As an aspiring data scientist, it is interesting for me to know how someone became one.

I am really interested in becoming a data scientist in 2025, but honestly, I am a bit confused by all the info out there. There are so many skills mentioned like Python, SQL, machine learning, stats, deep learning, cloud, data engineering and now AI and tons of courses, bootcamps, and certifications.

I am not sure where to start or what’s really important nowadays. Also, how much do I need to focus on projects or competitions like Kaggle? I searched some online courses for data science like DataCamp, Coursera ML, LogicMojo Data Science, Simplilearn, Alma etc, Suggest which is good?

If you are already working as a data scientist or recently made the switch, could you share how you did it? What worked best for you

If you read posts/comments from this sub, people are quite assertive that their path is the main path. They say get a masters/PhD in “ “ and that’s the only way in. They say don’t get a masters in analytics/data science but encourage the analytics @ Georgia Tech. Worry about your own goals and domains that you want to get into and focus YOUR path for that. There’s data scientist from a million different backgrounds and educational levels.

Background

I’m a self-taught data scientist, with about 5 years of data analyst experience and now about 5 years as a Data Scientist. I’m more math minded than the average person, but I’m not special. I have a bachelor’s degree in mechanical engineering, and have worked alongside 6 data scientists, 4 of which have PHDs and the other 2 have a masters. Despite being probably, the 6th out of 7 in natural ability, I have been the 2nd most productive data scientist out of the group.

Gatekeeping

Every day someone on this subreddit asks some derivative of “what do I need to know to get started in ML/DS?” The answers are always smug and give some insane list of courses and topics one must master. As someone who’s been on both sides, this is attitude extremely annoying and rampart in the industry. I don’t think you can be bad at math and have no pre-requisite knowledge, and be successful, but the levels needed are greatly exaggerated. Most of the people telling you these things are just posturing due to insecurity.

As a mechanical engineering student, I had at least 3 calculus courses, a linear algebra course, and a probability course, but it was 10+ years before I attempted to become a DS, and I didn’t remember much at all. This sub, and others like it, made me think I had to be an expert in all these topics and many more to even think about trying to become a data scientist.

When I started my journey, I would take coding, calculus, stats, linear algebra, etc. courses. I’d take a course, do OK in it, and move onto the next thing. However, eventually I’d get defeated because I realized I couldn’t remember much from the courses I took 3 months prior. It just felt like too much information for me to hold at a single time while working a full-time job. I never got started on actually solving problems because the internet and industry told me I needed to be an expert in all these things.

What you actually need

The reality is, 95% of the time you only need a basic understanding of these topics. Projects often require a deeper dive into something else, but that's a case by case basis, and you figure that out as you go.

For calculus, you don't need to know how to integrate multivariable functions by hand. You need to know that derivatives create a function that represents the slope of the original function, and that where the derivative = 0 is a local min/max. You need to know integrals are area under the curve.

For stats, you need to understand what a p value represents. You don't need to know all the different tests, and when to use them. You need to know that they exist and why you need them. When it's time to use one, just google it, and figure out which one best suits your use case.

For linear algebra, you don't need to know how to solve for eigenvectors by hand, or whatever other specific things you do in that class. You need to know how to ‘read’ it. It is also helpful to know properties of linear algebra. Like the cross product of 2 vectors yields a vector perpendicular to both.

For probability, you need to understand basic things, but again, just google your specific problem.

You don't need to be an expert software dev. You need to write ok code, and be able to use chatGPT to help you improve it little by little.

You don't need to know how to build all the algorithms by hand. A general understanding of how they work is enough in 95% of cases.

Of all of those things, the only thing you absolutely NEED to get started is basic coding ability.

By far the number one technical ability needed to 'master' is understanding how to "frame" your problem, and how to test and evaluate and interpret performance. If you can ensure that you're accurately framing the problem and evaluating the model or alogithm, with metrics that correctly align with the use case, that's enough to start providing some real value. I often see people asking things like "should I do this feature engineering technique for this problem?" or “which of these algorithms will perform best?”. The answer should usually be, "I don't know, try it, measure it, and see". Understanding how the algorithms work can give you clues into what you should try, but at the end of the day, you should just try it and see.

Despite the posturing in the industry, very few people are actually experts in all these domains. Some people are better at talking the talk than others, but at the end of the day, you WILL have to constantly research and learn on a project by project basis. That’s what makes it fun and interesting. As you gain PRACTICAL experience, you will grow, you will learn, you will improve beyond what you could've ever imagined. Just get the basics down and get started, don't spin your wheels trying and failing to nail all these disciplines before ever applying anything.

The reason I’m near the top in productivity while being near the bottom in natural and technical ability is my 5 years of experience as a data analyst at my company. During this time, I got really good at exploring my companies’ data. When you are stumped on problem, intelligently visualizing the data often reveals the solution. I’ve also had the luxury of analyzing our data from all different perspectives. I’d have assignments from marketing, product, tech support, customer service, software, firmware, and other technical teams. I understand the complete company better than the other data scientists. I’m also just aware of more ‘tips and tricks’ than anyone else.

Good domain knowledge and data exploration skills with average technical skills will outperform good technical skills with average domain knowledge and data exploration almost every time.

Advice for those self taught

I’ve been on the hiring side of things a few times now, and the market is certainly difficult. I think it would be very difficult for someone to online course and side project themselves directly into a DS job. The side project would have to be EXTREMELY impressive to be considered. However, I think my path is repeatable.

I taught myself basic SQL and Tableau and completed a few side projects. I accepted a job as a data analyst, in a medium sized (100-200 total employees) on a team where DS and DA shared the same boss. The barrier to DA is likely higher than it was ~10 years ago, but it's definitely something achievable. My advice would be to find roles that you have some sort of unique experience with, and tailor your resume to that connection. No connection is too small. For example, my DA role required working with a lot of accelerometer data. In my previous job as a test engineer, I sometimes helped set up accelerometers to record data from the tests. This experience barely helped me at all when actually on the job, but it helped my resume actually get looked at. For entry level jobs employers are looking for ANY connection, because most entry level resumes all look the same.

The first year or two I excelled at my role as a DA. I made my boss aware that I wanted to become a DS eventually. He started to make me a small part of some DS projects, running queries, building dashboards to track performance and things like that. I was also a part of some of the meetings, so I got some insight into how certain problems were approached.

My boss made me aware that I would need to teach myself to code and machine learning. My role in the data science projects grew over time, but I was ultimately blocked from becoming a DS because I kept trying and failing to learn to code and the 25 areas of expertise reddit tells you that you need by taking MOOCs.

