Are you a Data Analyst trying to break into data science? In that case, this post is for you.
Many individuals start in analytics since it generally has a lower barrier to entry, but as they gain experience, they realize they need to tackle more technical challenges, dive deeper into machine learning, and even just increase their earning potential. Moving from Data analyst to Data Scientist is usually a smart profession move — however it requires the suitable strategy.
In the event you’re recent here, my name is Marina. I’m an Applied Scientist at Amazon, and I’ve helped dozens of individuals transition into tech, even from non-traditional backgrounds — myself included.
On this post, we’re going to cover all the pieces it’s good to know to make the transition from data analyst to data scientist successful:
- What skills you’ll have to develop
- My favorite learning resources
- And techniques for landing interviews and securing job offers
Let’s get into it, starting with deciding if this transition is even an excellent idea for you in the primary place.
Role comparison
Before we start, let’s just be certain we’re all on the identical page about what the difference is between these roles anyway, starting with data analytics.
Data analysts deal with working with structured data to drive business decisions. Their toolkit typically includes SQL, Excel, Tableau or PowerBI, and basic Python for data processing, visualization, and possibly easy statistical analyses. The role centers on understanding data to reply questions on what happened and why.
Data scientists construct on these foundations but extend into predictive modeling and automatic decision-making. While additionally they use SQL and Python, they work more extensively with statistical modeling, machine learning frameworks, and cloud platforms. Their focus shifts to predicting outcomes and recommending actions.
A typical misconception is that data analysts must develop into data scientists to advance their careers. That’s definitely not true!
Senior analysts can earn high salaries and have a very strong business impact without deep ML or statistical knowledge.
Truthfully, not everyone seems to be going to enjoy data science work, and lots of could be happier staying on the analytics path.
So before we go any further, ask yourself the next questions:
- Are you interested by machine learning and the way it really works?
- Are you comfortable with (or a minimum of desirous about) advanced mathematics and statistics?
- Are you comfortable with technical challenges and software engineering concepts?
- Are you pleased with a task that has numerous ambiguity, each within the each day work and the profession progression?
In the event you’re still with me and considering “Yes, I definitely need to pursue data science,” let’s speak about the right way to make it actually occur.
Skills needed to transition
Alright, so now that you simply’ve decided to make the transition, let’s break down the important thing skills you’ll have to develop. We’ll deal with 4 core areas that form the inspiration of information science work.
Mathematics & statistics
In the event you’re coming from an analytics background, you almost certainly have some exposure to statistics, but data science might require a bit more depth on the maths front. You’ll should be comfortable with:
- Multivariable calculus and linear algebra, particularly matrix operations and gradients for understanding machine learning algorithms. But don’t worry — you don’t should be a math expert, you only need enough to grasp the basics to allow you to grasp how algorithms work.
- You’ll also need probability theory and hypothesis testing for experimental design.
- In addition to statistical concepts like several types of distributions and regression techniques
- And ideally, some experience with causal inference
Programming
In the event you’re already using SQL and basic Python in your role, you could have a head start here. Now it’s nearly leveling up. Give attention to:
- More advanced Python, so things like OOP fundamentals, writing modular maintainable code, unit tests, performance optimization, and so forth.
- Using ML frameworks like scikit-learn, Tensorflow, and PyTorch.
- And familiarity with basic data structures and algorithms for coding interviews. Generally this can just be questions on arrays and strings, so that you don’t have to go too crazy with this, however it’ll be necessary to know for interviewing.
Machine learning & AI fundamentals
That is one other core pillar of information science, so that you’ll need to be comfortable with ML fundamentals like:
- Supervised learning (so, regression and classification).
- Unsupervised learning (things like clustering and dimensionality reduction).
- Model evaluation and validation.
- Deep learning basics.
- And nowadays, being conversant in GenAI is a plus (but by this I mean learning the right way to work with APIs, not training models from scratch)
Big data & data engineering concepts
Finally, many data science roles involve working with large-scale datasets and constructing automated pipelines. For this, you’ll need to deal with:
- Working with cloud computing platforms, particularly AWS services like S3 and SageMaker
- Data pipeline development using tools like Airflow
- Potentially basic system design principles for scaling your solutions (that is more necessary as you develop into more senior or focus more on ML).
Find out how to develop these skills
Now that we’ve covered it’s good to learn, let’s speak about the right way to actually construct these skills. There are just a few different paths you may take, and the suitable one for you may rely on your budget, learning style, and schedule.
Self-study
In the event you’re self-motivated and disciplined, self-study is usually a totally reasonable and cost-effective technique to transition into data science. The secret is consistent practice and selecting the suitable resources.
Listed below are some great courses I’d recommend testing, so as (these are affiliate links, btw!):
You’ll also have to get an understanding of basic DSA for coding interview prep. For this I enjoyed Educative’s Grokking the Coding Interview Patterns in Python, which focuses on common patterns for data structures and algorithms questions. I discovered this really helpful in order that it doesn’t just look like it’s good to “know the trick” to reply the LeetCode problem.
