Starting a career in data analytics can open up many exciting opportunities in a variety of industries. With the increasing demand for data-driven decision-making, there is a growing need for professionals who can collect, analyze, and interpret large sets of data. In this post, I will discuss the skills and experience you'll need to start a career in data analytics, as well as tips on learning, certifications, and how to stand out to potential employers.

Starting out, if you have questions beyond what you see in this post, I suggest doing a search in this sub. Questions on how to break into the industry get asked multiple times every day, and chances are the answer you seek will have already come up. Part of being an analyst is searching out the answers you or someone else is seeking.

I will update this post as time goes by and I think of more things to add, or feedback is provided to me.

Originally Posted 1/29/2023

Last Updated 2/25/2023

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Commonly Asked Questions –

Q) Do I need a degree?

A) Its helpful. Many job postings for DA ask for a bachelor’s degree.

Q) Will the Google Data Analyst certificate from Coursera be enough to get me a DA job?

A) No. Not even close. The course teaches you some of the basic technical concepts of the industry and that’s about it. My feeling on the course is this – If you took the entire thing from start to finish and it didn’t scare you away, you may have a chance at this.

More on this certification -- It is marketed really well as being a potential game changer. This cert gets asked about 10x more than any other. Again, it is not a magical key to the industry. It lets you peek in the door to see what you are getting yourself into.

Q) How do I transition from X field I am in today to become a Data Analyst?

A) See the list below for the tips on how to do it.

Q) I already have a degree in X, will that help me?

A) Depends on the position, the recruiter, and the company. Most job postings show that they are looking for a degree in a related field -- Business, Statistics, etc. The more relevant, the better your chances are that it will help. Remember, a degree is just one part of an entire package you should have to help you transition into the field.

Q) What do I need to learn?

A) Excel, SQL, Python, and Power BI or Tableau is a good place to start. I would also learn them in that order. From those, you can start branching out to learn more, such as SSRS, Azure, SAS, Looker, Alteryx, etc.

Q) Do certifications matter?

A) Depends on the person doing the hiring. To some recruiters, it means you have at the minimum a basic knowledge set on the topic. To others, they may see them as useless throwaways that anyone spending 30 minutes on the Internet could get.

Q) Can I get a job right away?

A) Depends on your experience level. If you are trying to break in from another career, its going to be difficult and like any job hunt, you will probably be passed up for those that are experience already. Its going to come down to your knowledge of the field and how well you market yourself. See #11 below.

Q) Is having a degree in X enough to get me a DA role?

A) Probably not. Again, depends on the company and the hiring manager. You are going to improve your chances by adding a great resume and experience to your degree.

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Roadmap to break in to analytics:

1. Build a Strong Foundation in Data Analysis and Visualization: The first step in starting a career in data analytics is to familiarize yourself with the basics of data analysis and visualization. This includes learning SQL for data manipulation and retrieval, Excel for data analysis and visualization, and data visualization tools like Power BI and Tableau. There are many online resources, tutorials, and courses that can help you to learn these skills. Look at Udemy, YouTube, DataCamp to start out with.

2. Get Hands-on Experience: The best way to gain experience in data analytics is to work on data analysis projects. You can do this through internships, volunteer work, or personal projects. This will help you to build a portfolio of work that you can showcase to potential employers. If you can find out how to become more involved with this type of work in your current career, do it.

3. Network with people in the field: Attend data analytics meetups, conferences, and other events to meet people in the field and learn about the latest trends and technologies. LinkedIn and Meetup are excellent places to start. Have a strong LinkedIn page, and build a network of people.

4. Education: Consider pursuing a degree or certification in data analytics or a related field, such as statistics or computer science. This can help to give you a deeper understanding of the field and make you a more attractive candidate to potential employers. There is a debate on whether certifications make any difference. The thing to remember is that they wont negatively impact a resume by putting them on.

5. Learn Machine Learning: Machine learning is becoming an essential skill for data analysts, it helps to extract insights and make predictions from complex data sets, so consider learning the basics of machine learning. Expect to see this become a larger part of the industry over the next few years.

6. Build a Portfolio: Creating a portfolio of your work is a great way to showcase your skills and experience to potential employers. Your portfolio should include examples of data analysis projects you've worked on, as well as any relevant certifications or awards you've earned. Include projects working with SQL, Excel, Python, and a visualization tool such as Power BI or Tableau. There are many YouTube videos out there to help get you started. Hot tip – Once you have created the same projects every other aspiring DA has done, search for new data sets, create new portfolio projects, and get rid of the same COVID, AdventureWorks projects for your own.

7. Create a Resume: Tailor your resume to highlight your skills and experience that are relevant to a data analytics role. Be sure to use numbers to quantify your accomplishments, such as how much time or cost was saved or what percentage of errors were identified and corrected. Emphasize your transferable skills such as problem solving, attention to detail, and communication skills in your resume and cover letter, along with your experience with data analysis and visualization tools. If you struggle at this, hire someone to do it for you. You can find may resume writers on Upwork.

8. Practice: The more you practice, the better you will become. Try to practice as much as possible, and don't be afraid to experiment with different tools and techniques. Practice every day. Don’t forget the skills that you learn.

9. Have the right attitude: Self-doubt, questioning if you are doing the right thing, being unsure, and thinking about staying where you are at will not get you to the goal. Having a positive attitude that you WILL do this is the only way to get there.

10. Applying: LinkedIn is probably the best place to start. Indeed, Monster, and Dice are also good websites to try. Be prepared to not hear back from the majority of companies you apply at. Don’t search for “Data Analyst”. You will limit your results too much. Search for the skills that you have, “SQL Power BI” will return many more results. It just depends on what the company calls the position. Data Scientist, Data Analyst, Data Visualization Specialist, Business Intelligence Manager could all be the same thing. How you sell yourself is going to make all of the difference in the world here.

11. Patience: This is not an overnight change. Its going to take weeks or months at a minimum to get into DA.

Be prepared for an application process like this

100 – Jobs applied to

65 – Ghosted

25 – Rejected

10 – Initial contact with after rejects & ghosting

6 – Ghosted after initial contact

3 – 2nd interview or technical quiz

3 – Low ball offer

1 – Maybe you found something decent after all of that

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Links to help get you on your way:

YouTube:

Alex The Analyst

Luke Barousse

Ken Jee

Tina Huang

Thu Vu

Sundas Khalid

Kenji Explains

Guy in a Cube

Data Tutorials

Tech with Sofia

Shashank Kalanithi

Ali Ahmad

CareerFoundry

Data Set Websites:

Kaggle

Data.gov

Our World in Data

Google Datasets

Opendatasoft

Tableau

Maven Analytics

UCI Machine Learning Repository

Learning Websites:

YouTube

Udemy

Coursera

Data Camp

Code Academy

Leet Code

Stratascratch

Always enjoyed playing around with personal data analysis, and this has been a labour of love for some years now that I dip in and out of, so giving a bit of an update to progress, since the original.

For background, this is built upon the available data export functions from WhatsApp and iMessage (local SQLite database). There are no calls to LLMs or AI involved, and no cloud/servers, it runs offline with everything done locally on-device using standard data analysis techniques.

I've gotten the majority of dashboard design inspiration from the many many hours of playing football manager!

Key components to the analysis:

• Custom built weighted points scoring mechanism for messages which factors in message length, content (laughs, apologies, questions, images, videos etc), speed of response, whether it started a new conversation as well as a series of other factors in order to produce a "contribution balance" assessment. 

• Each conversation is rated based on the total score, giving a quantitative view of how balanced, rich, or responsive it was. 

• I use a heuristic tagging system to detect key language traits - like questions, apologies, laughter etc - using lightweight rules instead of heavier NLP models - tried these early on and the results weren't that much better than plain heuristics.

• I’ve avoided sentiment analysis so far, as standard on-device NLP models didn’t perform well. Also tried experimenting with small on-device LLMs for richer insight - but they are also unreliable and too slow frankly, so am now working on some custom models that can leverage the on-device machine learning model frameworks.

For those that want to try, it's available publicly as a free tool called Mimoto on the app store, supporting data from both WhatsApp (on iOS) and iMessage (on macOS).

Appreciate the community's feedback, and happy to answer any questions, or listen to ideas anyone has about the project. Cheers!

This is an updated deep-dive into my relationship with my wife, based on 12+ years of WhatsApp messages-from when we first met to today.

I built a tool called Mimoto to analyze everything locally and privately, now supporting both WhatsApp (iOS) and iMessage (macOS)

It’s a passion project, and a bit of an over-the-top experiment in relationship analytics.

Key components:

• I created a points scoring mechanism for messages which factors in message length, content (laughs, apologies, questions, images, videos etc), speed of response, whether it started a new conversation as well as a series of other factors in order to produce a "contribution balance" assessment.

• Each conversation can be rated based on the total score, giving a quantitative view of how balanced, rich, or responsive it was.

• I use a custom heuristic tagging system to detect key language traits - like questions, apologies, laughter - using lightweight rules instead of heavier NLP models.

• All analysis happens fully on-device, with no cloud processing or storage. Privacy-first by design

• I’ve avoided sentiment analysis so far, as standard on-device models didn’t perform well. But I’m now experimenting with small on-device LLMs for richer insight.

Long-term aspiration is to help people derive value from their vast chat histories by using it to build a contextually rich digital avatar from the data.

I got loads of great feedback when I first posted about this project a couple of years ago, would love to hear what this community thinks of the latest version.

Hey Folks,

I added 400 inteview questions to Data Analyst section.. Google, Amazon, Microsoft, Apple, Palantir, DoorDash, Databricks, Snowflake, Dropbox, Adobe, Netflix, Accenture any many more.

It took us around 5 months and a lot of hard work to clean, categorize, and edit all of those questions. just Please don't abuse the service to avoid limits e.g. using multiple account

Posting here: https://prepare.sh/interviews/data-analysis

If you are curious there is also information on the website about how we get and process those question.

You spend months learning SQL, building dashboards, polishing your resume, and applying to hundreds of roles.

Then you land the job and it’s nothing like you expected.

This is exactly what happened to me with my first data job 3 years ago.

On day one, the onboarding doc is a broken PDF from 2021.
Your manager says they’ll get your access “soon.”
Two weeks go by and you're still locked out of half the tools.

You’re digging through SQL written by someone who left three years ago.

And you're asked to report on metrics that don’t even seem to be defined anywhere.

No one prepares you for this part.

Just wanted to share for those still trying to break in. It’s normal to feel lost at first.

What helped me was connecting with others who had already been through it, asking questions (even when they felt dumb), and slowly realizing that being overwhelmed doesn’t mean you’re behind.

If you're feeling stuck or disoriented in your first role, you're not alone. Keep learning. Keep building. It does get better.

I also hang out in a growing data community where we support each other through this stuff. Happy to DM if you’re looking for people to talk to about it.

Hey all, recently I was laid off from my sales job. I was not having fun in it and knew ever since I graduated college I wanted to become a data analyst but needed to work on my communication skills and other skills. Today I received news that I would be receiving a verbal offer for a Junior Data Analysis position for 60k. My question to everyone is should I go for more money, how should I undone the talks with HR ?

Actually i got it a few weeks ago but i think with all the pessimism here it's a good idea to post it now.if i can get a job with 0 years of experience and just a bachelor degree you can too!

Hey everyone!

I’ve been working on a game called DataMart, where you manage a corner shop and everything runs on real SQL. You build it all yourself: the databases, schemas, tables, relationships and queries, your way, on a real PostgreSQL server that runs inside the game.

Customers will ask you things like “Where can I find the apples?” or other questions about the store. Once a week an auditor comes by to check you’re tracking everything properly, and asks you a few questions you’ll answer with SQL.

There are also mysteries and stories involving the people in town, and of course you unravel those with SQL too.

The game is in alpha and free at https://rodrigoavf.itch.io/datamart

I’d love to hear your thoughts!

Ps. Yes, the game is being created with the help of AI, and what is not now days?

I just started my first job as a business data analyst a month-and-a-half ago and holy crap, I never thought it’d be this depressing. My boss catches me on every minor mistake I make in emails. I feel like I can’t communicate with people without her breathing down my neck. I feel like everything I do is a mistake, so I triple check everything I work on and then worry I’m going too slow and not getting enough done to meet deadlines. I just failed to meet a deadline for the first time today. I feel like I’ve been so disorganized and like I might not be cut-out for this unforgiving work.

Is this normal? Will it get better? Will I get better if I keep at it? What can I do to keep my tasks organized? Please help!

I spent 8 months of my life trying and failing to become a data analyst, and have finally given up on it. After getting laid off in November of last year I decided to make a career change and become a data analyst. I had no experience and knew I’d have to learn a lot of skills to get my foot in the door. I had a good amount of savings so decided to study full time, enrolled in a Data Analytics bootcamp and spent all my time outside of class reinforcing what I’d learned. I got really good with Python, excel (VBA scripting), SQL and tableau and had a good understanding of the data analysis process after around 6-7 months of studying full time. I graduated the course and continued doing personal projects and started applying for jobs, which I was optimistic about at first, but quickly realized literally nobody wants to hire an entry level Data Analyst right now. I applied for jobs all day every day for almost 3 months, sent out 312 tailored resumes with my skills and projects from my GitHub and got a couple interviews but nothing came of them. After 3 months of sending applications into the void I finally gave up and last week just accepted a job offer doing what I was doing before I got laid off, as I had plenty of experience in that on my resume. It really sucks to have spent so much time and effort learning these things that I will probably never use now, but I’m really not sure what I could do differently. I have the skills but just never got a chance to prove it, just couldn’t get my foot in the door. I guess this is a warning that data analysis is really over saturated right now, so if you are thinking of a career change and have no experience or connections in data analysis I just want to warn you of my experience.

Just sharing a quieck view of my daily work — I build reports, dashboards, and dig into data to help teams make better decisions.

If you're curious about the tools I use, what the job is like, or how to get into this field, feel free to ask. I'm also trying to understand what people are most interested in when it comes to data work.

I'm curious to know what aspects of a candidate's resume make you excited to interview them for a data analyst role.

• Projects showcasing the use of multiple DA tools?

• Seeing candidate's genuine interest in data?

• STEM degree

• Graduating from a high-ranked school

• Do personal projects involving machine learning stand out?

Any insights or specific examples would be greatly appreciated!

Hello! I'm Daalma, and I love Pokémon. As a Data Scientist, I've been working on this project in my spare time. It's something I hope reflects my love for the series and that others as passionate as I am will find interesting or appealing.

This is a complete Data Science project with three main objectives:

1: Generation of a dataset using web scraping containing information about all Pokémon (up to Generation IX), including variants and forms.

2: Preprocessing the dataset, extracting basic information, and creating informative visualizations.

3: Applying Machine Learning and AI techniques to generate higher-level insights and visualizations.

You can check out the project here: https://github.com/Daalma7/PokemonDataScience

The results of the project have been quite good, and while I reserve the right to have made mistakes, I must say I’m really pleased with the graphics and outcomes. If anyone wants to take a look and share their thoughts, I would be very grateful. Below are some images showing a sample of what I've done.

Thank you so much for reading!

Daalma

Been working with SQL for a while now and these are the patterns that genuinely made a difference once I learned them:

1. Use CTE (WITH clause) instead of nested subqueries — your queries become readable and you can reuse the result set multiple times in the same query without recalculating.

2. ROW_NUMBER() for deduplication — instead of clunky GROUP BY hacks, use ROW_NUMBER() OVER (PARTITION BY id ORDER BY updated_at DESC) and filter WHERE rn = 1 to keep only the latest record per group.

3. CASE WHEN inside aggregates — you can do conditional aggregations like SUM(CASE WHEN status = 'sold' THEN revenue ELSE 0 END) without a WHERE clause, which means you get multiple breakdowns in a single pass.

4. NULLIF to avoid division by zero — wrap your denominator: revenue / NULLIF(units, 0). Returns NULL instead of crashing.

5. DATE_TRUNC for time-based grouping — instead of converting dates manually, DATE_TRUNC('month', order_date) groups everything cleanly by month/quarter/year.

Hope this helps someone who's in the early stages. Took me longer than I'd like to admit to discover some of these.