Eventually, I paid up for DataQuest. I naively thought the course would teach me everything I needed to know. While you will not be proficient in anything DS upon completing, the interactive format made it easy to jump into 30-60 minutes of structured coding every day. Like a real language consistency is vital.

Once I got to the point where I could do some basic coding, I began my own side project. THIS IS THE MOST IMPORTANT THING. ONCE YOU GET THE BASELINE KNOWLEDGE, JUST GET STARTED WORKING ON THINGS. This is where the real learning began. You'll screw things up, and that's ok. Titanic problem is fine for day 1, but you really need a project of your own. I picked a project that I was interested in and had a function that I would personally use (I'm on V3 of this project and it's grown to a level that I never could've dreamed of at the time). This was crucial in ensuring that I stuck with the project, and had real investment in doing it correctly. When I didn’t know how to do something in the project, I would research it and figure it out. This is how it works in the real world.

After 3 months of Dataquest and another 3 of a project (along with 4 years of being a data analyst) I convinced my boss to assign me DS project. I worked alongside another data scientist, but I owned the project, and they were mostly there for guidance, and coded some of the more complex things. I excelled at that project, and was promoted to data scientist, and began getting projects of my own, with less and less oversight. We have a very collaborative work environment, and the data scientists are truly out to help each other. We present our progress to each other often which allows us all to learn and improve. I have been promoted twice since I began DS work.

I'd like to add that you can almost certainly do all this in less time than it took me. I wasted a lot of time spinning my wheels. ChatGPT is also a great resource that could also increase your learning speed. Don't blindly use it, but it's a great resource.

Tldr: Sir this is Wendy’s.

Edit: I’m not saying to never go deeper into things, I’m literally always learning. I go deeper into things all the time. Often in very niche domains, but you don't need to be a master in all things get started or even excel. Be able to understand generalities of those domains, and dig deeper when the problem calls for it. Learning a concept when you have a direct application is much more likely to stick.

I thought it went without saying, but I’m not saying those things I listed are literally the only things you need to know about those topics, I was just giving examples of where relatively simple concepts were way more important than specifics.

Edit #2: I'm not saying schooling is bad. Yes obviously having a masters and/or PhD is better than not. I'm directing this to those who are working a full time job who want to break into the field, but taking years getting a masters while working full time and going another 50K into debt is unrealistic

HOW DO I GET A JOB IN DATA SCIENCE?

Hey you. Yes you, person asking "how do I get a job in data science/analytics/MLE/AI whatever BS job with data in the title?". I got news for you. There are two simple rules to getting one of these jobs.

1. Have experience.

2. Don't have no experience.

There are approximately 1000 entry level candidates who think they're qualified because they did a 24 week bootcamp for every entry level job. I don't need to be a statistician to tell you your odds of landing one of these aren't great.

HOW DO I GET EXPERIENCE?

Are you currently employed? If not, get a job. If you are, figure out a way to apply data science in your job, then put it on your resume. Mega bonus points here if you can figure out a way to attribute a dollar value to your contribution. Talk to your supervisor about career aspirations at year-end/mid-year reviews. Maybe you'll find a way to transfer to a role internally and skip the whole resume ignoring phase. Alternatively, network. Be friends with people who are in the roles you want to be in, maybe they'll help you find a job at their company.

WHY AM I NOT GETTING INTERVIEWS?

IDK. Maybe you don't have the required experience. Maybe there are 500+ other people applying for the same position. Maybe your resume stinks. If you're getting 1/20 response rate, you're doing great. Quit whining.

IS XYZ DEGREE GOOD FOR DATA SCIENCE?

Does your degree involve some sort of non-remedial math higher than college algebra? Does your degree involve taking any sort of programming classes? If yes, congratulations, your degree will pass most base requirements for data science. Is it the best? Probably not, unless you're CS or some really heavy math degree where half your classes are taught in Greek letters. Don't come at me with those art history and underwater basket weaving degrees unless you have multiple years experience doing something else.

SHOULD I DO XYZ BOOTCAMP/MICROMASTERS?

Do you have experience? No? This ain't gonna help you as much as you think it might. Are you experienced and want to learn more about how data science works? This could be helpful.

SHOULD I DO XYZ MASTER'S IN DATA SCIENCE PROGRAM?

Congratulations, doing a Master's is usually a good idea and will help make you more competitive as a candidate. Should you shell out 100K for one when you can pay 10K for one online? Probably not. In all likelihood, you're not gonna get $90K in marginal benefit from the more expensive program. Pick a known school (probably avoid really obscure schools, the name does count for a little) and you'll be fine. Big bonus here if you can sucker your employer into paying for it.

WILL XYZ CERTIFICATE HELP MY RESUME?

Does your certificate say "AWS" or "AZURE" on it? If not, no.

DO I NEED TO KNOW XYZ MATH TOPIC?

Yes. Stop asking. Probably learn probability, be familiar with linear algebra, and understand what the hell a partial derivative is. Learn how to test hypotheses. Ultimately you need to know what the heck is going on math-wise in your predictions otherwise the company is going to go bankrupt and it will be all your fault.

WHAT IF I'M BAD AT MATH?

Git gud. Do some studying or something. MIT opencourseware has a bunch of free recorded math classes. If you want to learn some Linear Algebra, Gilbert Strang is your guy.

WHAT PROGRAMMING LANGUAGES SHOULD I LEARN?

STOP ASKING THIS QUESTION. I CAN GOOGLE "HOW TO BE A DATA SCIENTIST" AND EVERY SINGLE GARBAGE TDS ARTICLE WILL TELL YOU SQL AND PYTHON/R. YOU'RE LUCKY YOU DON'T HAVE TO DEAL WITH THE JOY OF SEGMENTATION FAULTS TO RUN A SIMPLE LINEAR REGRESSION.

SHOULD I LEARN PYTHON OR R?

Both. Python is more widely used and tends to be more general purpose than R. R is better at statistics and data analysis, but is a bit more niche. Take your pick to start, but ultimately you're gonna want to learn both you slacker.

SHOULD I MAKE A PORTFOLIO?

Yes. And don't put some BS housing price regression, iris classification, or titanic survival project on it either. Next question.

WHAT SHOULD I DO AS A PROJECT?

IDK what are you interested in? If you say twitter sentiment stock market prediction go sit in the corner and think about what you just said. Every half brained first year student who can pip install sklearn and do model.fit() has tried unsuccessfully to predict the stock market. The efficient market hypothesis is a thing for a reason. There are literally millions of other free datasets out there you have one of the most powerful search engines at your fingertips to go find them. Pick something you're interested in, find some data, and analyze it.