And, just a few books which might be value reading (these are also affiliate links, but I do <3 all these books):
There are tons more, but this may be my top three. Here’s a link to more of my favorite technical books if you desire to explore further!
A very powerful thing when going the self-study route is consistency. Make a schedule and keep on with it, even when it’s just just a little bit every day.
Bootcamps
Now, possibly you’re considering you’d prefer to have just a little more structure and out of doors accountability in your learning. In the event you don’t need to commit to a full degree, bootcamps will be an alternative choice.
Some pros of bootcamps are:
- Fast-paced learning — You generally can complete them in just a few months.
- Structured curriculum, because all the pieces is laid out for you, so that you don’t need to piece together your individual learning plan.
- And community support — You get to learn alongside peers and get mentorship from instructors who could also be folks already working in the sector.
One thing to take note is that bootcamps vary in quality, and never all are super valued by employers. Before enrolling, do your research — so, check reviews, discuss with alumni, and be certain they provide profession support.
Master’s degree
For those in search of a deep dive into data science with strong networking opportunities, a Master’s degree is usually a solid investment. This is particularly useful if you happen to’re transitioning from a non-technical background, or if you happen to’re anxious your background won’t be passing resume scanning tools.
The downside is clearly that Master’s programs will be expensive and time-consuming. But the excellent news here is that there at the moment are inexpensive, part-time online programs that permit you to study while working. For instance, Georgia Tech’s programs are really inexpensive and of pretty decent quality.
Mentorship
Regardless of which path you are taking, mentorship will be incredibly helpful. Having someone to guide you, provide feedback, and help with profession navigation could make an enormous difference.
Some ways to search out mentors:
- At your organization — If your organization has data scientists, ask if you happen to can collaborate or shadow them.
- LinkedIn — Join data science groups or reach out to professionals (I even have an entire video on mentorship strategies if you happen to need assistance with this!).
- Online communities like Reddit, Discord servers, and Slack groups will be one other avenue to attach with fellow learners and professionals.
- Or, hire a mentor — In the event you’re serious about leveling up quickly, investing in a mentor be value it.
Demonstrating experience
Okay, so that you’ve learned all the abilities you would like. That’s great, but how do you prove to a possible employer that you simply actually can do the job of a Data Scientist?
I even have an entire video on the right way to construct a portfolio and get experience outside of your full-time employment. The TL;DR there’s that it’s best to try your best to do self-motivated projects that permit you to simulate the working conditions of being on the job as closely as possible.
But if you happen to’re reading this post, there’s an honest likelihood you’re currently working as a Data Analyst already, which provides you an entire other set of opportunities to leverage inside your current role.
For instance, let’s say you’re commonly creating reports in Excel or Tableau. You might automate this process with Python scripts, possibly even add some predictive elements. Or if your organization runs A/B tests, volunteer to assist with the statistical evaluation.
If you could have a knowledge science team, attempt to collaborate with them on a project. And if there isn’t a knowledge science team, pitch your employer on some impactful projects that will also allow you to to learn.
Best case scenario, this can lead to an internal transition. Worst case, you now have concrete examples of impact and real data science projects to incorporate in your resume.
Getting a job
In the event you’re in a position to transition internally then great, you’re done! If not, listed here are some strategies to allow you to get that first Data Science role:
First, let’s speak about the right way to position yourself online. Your resume, LinkedIn, and GitHub have to tell a consistent story that you simply are already a reliable data scientist (because if you could have the abilities and have done solid projects, you’re!). So, as a substitute of writing “Data Analyst in search of Data Scientist role,” you would possibly say “Data skilled specializing in predictive analytics and machine learning.”
On the subject of your GitHub, be certain to place your best stuff at the highest here. This is particularly necessary for analysts, since your coding skills can be under more scrutiny. So,
- Pin your best ML projects at the highest
- Write clear READMEs that designate your approach
- Be sure that your code is well structured and documented, showing you understand software engineering principles
- And add visualizations and results to showcase the impact, which must be easy for you along with your background!
Once it’s time to use, prioritize hybrid roles. These are positions that sit between traditional analytics and data science, they usually’re often a superb stepping stone.
For instance, a lot of firms (including big tech firms like Meta and Amazon) have roles that they call “Data Scientist” but are literally more like advanced analytics positions. And truthfully at many firms, the lines are blurry anyway. Use this ambiguity to your advantage!
While you’re networking and preparing for interviews, leverage your analytics background. Use your deep understanding of business context, clear communication skills, and examples of the way you’ve influenced the business to deliver measurable impact. Other candidates who could also be more technical than you would possibly struggle with the business and communication side of things. So don’t be afraid to lean into your strengths.
Remember, this transition isn’t going to occur overnight, and that’s okay. What matters is consistent progress. Every line of code you write, every concept you learn, every project you complete — all of it adds up.
In the event you’re feeling like you would like some support along with your data science/ML profession, listed here are some ways I will help:
