activities and consumer behavior
Business Analyst - helps maximize a business' effectiveness through data-driven decisions, they help form business insights and give suggestions to business
Research Analyst - responsible for researching, analysing, interpreting and presenting data relate to markets, operations, finance, economics and other information related to the field they work in
Operations Analyst - reviews a company's policies, procedures and functions to help the company make improvements (re-do policies, adjust logistics, streamline operations)
Risk Analyst - a financial specialist who looks the risks associated with investments or new clients to determine whether it is safe to make a financial decisions
Quantitative Analyst
Data Governance Analyst - helps the organization maintain best practices regarding information security, integrity, and access
Data Quality Analyst - monitors the quality of data from which organizations make informed decisions
Optional: Reach out to recruiters or hiring managers
If you want to stand out you can try reaching out recruiters or hiring managers. As a note, this doesn't mean spamming these people and hoping for the best. You want to be personable and do your research beforehand. Don't just ask "do you have a job for me?" or "I need a job". Also, make sure whoever you're contacting actually hires for the roles you're looking for. Don't reach out to a recruiter that only hires salespeople if you're interested in a data job.
There are two approaches for this:
First apply for a job then message the relevant person (usually whoever posted the job on LinkedIn); or
Reach out to recruiters that work for companies I'm interested in and hire for the type of jobs I want (e.g. data analyst).
First Method: Apply then Connect
First apply for a job then message the relevant person.
How do I find the recruiter/hiring manager?
If I apply on LinkedIn first I always check to see if the job posters profile is provided. If it isn't shown or I apply on another site like Indeed I go to the company page on LinkedIn. Then search in the people section on the company page. I look for either the department that's hiring and type in "marketing" and search for the hiring person there. Or I type in "recruiter" and scroll through the people that show up. Before I reach out to the recruiter I make sure they hire for the role I'm interested in. I wouldn't message a recruiter that focuses on administrative if I'm looking for a data job. This information can usually be found somewhere in their profile. If not you can make your best guess here.
Next I connect using a personalized invite. Here's an example of one below:
Hi Jane. I saw you posted about a data analyst position with [company name]. I applied to the position. I would love to work for [company] because [reason]. [Something we have in common or why I’d be a good fit for the job]. I would love to connect and chat more about the position. Thank you – Kelly
Second Method: Connect First
Reach out to recruiters first that work for companies I'm interested in and hire for the type of jobs I want.
How I Find the Recruiter/Hiring Manager
My search is based on recruiters that work for companies I'm interested in and recruit for roles I want. I search in two main ways:
Find the company page on LinkedIn and go to the "people" tab. Then I search for the word "recruiter" or "data manager". From there I look at people's profiles to see if they recruiter for the roles I want. For me it's data/business analyst roles. I wouldn't reach out to a recruiter if they only hired engineers. As a note if the recruiter doesn't look like they're that active on LinkedIn I usually don't bother.
I search the word "recruiter" (or "manager") + data/business analyst. Sometimes recruiters post about jobs they're recruiting for or they mention what roles they recruit for. I take a more general approach with this. I do the same thing and check out the profile. I also tend to focus on recruiters that are more local to my area (California). Unless the recruiter hires for remote roles (be sure to double check this).
I reach out to a recruiter or manager first and see if there's any positions open that I would fit. Below is an example of personalized connection requests I've sent.
Hi Jane, I see that you work for [Company]. I've heard [something about the culture] from one/some of my connections. I wanted to reach out because I'm currently switching careers to become a data analyst. I’d love to find out if I may be a fit for any of your current openings. Thank you! - Kelly
Personalized Invites > Inmail
In my personal experience I've noticed people respond to personalized invites more than inmail credits. So that's why I use this method first. But if you want to see how I write an Inmail message check out the detailed article on this topic: How I Reach Out to Recruiters on LinkedIn.
Interviewing
Once you've applied (hopefully) you'll get a few interviews after this. If you're not landing interviews review your resume, portfolio and reach out to your network for more opportunities. Once you get an interview it's vital to prepare beforehand (which I'll go over). Having a good resume or portfolio alone won't get you the job. The interview is an important part that demonstrates to the employer you're the right fit for the role. Take this seriously and prepare beforehand.
Below I'll be sharing my strategies for interviewing. It has three stages: before an interview, during an interview, and after an interview.
Interview Process Overview
The interview process is different for all companies. When you're on your first interview or a phone screening (initial call where a recruiter or hiring manager wants to get more information about you), ask them what the interview process is like. Below is a common process for data analyst roles (but this is a generalization):
Optional: Screening Call - An initial call where a recruiter or hiring manager gets information about me. Specifically my background, experience and skills in specific tools. They also go over the role.
Hiring Manager Interview - It's a typical behavioural interview, they ask questions like "tell me about yourself" or "tell me about a time when you...". They want to get to know you and your background, and see if you're a good fit for the role.
Take Home Assessment - Next if there is a take home assessment it's typically given after the first interview. You're sent the details via email like when it's due and expectations.
Technical Interview or Reviewing Assessment Interview - This type depends on whether or not you have a technical interview or a take home assessment
Technical Interview - Instead of a take home assessment you have an interview where the interviewer tests your technical abilities (more on this in the [section below]).
Reviewing Assessment Interview - If you did have a take home assessment they may go over it with you during your second interview (assuming you did well enough to move on in the process).
Final Interview - Lastly is the final interview. Most of these I've had with the hiring manager's boss like a director or vice president. This is also another behavioral interview and typically to see if you'll fit in with the company's culture. While I said this is the final interview there may be one or two interviews after this. It all depends on the company.
Advice
Next is actual interview advice.
Before
Below are techniques I use before the interview. Some of these you should do before applying for the job. Like researching the company and looking at the job post. But because the time between applying and interviewing may be long, you should re-research the company.
Research the company - Look at what they've done recently (articles), view their social media profiles, view the website. If I know someone at the company I also like to have a chat with them about their experience working there.
Review the job post - What is the job description, the responsibilities, job title. Also if there is a salary range I review that. Through my own research I've established a salary range with my level of education/experience.
Practice answering common interview questions - I like to use the STAR method for this, especially for common behavioral questions like: "tell me about yourself", "why are you interested in working for this company?", and "what are your greatest strengths?".
Have questions to ask at the end - I have a list of general questions I ask for every company and specific ones based on my research. Here's a list of 57 questions to ask from the Muse.
During
Next is what I do during the interview. There are a few key things I like to remember: stay calm, listen to the interviewer/s, and you're also interviewing them as well (see if this is a company you want to work for).
Take notes - I personally take notes during the interview. I write down the questions they ask and my responses. It helps me recall information in case there's a follow up interview. And it's useful for after the interview, more on that in the "After" section. You can view how I take notes in my LinkedIn Post.
Listen to the interviewer/s - First because it's polite. But second I like to use information the interviewer gives in my answer. For instance if they mention juggling a lot of projects, I can mention how I efficiently manage multiple projects and deadlines at my last job. Also, it's good to know how the company/interviewer answers your questions. Pay attention to how they talk about the company. Remember interviews are a two way street. They are getting to know you while you're also getting to know them.
Build rapport - If you have all/most of the technical skills then another key part of interviews is showcasing your soft skills. Generally speaking, most people hire people they want to work with. Building rapport and getting to know your interviewer helps. One way I practice my conversational skills is having coffee chats.
Emphasize on what you can bring to the company - Remember the company is looking to hire a person to solve a problem. I like to talk about how I can use my skills to help them with their mission or next project. It can be about my technical skills like SQL or about my soft skills like communication.
Take Home Assessments
What if you're given a take-home assessment? A take-home assessment is a project or task a candidate completes for the company. It tests their skills and abilities in a real world scenario.
Below are some tips.
Ask questions. If you don't understand what the assessment is asking contact whoever send you the assessment and get clarification
Stick to the given time frame. The company will give you a rough estimate of how long they expect the assessment to take. Most of mine have been 3-5 hours. If they don't, ask the person who gave you the assessment. While It's important to do the task as well as you can don't spend all your time on this project.
Focus on what the answer. Specifically on next steps with the data and business recommendations. Be sure you're answering the question they're asking and not making things look pretty.
Double check your work. Make sure your analysis is accurate and the results are consistent with the questions you need to answer.
Understand the data. Spend time understanding the data. The key metrics, data types, and what the columns mean. Then you can look for outliers, and identify patterns and trends.