DO I NEED TO BE GOOD WITH PEOPLE? (courtesy of u/bikeskata)

Yes! First, when you're applying, no one wants to work with a weirdo. You should be able to have a basic conversation with people, and they shouldn't come away from it thinking you'll follow them home and wear their skin as a suit. Once you get a job, you'll be interacting with colleagues, and you'll need them to care about your analysis. Presumably, there are non-technical people making decisions you'll need to bring in as well. If you can't explain to a moderately intelligent person why they should care about the thing that took you 3 days (and cost $$$ in cloud computing costs), you probably won't have your position for long. You don't need to be the life of the party, but you should be pleasant to be around.

WHAT IF I HAVE OTHER QUESTIONS?

READ THE GD /R/DATASCIENCE SUB WIKI. IT'S THERE FOR A REASON AND HAS GOOD INFORMATION.

And if you're posting these questions on r/datascience, please for the love of all that is good in this world, use the weekly thread. Your post is gonna get nuked by the mods and no one is going to see it and you're going to die alone.

How many years were you working as a data analyst prior to becoming a data scientist? Did you have a master's degree?

TL;DR: learned a buncha shit in 20 months with no prior anything-related experience, got job as data scientist

Edit: Seems like this was removed from r/learnprogramming. Trying to direct all the PMs to come here

First, I want to thank the entire reddit community because without this place I wouldn’t have went down the rabbit hole that is self-learning, job searching, and negotiation.

Second, just to list out my background so people know where I started and how I got here: I graduated in 2013 with a bachelor’s in civil engineering (useless in this case) and again in 2015 with a master’s in operations research (much more useful, namewise at least) both from the same top school. The name of the school and the operations research degree opened up quite a few doors in the beginning of my (2-year) career, and definitely was a factor in getting an interview, but had nothing to do directly with what was needed for the Data Science job. This is because that offer was contingent on a programming skillset and specific data science problem-solving abilities, of which I had none right after graduation.

The most useful advice to keep in mind: keep trying, keep learning, don’t be afraid to switch jobs when you’re bored or it’s not what you want, continuously look for new opportunities, and always negotiate. I went from a 47k job where I lasted only 4 months, to a 65k job where I lasted just under a year, to a 90k job where I stayed 10 months, to my new job at 115k. All in under 2 and a half years. Strap yourself in, this will be long!

Step 1:

Get your first real job out of college, realize how much you loathe it, feel entitled because they’re not paying you for your amazing theoretical prowess that isn’t really useful, realize that you were meant to do much more cool shit, and convince yourself that you need a higher paying job.

My first job out of grad school lasted 4 months. It was an analyst title, which I thought was awesome because I had no idea what analysts do, but it was mostly bitchwork and data entry. The one upside was that my boss mentioned a pivot table once, and I googled it, so I finally learned what it was. But I still figured I was too smart for this shit so I looked for other jobs because I needed something to challenge me.

Congrats, you now have the drive to get your ass to a better role!

Step 2:

I got into the adtech industry after my 4-month stint, they liked me because of that pivot table thing I learned to do /s. This is where the data science itch began, but I knew I wouldn’t be satisfied in the long run. As pompous as it is to keep saying I was too smart for this shit, I was. I just needed the tools to show that.

The amount of data that lives in the industry is insane, and it’s always good to mention how much data you’ve worked with. This place is where you earn your SQL, Excel, and Tableau medals. You edit some dashboards, you pivot and slice data, you don’t necessarily write your own complex queries from scratch but you know how they look like and know what joins do.

By no means was I going to do any advanced stuff at work so I needed to start doing it on my own if I wanted to grow. In my time at this job (after work but also during work. Use your down time wisely!), I took MIT’s Intro to Comp Sci with Python, Edx’s Analytics Edge, and Andrew Ng’s Machine Learning. This set up the foundation but since they were all intro courses, I couldn’t apply the knowledge. There were still a bunch of missing pieces.

But! At least I got started. Towards the end of my time there I found rmotr.com through reddit. I finished the advanced python programming course, which was incredibly difficult for me at the time because of the knowledge density and intensity. I highly recommend it if you want to learn more advanced python methodologies and applications, and also if you’re leaning towards the development side.

Step 3:

I left my last company of a few thousand people, where everything was essentially fully established, and moved to a smaller company of 100ish people. There was more opportunity to build and own projects here, and it’s where I earned my dev, analytics, and machine learning medals. This is where classes will continue to aid in your learning, but where google and stackoverflow will help you actually BUILD cool shit. You will have thousands of questions the classes won’t be able to answer, so your searching skills will greatly improve in this time.

During my time here I completed Coursera UMichigan’s Intro to Data Science with Python. I completed it relatively quickly and from what I recall, it wasn’t too challenging.

After that course, I stumbled on Udemy and completed Jose Portilla’s Python for Data Science and Machine Learning bootcamp, which was a turning point from knowledge to application. This class is a must. It’s how I learned to neatly organize my data frames, manipulate them very easily, and, thanks to google and stackoverflow, how to get all that data into csv and excel sheets so I can send them to people. It doesn’t sound like much, but data organization and manipulation was the #1 worthwhile skill I learned. It’s also where I learned to implement all machine learning algorithms using scikit-learn, and a bit of deep learning. There wasn’t much theory behind it, which was perfectly fine, because I was going for 100% application.

This is also where I took advantage of the training reimbursement at work- I kept buying courses and it was free! During this time I also completed Stanford’s Statistical Learning course on their Lagunita platform (good for knowledge base), the first three courses of Andrew Ng’s Deep Learning Specialization on Coursera (it was a breeze because it was in python and I had a deep understanding of dataframes by this time, also very good for knowledge base and algorithm implementation from scratch), and another Udemy class from Jose Salvatierra called the Complete PostgreSQL and Python Developer Course- also a game changer. It was the first course I had on clean python code for software development. The way he thinks is outstanding and I highly recommend it.

Step 4: Resume Building and Linkedin

There are articles out there that can explain this a lot better than I can, but here were my steps to have my resume and Linkedin Ready:

Resume

1. Kept the resume to one page, had it look more modern, sleek, and fresh (even had dark grey and blue colors)

2. Under my name, listed my email, number, github, and linkedin across the entire width of the page

3. Recent work experience on top. Descriptions included what technology I used (python, impala, etc.) to do something (built multiple scrapers, python notebooks, automated reporting, etc.) and the effect (saved hours of manual work for account managers, increased revenue day over day by X, etc). This can be easily remembered by saying I used X to do Y with the Z results.

Note: Not all of my descriptions had results. My last listed job on my resume only had the support work I did- I supported accounts totaling X revenue monthly, partook in meetings with clients, etc. Not every task has a quantifiable outcome but it’s nice to throw some numbers in there when you can.

4. I read in some places that no one would care about this, but I did it anyway, and listed all courses and bootcamps I had finished by that time, which was around 8. While I had some projects I had done at work I could speak to, I wanted them to know that I was really dedicated to learning everything I could about the field. And it worked!

5. Below that was my education- both degrees listed without GPAs

6. And lastly, active interests. Maybe old-school corporations don’t care for things like this, but for start-uppy tech companies that are in a growth stage, I figured they’d like to see my what I do on the side. I’ve been competitively dancing for almost a decade and weightlifting for more than that, so if being a dancing weightlifting engineering-background guy makes me seem more unique, I’m going for it. Whatever makes you stick out!

Linkedin

1. Professional-looking photo. Doesn’t have to be professional, just professional-looking.

2. Fill out everything LinkedIn asks you to fill out so you can be an all-star and appear in more searches. The summary should include a shitload of keywords that relate to what you’ve done and what you want to do. Automation, analytics, machine learning, python, SQL, noSQL, MS-SQL, throw all that shit in there.

3. I only filled out the description for my most recent job because that’s where I actually did cool shit. I put a lot more detail here in LinkedIn than I did on my resume. Then I listed the 3-4 jobs I had before that, no description

4. Put all my certifications from the courses I took with links

5. Put my education, obvs

6. The rest…eh. Doesn’t really matter.

Step 5: Job Search

So you have your nice and shiny resume ready, and your LinkedIn set to go. This is where the entirety of your hard work will be rewarded. How badly do you want this job?

I stopped using indeed, monster, etc. a long while ago.

The single tool I used was and still is Glassdoor. Download a PDF copy of your resume to your phone or a cloud drive, search on Glassdoor ON THE DAILY. Keep saved searches ready to go- “junior data scientist”, “data scientist”, “senior analytics”, “senior data analyst”, “junior machine learning”, “entry data science”, and so on. When you’re on the bus or laundromat or in bed late at night and can’t sleep, look for openings. Filter by the rating you’re willing to take on and apply like mad. I got dozens of applications done just from waiting at the laundromat. All the calls I had after were 100% from Glassdoor applications.

Step 6: The initial call

I’ve had 3 total initial calls from the probably 50 or so applications I sent over the summer (very few openings that didn’t require 5+ years of java and machine learning product dev etc. etc. and largely distributed blah blah where I live).

Here were most of the things I was asked:

• What tools I used at work

• How have I made processes more efficient at work

• Anything I’ve automated

• Largest amount of data I worked with and what was the project and result

• Why the shift from the current job

• How much I know about their company and how I’d describe the company so someone else (do your research!)

I had 100% success on my initial calls. Each time mentioned some sort of python, automated scripts (simply by using windows task scheduler and batch file- thanks to google search!), and a data manipulation project (highest I’ve had is a few million rows), and I was good to go.

Step 7: The data exercise

From those 3 initial calls, I had 2 exercises sent via email and one via Codility.

The first exercise was SQL and visualization heavy. I was given a SQLite database to work from and had to alter tables to feed into other tables to aggregate other metrics and so on. Once that was done, I had to use the resulting tables to do some visualizations and inference.

Did I know how to do most of what they asked? Hell no. I had google and stackoverflow open for every little detail I didn’t know how to do off the top of my head. The entire thing took about 20-25 hours spread across the week and even when I submitted it didn’t feel complete. I couldn’t afford not to put all my free time into this exercise.

The end result: the hiring manager and team was impressed with the code, but they didn’t vibe with the presentation style of my jupyter notebook and it was very apparent that I lacked the domain knowledge required (this was for a health tech company, and I have no health anything experience). It actually prompted them to re-post with an altered job description requiring domain knowledge. Woo? Regardless, this served as a huge source of validation for me- these senior level members thought my code was good.

The second exercise was from the company I ultimately accepted. It was 3-4 hours in total to assess business intelligence skills (SQL and visualization). They liked it and I moved on to the in-person, which I’ll go into in the next step.

The last exercise was codility- and while my code “worked”, there was likely some test cases I didn’t account for. Either that or the company got irritated when I said I received an offer and if they could speed up the process. They didn’t follow through.

Step 8: The in-person interview

So you got to this stage! Congrats!

And you’ll be interviewing with 3 VPs, 2 C-level execs, and 2 data scientists. Jesus fuck, you’ve never met this many executives in your whole life.

No need to freak out. This simply validates your hard work. You’ll be meeting with very important people for a very important job, and they think you might be good at it.

Even if I hadn’t made it past this, I tasted victory.

I did something that may not be recommended by most people: I didn’t prepare for questions they’d ask me, but rather prepared for all the questions I’d ask them. This did two things: I didn’t obsess about what they’d ask me so I was relaxed, and it gave me a lot of chances to show I knew my shit when I asked them a bunch of stuff. Besides, for a data science job, I figured they’d ask questions about how I’d solve some problems they currently have, as opposed to some common questions. And that’s exactly what they did. Not something you can really prepare for the night before, since it’s a way of thinking you’d have to grasp through all the classes and projects and problems you solved at your current job.

IMPORTANT NOTE: I am not advocating ignoring prepping for questions. I did about 30-35 interviews, phone and in person, before my current job so I had a lot of learning experience. I already had a more natural-feeling response for most questions. And if you really were into your projects at your current job, you’ll know what you did inside out, so it’s easier to talk about it on the spot. But by all means, if you don’t have much interview experience, prepare and practice!

Here are my notes from after the interviews, including what was asked and how I answered, and what I asked:

VP of Data Science

• Notice any hiccup in your exercise? I debated with him on the accuracy of a single statement in the exercise, assuring him that since I used a Hadoop-based query engine and they used AWS, my method worked every time I used it. I never checked whether he or I was right because afterwards I started thinking he was right and didn’t want to feel like an idiot. But we moved on rather quickly.

• How would you implement typo detection? I gave a convoluted response but put simply, some distance index between words. As in, how many changes would it take to get to the word we may want. He liked the answer because it’s what he was thinking too.

• How’s your style of explaining things to people? Very logical step-by-step process with the goal of weaning people off needing me. I’d explain it to them completely, then next time leave a few steps missing and ask if they’d remember, then eventually just give them a step or two.

• What’s something you want to be better at? Being more personable when explaining technical terms to non-tech people

Then I went crazy with a ton of questions about what projects they’re working on, what’s the first thing I’d be working on, the challenges they have currently, how do they interact with the sales team, and so on.

VP Tech

• So, data! Tell me about it. I told him that I love it, I’m excited by it, and I wana get better at it.

• What as a process you made more efficient at work. Created an automated process using a batch file to run python script via task scheduler. It scrapes an internal web tool and creates reporting that otherwise doesn’t exist, which saves hours for the account managers weekly.

• So you aimed towards a process that would essentially take something that’s not working too well, fix it, and productionalize it? Why yes, yes indeed.

• So that kind of sounds like a software development mentality. Absolutely, and eventually after I have a lot of exposure to the research side of data science I’d like to get more into a machine learning engineering role to build everything out.

• Cool man!

He probably liked that I wasn’t purely analytics, but also built tools to solve problems not related to data science.

COO, President

• What are areas do you think you need development in? Being more on the business side of things, as I tend to like delving deep into my code to make things work I sometimes get delayed info of the overall business health.

• Do you have any entrepreneurial experience? I said nope, to which he responded with “Nothing? Not even selling lemonade?”. Then it jogged my memory of when I tried to sell yugioh and pokemon cards at the pool when I was young, with my binder of sheets with prices too high so no one would buy. He had a laugh and said it was a good answer because the simple experience in learning the prices were too high was a lesson.

• What are you looking for? Something challenging, where I won’t be just a SQL monkey (this term was thrown around by a lot of the team, so I kept repeating it and made references to who mentioned it to show that I’m paying attention), where there will be big issues to solve across the company, and a place where I’d be doing something meaningful. In this case, it was helping local businesses thrive, and I’m all for that. I’m coming from an adtech background, so the emphasis was very clear on the “finding meaning” part.

• If that's the case, why this company? I liked that they were VERY fast with their interview process. I told him that and that it shows a lot about the company and how much they care to get things done.

• What was your proudest moment? Told him about the first time I built a tool that helped the business, which was at my current company. The year or so of effort learning python and databases and manipulating dataframes led to a really cool scraping project that now seems rather novice, but I couldn’t contain my excitement when I accomplished it.

Data Scientists

Sit and chat. I asked them questions about how they like it there, what projects they worked on, etc. Very laid back.

VP Marketing (first form)

This was the one guy who really grilled me with problem solving questions.

• Why did google decide to build out their own browser? This is where my background in adtech helped. I listed almost everything I could about user data, selling to advertisers, tracking users, etc. He thought those were good answers, but it wasn’t what he was looking for. He asked me the next leading question.

• What was so good about chrome compared to IE? I stumbled on this since I never could really compare it fully to internet explorer since I never used IE, I just knew people said it sucked. With some guidance I answered correctly: faster load times.

• And what does that mean? I took a few seconds of thought and answered correctly, that google wants their search pages to load faster.

From there, he pulled some stats about google CPC and rates from another country and asked me how much would google make in capturing a certain percent of the internet explorer user market. My process was correct, but the multiplication was off in the end. A bit embarrassing, but at least I owned it and made some jokes about division by hand. Got the correct answer after.

That concluded the first in-person interview. Got called for another in-person and I was shitting myself because I thought maybe they didn’t get enough information. I was much more nervous for this one, but once the interviews started I was calm and confident.

CMO

• What are some of areas that you need development in? Same as I said before- business side things.

• Why the short tenure in your old jobs (4 months, 12 months, 9 months)? THIS is where you have to show yourself as the ever-growing, constant-learning, autodidact with insatiable appetite to learn. I told him I learn on my own outside of work, I apply that knowledge to build cool shit, and that I outgrow my positions very quickly so I needed something more challenging. I backed it up with the projects I completed.

• What'll be the biggest challenge you'll face here? Data Science team structure- sprints, prioritizing the right projects, etc. Haven’t experienced it before so I’d have to learn how to operate within that structure.

• What would your current boss say about you? I explained that I have sort of two bosses, one tech and one nontech. The tech one would say I can take an idea and run with it to build a tool. The nontech would say I’m very helpful and available asap when he needs me.

• What would they say you need improvement on? Nontech boss- business side of things. Tech boss- get more into the details of adtech, like which scripts are executed on the page, how it relates to different servers, etc.

• What would your last boss say about you? Always learning on the job

• What's one example of when you thought outside the box? Gave example of how the data engineering team was backed up and couldn’t ingest some third party data, so I used python to ingest the data 6-8 weeks before they could do it. I also explained that while the process was essentially the same (extract, transform, load) I thought outside the box by not relying on the team assigned with the task and figured out my own way to do it. He thought that was an excellent example.

• What was your proudest moment? Same answer as before

• Why the move? Current company is pivoting, has been for 8 months but not much to show for it, a lot of senior leadership is exiting, not confident in the direction it’s taking, so figured this would be a great time to make a change.

• How would you describe your old bosses? Last job- was first a coworker that was promoted to my boss. She was very kind, figuring out how to manage, but never lost sight of being compassionate and fighting for her team. Wonderful overall. Current job- nontech boss is very hands off since he doesn’t know the details of what I do, but gives good overall ideas. With tech boss, we work together constantly on data tasks or ideas for new tools to build. Very logical and unemotional at work, similar to me.

After, I asked about what success looks like in the role and what were the biggest challenges facing his department.

VP Marketing (final form)

Here he was again! Back with more questions to grill me. I really liked the guy because he did his due diligence, and it was fun because the questions made my brain’s gears go overdrive.

• How would you go about seeing if users ordering from more than one location is profitable? I responded with a very convoluted explanation for A/B test, which he said was good, then asked how to do it without the ability to do A/B test using data we already have. Was able to eventually tell him something along the lines of a time series analysis involving control groups.

• Walk me through how you'll implement A/B test. Told him the basics, but that I haven’t done it in practice. Couldn’t answer his question about how long it should run for so I told him straight up, and he was okay with it.

• How would you go about determining the optimal number of recommendations to show on the app for each geographical type? Basic group-bys by geo and success rate for each number of recommendations shown.

• What is logistic regression? At this point I had just finished one of Andrew Ng’s deep learning course, where you code a logistic regression from scratch, so I did a little showboating here with how much I knew =D

• Take me through the process of how you got into machine learning. I told him basically what I’ve described here- that I felt useless after my master’s, needed to not be left behind in the machine learning revolution, went crazy from day one and here I am.