Technical Interview
Now what if you have to go through at technical interview instead of a take home assessment? Well first a technical interview is an interview to assess your technical ability, personality, and problem solving abilities for a role. They will assess skills like SQL, Tableau/Power BI or Excel. Or anything else they feel you need to know for the role.
Types of Questions
Broad - these are about statistics or data analysis concepts
Problem Solving Questions - when an interviewer asks you how you would approach a problem
Conceptual Questions about a skill - testing your knowledge on broad concepts for that specific skill
How to Practice?
Practice Questions on Sites - For specific tools like SQL there are websites that provide practice problems like Hackerrank, DataLemur, or Stratascratch.
Mock Interviews - Ask a friend or a professional to give you a mock interview.
Real Interview Experience - the best way to practice is to actually go through a technical interview. Take notes and if you can ask your interviewer for feedback on your performance/answers.
Three Tips to Help You Succeed
Think out loud - the company/hiring manager wants to learn about how you think and problem solve. You want to explain your thought process behind your answer. If you don't know the answer you can talk through what steps you want to do. Like "I would want to do exploratory analysis using SQL first and then...".
Ask questions - when in doubt, ask questions. If you don't know what the interviewer is looking for, ask. It'll help you understand the question. And show your interviewer you're willing to ask and clarify if you don't understand something, an important skill to have.
Breathe - It's okay to be nervous. If you're having a hard time or your brain isn't working. Take a moment to breathe and center yourself. I like to take a drink of water to give myself a pause to think.
Want more details on this? Check out my detailed article on this topic: How to Prepare for a Data Analyst Technical Interview.
After
After the interview I don't just sit back and wait for a response.
Reflect/review my responses - using my notes I took during the interview I analyze my answers to see if I can improve on my wording or provide a better example.
Keep in contact with your interviewer - while I understand people get busy and things happen, I also like to stay updated on the process. If the interviewer gives me a rough timeline of when they will have a decision I make note of it. Then if I haven't heard anything from them after 2-3 business days past that deadline, I contact them. This is a general guideline and circumstances may vary.
Thank the interviewer for their time - regardless if I got the job (or not) I always like to send a thank you email. It's usually quite short and thanks them for their time and the opportunity to interview with the company.
Continue doing what I was doing- even if I feel like I did well in an interview I don't completely stop the job process to wait to hear back. I continue upskilling, practice my interviewing skills, job searching and networking. Unless you sign an offer it's best to continue job searching.
What happens if you're rejected? If that happens, don't forget to thank your interview via email and offer to stay in touch with them. You never know when another job opportunity will come up. Also take some time, it's okay to feel disappointed with a rejection. But after you've taken time, move on.
Additional Resources
Below are some additional articles I've written about interviews:
Bonus Section
In my journey to becoming a data analyst, I left out one element: content creation. Here's why:
Not Essential: It's not necessary for everyone but can help you stand out in a competitive job market.
Time & Effort: It requires commitment, even after securing a job.
Networking: Many in my network found their jobs through their online presence.
Long-Term Benefits: Don't expect immediate results; it's about building traction over time.
If you want to get started I recommend sharing your journey on social media. Use sites like LinkedIn, Tiktok or any other one. If you’re not sure about what topics to cover see this blog post: 10 LinkedIn Data Analytics Post Ideas (with Examples).
Conclusion
Then you keep applying, networking and improving your skills. Eventually with time, perseverance, and a little bit of luck. You'll get a data analyst job. It takes time, and it's not easy. But it can be done. There's no specific timeline for this. For some people it takes a few months others it takes years. There are a variety of reasons for this and there's no way to know for sure when you'll get a data analyst role. But if it's a serious goal of yours then do it.
If you do all of this and you're still struggling I would recommend taking a look at your job search strategy. Ask others how they became a data analyst, do some research and listen to podcasts or watch Youtube videos, or even hire a career coach.
Hopefully that helped. The important part is to take what you think is helpful and adjust your strategy. This is a guide, not a rulebook on how you can become a data analyst. You don't have to do all of this. Take bits and pieces of what works for you. Good luck! Let me know how it works out for you, feel free to email me at: kelly@kellyjadams.com.