I asked him:

• What are the projects I'll work on in the first month?

• You worked at other huge and established companies, so why here and what makes you come back everyday?

And! I give you the absolute best question to ask:

• “You’ve had the most opportunity to get to know me and my skillset. I’d like to know if you had any reservations about my qualifications as a candidate so we can discuss and take care of any concerns.”

Boom! And just like that, I knew how impressed he was and that the only reservation was my short experience, but that I more than made up for it with my passion and drive. He almost didn’t want to say my lack of experience was a concern and looked very hesitant, I guess in fear of having me being like “peace!”

And that was that!

Step 9: Wait forever and get paranoid

Title says it all. It’s hard to wait and wait especially when you felt like you did really well, and especially when the interviewing process took 3 weeks but the decision process takes another 3 weeks. My advice is simply keep applying to other places, don’t take your foot off the pedal, and continue learning/building things. I managed to finish another 2 courses from the time of the first interview to the offer, and even built my own small personal website. Don’t let up!

Step 10: Negotiate

I’ll leave it to you to gather more advice on negotiating and how to go about it, but my general advice is to always negotiate. Whether the market value is higher than the offer (I’m not a fan of this explanation but I’ve never had to use it), or you suddenly feel that the responsibilities are worth more or, as in my case, you realize they don’t offer benefits you thought would be offered, then NEGOTIATE. It can be by phone or email, just do it. It’s uncomfortable, you’ll question your decision every second of the day for what seems like forever, you think they’ll rescind the offer and get someone cheaper. Just relax. It’s business. It’s part of showing your skills by not leaving money on the table. With a role as specialized as this where there is a lot of demand, you have the upper hand if you’ve already proved yourself. I got a nice bump at my current job and at the new data science job by asking for more. I’ll leave you this fantastic link that helped with a changing mindset:

http://www.kalzumeus.com/2012/01/23/salary-negotiation/

And that’s a wrap! A quick summary of the most important lessons I learned in this journey:

• You don’t have to get an expensive Data Science degree or go to an expensive bootcamp. Everything is literally available for free somewhere online, and more structured resources are available at very low cost (Udemy and their $10 specials!)

• Glassdoor is the most important app in this process. Download it, keep a fresh copy of your resume on your phone, and send out apps during your commute, at the laundromat, while in bed on a lazy Saturday, etc. It’s almost effortless

• Absorb everything you can. A lot of it won’t stick, but a lot of it will.

• Learning demands consistency. 10 hours of study spread across 2 weeks is much better than 10 hours you did that one weekend 2 weeks ago.

• USE what you learn somehow- if you picked up python, google how to scrape the web, or how to automate sending files via email, or how to connect to a certain database. Make a project out of it, even a mini-project that you can speak about later. Google will show you the way! Optimizing processes is sexy and it was the most frequently asked question in this job search.

• In case you couldn’t tell, google and stackoverflow were lifesavers

• Talk is cheap. A lot of people I know talk about taking classes and how excited they are. A year later they’re in the same place. Learn it, use it, and continue learning. Spend less time talking about how you’re gonna do something and work towards getting it done.

• You’ll stumble through a lot of material- and that’s okay. Not everything is connected in the beginning, and a lot of it will feel like wasted effort. Keep going! You’ll reach the “aha!” moment when everything clicks and you “get it”. It might take a year and a half, but think about what would have happened if you started a year and a half ago?

• Adding to the last point, it’s hard to know where to start and where to go. I’ll summarize a cheap quick start guide for data science below if you’re lost!

• Get ready to make sacrifices. On average it was 3-4 hours daily, everyday, before or after work, and sometimes 6 hours on each of the weekend days. And this isn’t counting the coding I did during work to make things more efficient, which is at least another 3-4 hours per workday.

• I did take about 6-8 weeks off in total throughout the whole process though. You’ll burn out sometimes, and that’s okay! If you’re as driven and passionate as I was, you’ll come back to it weeks later, maybe even a month.

• Lastly, reddit is a place of vast knowledge of the field. Use it, go to r/learnprogramming or r/datascience or r/jobs or r/personalfinance. There will be questions and topics covering a lot of what I covered here.

Quick start guide for data science:

(in no particular order)

• Introduction to Computer Science with Python from Edx.org

• Either:

o Andrew Ng’s Machine learning via coursera (not in python, but teaches you to know the matrix manipulation fundamentals)

o Statistical Learning via Stanford Lagunita (more theory than programming understanding, but covers similar concepts, and introduces R which is also a good tool)

• Python Data Science and Machine Learning Bootcamp via Udemy Again, this is just to get started. Google and stackoverflow will take you to the next level and other courses will fill the knowledge gaps.

Full list of courses I’ve completed:

• Complete Python Web Course from Udemy

• Complete Python and PostgreSQL Developer Course from Udemy

• Deeplearning.ai's Specialization from Coursera

• Statistical Learning from Stanford Lagunita

• Python for Data Science and Machine Learning from Udemy

• Introduction to Data Science in Python from Coursera

• Introduction to Computer Science and Programming using Python from Edx

• Analytics Edge from Edx

• Machine Learning from Coursera

Thanks for reading! Wishing you the best in your data science journey. I hope it’s as rewarding, exciting, and fruitful as it was for me.

Hey everyone,

Thank you for stopping by and reading the post. A little background of me; I’ve recently graduated from Dental Hygiene School, and have a B.A. in Biology.

I’ve had a rough start in my hygiene career, but thankfully landed at a great clinic along an amazing team.

Dental Hygiene takes a huge toll on the body overtime, and I may have a rheumatoid condition. It’s put a few things in perspective, and I might wanna veer away from the career and move towards something else.

I’ve studied biology and in the midst I’ve taken classes for Stats and Calculus and really enjoyed it. I’ve never coded and have no real computer knowledge outside of PC gaming, plus I can be lazy at times and have difficulty problem solving individually. I enjoy presenting, and love explaining things in ways for people to understand what I’m talking about. I ask a lot of questions in regards to how and why things are done in specific ways. One big driving force for me is having a team to support

For those who have more insight in this realm of careers,you think someone like me could potentially be a great data scientist?

We live in a world where companies accumulate vast quantities of information. They’re trying to use that information to make hard decisions. That’s where data science can help – it is mainly focused on taking raw data and turning it into value.

A data scientist collects data, scrubs it clean, studies it and presents its findings in a way that helps businesses, the government, or research more effectively. If you look at what a detective does, you won’t be surprised: the detective follows leads. A data scientist follows data.

The question on most people’s minds is how to become a data scientist and other related questions – this article will show you how.

Educational Qualifications Required

If you want to start your career in data science, the first and best thing is to get an education. In general, employers will look for at least a bachelor’s degree in a quantitative or technical field like math, statistics, computer science or engineering. 

Credentials at a master's level will give you a leg up if you’re looking to make an impression. A vast majority of data science job descriptions now list a degree in data science as a strong preference.

Recommended Degrees (Statistics, Computer Science, etc.)

Some of the degree paths you could consider include:

• A bachelor's in Statistics, which provides profound knowledge of probability, sampling and inference.

• A bachelor’s degree in Computer Science, where you will learn programming, algorithms and data structures.

• A bachelor’s degree in Mathematics, to develop logical and analytical thinking.

• An undergraduate degree in Engineering, specifically those that have to do with computing and data.

You can study the B.Sc. Data Science & Big Data Analytics programme at MIT-WPU, Pune. This programme teaches programming languages such as Python, R and SQL as well.

The other one is the integrated B.Tech CSE (Artificial Intelligence & Data Sci) programme at MIT-WPU, Pune, where you get the best of both computer science engineering and AI and data science.

These are the degrees that prepare you to answer the question of how to become a data scientist.

Essential Skills for Data Scientists

As you progress through your degree or begin to study, you must develop the skills required. This is what you need to become a data science expert.

Programming Languages (Python, R)

Most data scientist roles require you to be a programmer. Two of the more popular languages are Python and R. Python is general-purpose, with broad industry adoption. R is a powerful system for statistical computing.

You also need SQL (for databases) and sometimes tools like big data platforms.

Statistics and Mathematics

You have to know fundamental mathematics such as linear algebra, calculus, probability and more statistics than you think. These allow you to make model-based inferences, explore hypotheses and infer conclusions from data.

Another report claims that analytics skills are in ‘extremely’ high demand because analysis drives business performance.

Software for Machine Learning and Data Visualisation

Contemporary data science approaches rely on machine learning (ML) for predictive modelling. Over three-quarters of jobs posted for data scientists need ML skills.

You should also be familiar with any data visualisation tools (e.g. Tableau, Power BI) or libraries (matplotlib/seaborn) to clearly communicate your results.

Recommended Certifications and Online Courses

In addition to your degree, you can strengthen your credentials with certification or online learning. There are countless platforms that provide data science courses in Python, statistics, machine learning and visualisation.

These enable you to address gaps or specialise in an area. For instance, you might go after a certificate in machine learning or one in a tool.

When looking at a full-time qualification, a data science full time course at university or college can offer structured, immersive learning and often an accredited qualification.

Building Real-World Experience

It’s great to have theory, but you need to demonstrate that you can use it.

Internships

Look for internships in data science, analytics or business intelligence. Real organisations also provide real data, real problems and real access to how decisions are made.

Projects and Kaggle Competitions

Work on your own projects. Use public data sets. Take part in competitions on sites like Kaggle. Publish your work in a portfolio or blog.

This really cements the question of how to become a data scientist. You are showing that you can deliver.

Career Path and Job Roles

The normal career route would be:

• Beginner Data Analyst or Junior Data Scientist

• Data Scientist (after 2–4 years)

• Lead Data Scientist or ML Engineer

• Chief Data Scientist or Data Science Architect

Data engineer, machine learning engineer, business intelligence developer and data architect are all similar job titles.

The need for data scientists remains strong. According to one source, the market of data science platforms is projected to expand at a CAGR of 25.7% till 2032.

Tips for Aspiring Data Scientists

• Begin early: start learning programming, mathematics and statistics now.

• Create a portfolio: real projects demonstrate that you can do the work.

• Be curious: ask questions, look for data, try to tell a story.

• Keep learning: tools and methodologies change rapidly.

• Network: join data science communities, attend events and connect with professionals.

• Opt for a good data science full time course if you can, but also monitor self-learning.

• Combine technical expertise with domain knowledge. Understanding how a business operates can lead to greater success as a data scientist.

The Future for Data Science Jobs

For anyone wondering how to become a data scientist, the future is bright.

With more and more organisations depending on data, the demand for talented individuals will only continue to rise. If you have the right education, skills, experience and mindset, you can establish a successful career.

Whether you choose a full-time data science full time course or a more focused certificate, the important thing is to keep learning and keep practising.

If you pick wisely and are ready to study hard consistently, you can become one of the data scientists making decisions that affect the entire industry.

I wanted to share some advice for those of you just starting your career: Don't limit yourselves to only accepting a "Data Scientist" title straight out of university (or BootCamp).

I can agree that the "ideal" path to becoming a data scientist is to land DS entry-level role or internship right after graduation. However, the reality is that this is much more difficult than you might think, especially now.

I didn’t take the most direct path to my first job as a Data Scientist.

I graduated from university with a B.S. in Computer Science and a specialization in Machine Learning and landed my first full-time job as a Data Analyst shortly after graduation. About a year later, I started a new role as a Business Analyst (aka Business Intelligent Analyst). And after working for about 2 years as a Business Analyst, I went on to land my first role as a Data Scientist.

All and all, I’ve been working in Data & Analytics for almost 7 years now. I genuinely believe that working as a Data Analyst and Business Analyst helped me become a much more well-rounded Data Scientist, so I don't regret following the longer path.

Just keep an open mind and consider other data titles along your journey. I wrote an entire article on this topic in case any of you are interested.

Best of luck out there!

My question to you, a data scientist at any level of their career, is that how was your journey to becoming a data scientist?

Did you start your professional career with data science? If not, which field did you transition from? What is your skill set? (Stuff other than DS as well) How long have you been in this field? What are your plans in the future?

Just curious to see what kinds of backgrounds do data scientists come from, and what avenues did people choose to get to this point.

So, I've been toying with this idea of diving headfirst into the world of data science. The whole thing - crunching numbers, using code to solve complex problems, even having an exciting moment when a pattern emerges from a sea of data - sounds super exciting to me. I already have some knowledge in the area such as knowing what does ++ mean in coding, but I am definitely eager to learn so much more. I'm aware that changing lanes isn't as easy as it sounds, and that's why I'm here, calling out to all the data science girls, enthusiasts, and everyone in between.

• Skills to Thrill: What are the top skills you think a newbie data scientist needs to get their hands on? I mean, I know about Python and SQL, but what about stuff like TensorFlow or Apache Spark?

• Learning the Ropes: What's the general vibe towards online learning versus formal degrees? Would it be better to learn it by myself or get a certification from an online bootcamp? Or do Master’s? Maybe you have any suggestions for best online coding bootcamps or Master degrees?

• Study Stuff: Any books, courses, or blogs you swear by? I've heard about "Python for Data Analysis" and "The Elements of Statistical Learning". Do you have any other favorites?

• Projects: What kind of stuff should I be working on as a beginner? Would it be a good idea to work on personal projects like a movie recommendation engine or contributing to open-source projects? And how can I show these off to potential employers?

• Schmoozing: How important is networking in this field? Should I be hitting up industry events, participating in hackathons, or joining LinkedIn groups?

• Jobs: What are data entry jobs and which ones would you recommend to look into?Are specialized roles like machine learning engineers on top right now, or should I aim to be a jack-of-all-trades?

I'm sure your experiences and insights will be super helpful for a rookie like me and others in the same boat. Any advice you can offer will be seriously appreciated!

Thanks in advance for the help!

So I'm studying economics in my second year and for the last month and a half I've been learning python and I've been enjoying it. I've been reading about data science and I'm really interested in it, however I wanted to ask if there are any self taught data scientist and what resources you used. Sorry if what I say seems naive. Thank you Edit: I can't reply to all the comments, but I read and appreciate all of them! Thank you

Hey everyone!

I’m excited to share my latest blog on Medium about "My Journey to Become a Data Scientist"

In the post, I talk about how I transitioned from having zero technical background to diving deep into Python and embracing data-driven decision making. I share the challenges I faced along the way and what kept me motivated.

If you're thinking about a career in data science or making a non-tech to tech transition, this blog might inspire you to take that first step!

👉 My Journey to Become a Data Scientist

Would love to hear your thoughts or experiences too!

I have been hunting jobs for almost 4 months now. It was after 2 years, that I opened my eyes to the outside world and in the beginning, the world fell apart because I wasn't aware of how much the industry has changed and genAI and LLMs were now mandatory things. Before, I was just limited to using chatGPT as UI.

So, after preparing for so many months it felt as if I was walking in circles and running across here and there without an in-depth understanding of things. I went through around 40+ job posts and studied their requirements, (for a medium seniority DS position). So, I created a plan and then worked on each task one by one. Here, if anyone is interested, you can take a look at the important tools and libraries, that are relevant for the job hunt.

Github, Notion

I am open to your suggestions and edits, Happy preparation!

I’m about to graduate high school in May and start college in the summer. I had my major set to animal biology to become a vet, but I changed my mind and I’m looking into data science and other related fields. The college that I chose doesn’t have a computer science major because it’s a smaller campus part of a system. The closest major that they have is Business Analytics & Information Systems. Can I become a data scientist with this major? If not, what are related jobs that day decently well? What are some other qualifications you would need on the job, entry level pay, things that you actually do at work, stress levels, how easy it is to find a job?

Hey all,

I am currently finishing my sophomore year of university for my computer science degree, and I want to go into data science or even data analystics in the future, however I am unsure on how to start given I don't have any experience with this stuff. What would you guys recommend that I do to get started?

Any thoughts would be appreciated.

Thanks.

Hey everyone — I'm finishing up my MSc in Data Science this fall (Fall 2025). I also have a BSc in Computer Science and completed 2–3 relevant tech internships.

I’m starting to plan my job hunt and would love to hear from working data scientists or others in the field:

• Should I be applying in bulk to everything I qualify for, or focus on tailoring my resume with ATS keywords?

• Are there other strategies that helped you break into the field?

• What do you wish someone had told you when you were job hunting?

• Is it even heard of fresh graduates landing data roles?

I know the market’s tough right now, so I want to be as strategic as possible. Any advice is appreciated — thanks!

I am a Software Developer, I want to start learning Data Science. I recently started studying Statistics and understanding the basic Python tools and libraries like Jupyter Notebook, NumPy and Pandas. but, I don't know where to go from there.

Should I start with Data Analysis? or Jump right into Machine Learning? I am really confused.

Can someone help me set up a structured roadmap for my Data Science journey?

Thank You.

Hi long post ahead,

I will try to list down possible steps on how to land a job as a data scientist, no matter if you're a career shifter or a fresh graduate. These are all my personal opinions based on my observations and this will not guarantee that you'll receive a job offer for a data scientist position. Pero keep on pushing mga kapatid. Ohers may add their own tips if I missed any.

How I became a Data Scientist:

- Passionate about data science since college, was forced to get a job immediately after graduation because I'm a breadwinner. Data Science jobs for fresh grads are a rarity back then so I took a job as a software engineer just to rack up experience, constantly joining Kaggle competitions and studying about DS. Finally got a job as a Data Scientist after 2 years.

Now, How to become a data scientist:

1. educational background - If you already have a degree on BSCS or BSIT or BSIS or any other computer studies course, then that's already an advantage. BSCS is the better course if you're pursuing a career in DS. If your educational background is not related to the courses above don't worry proceed to #2.

2. learn the fundamentals - The next step for you is to learn the fundamentals of data science thru reading books, watching ds courses (youtube, udemy, coursera,etc). I have no specific suggestion for a tutorial or content creator to follow, but look for tutorials in DS that has a good blend of Statistics and Machine Learning. Don't focus too much on ML there's more to Data Science than fancy machine learning algorithms. You could also try taking up a short course such as DOST's SPARTA, or taking another full-blown degree like BS Data Science or MS Data Science.

3. rack up relevant experience - How can you gather DS-related experience if you couldnt even land a DS job? Get a DS job lol.

   1. You could look for internship on startup AI companies here in the Philippines. I'm sure they'll be willing to accept interns but the allowance is not a guarantee. See my post here.

   2. Build up your profile. Join Kaggle competitions, join hackathons, get publicly available data then do some analysis, then do some machine learning, then post your analysis and results online thru blogs or videos.

   3. If you are currently employed, try proposing data science related projects to your supervisor.

4. apply, apply, apply - Send your application to any job posting that is looking for a data scientist. Data Scientist fresh graduate? APPLY! Data Scientist 3+ years relevant experience? APPLY! Senior Data Scientist 5+ years relevant experience? APPLY! This step is probably the most tedious and frustrating part. Data Science is still a pretty young field and there's not that much demand for Data Scientists currently especially for freshers. Just keep on sending applications and whenever an opportunity for an interview arise, always give your best. Just for motivation, I have recorded (almost) all my job applications since 2018 in a spreadsheet and just to summarize: I applied to 60+ job posting, got interviewed 21 times, got the job offer 4 times. You'll never fail in a job interview, you will either pass or you will get better. Goodluck!

Edit: Formatting and stuff