How to Successfully Make a Data Science Career Transition – Everything You Need to Know

Making a Data Science Career Transition? You Need to Know How to Overcome Key Challenges

So, you’ve chosen data science as your potential career. You’ve read the headlines, surveyed the web to understand what data science is, looked at a few courses and articles, and are feeling confident about your choice.

It’s a great decision!

Data science is still a nascent field with a stunning number of job openings around the globe. The demand is outstripping the supply! That means there are more vacancies than qualified data science professionals.

But how is that possible? Isn’t the world swimming in data scientists right now? Doesn’t everyone want to land the “sexiest job of the 21st century”? That’s the overwhelming feeling we get when talking to people, looking at media reports, scouring social media, etc.

Here’s the thing – yes, there are thousands of data science aspirants – they just don’t know how to successfully transition into data science. They have the required theoretical knowledge but no practical work experience. This is a major hindrance and is often a source of frustration for data science transitioners.

The path to becoming a data science professional is riddled with obstacles. It’s not a smooth journey and anyone transitioning into this field should be aware of it. There are key challenges every transitioner inevitably faces – and that’s what we aim to address here.

This resource is your one-stop destination to have all your data science career transition questions answered. We’ve interviewed hundreds of these candidates over the years and can confidently pinpoint key areas where transitioners often trip up. By the end of this resource, you will be in a much better position to make that career transition to data science – and ace it!

To help you make this transition, we have put together the most comprehensive resource of all, the AI and ML BlackBelt+, that combines the power of data science, machine learning, and deep learning to help you become an in-demand industry-relevant data science professional!

Here’s What We’ll Cover in this Data Science Career Transition Resource

• The Ever-Expanding Power of Data Science
• Different Roles in Data Science
• What Does a Data Scientist Role Looks Like?
• Skills you Need for Data Science
• Data Science Tools you Need to Master
• Work Experience Questions – How Can you Leverage your Previous Experience in Data Science?
• Data Science Interview Tips for Transitioners
• Common Myths About Data Science Transition You Should Avoid
• Common Mistakes Data Science Transitioners Make and How to Avoid Them

The Ever-Expanding Power of Data Science

Data Science seems really exciting but first, let us get our basics clear! What actually is data science? I’m not going to bore you with long lines of definition so here’s a short explanation:

Data Science is an amalgamation of Statistics, Computer Science, and specific domain knowledge.

Statistics and computer sciences are the generic fundamentals that can be perfected by studying and a little bit of practice. It is the domain knowledge that takes time, research, and effort to gain.

You don’t need to master each vertical but having a decent grip on all will help you in the long run.

Data Science is quite a big field in itself. It starts with simple data reporting activities to advanced predictive modeling using Artificial Intelligence. As you can observe by looking at the Data science spectrum below, the higher the complexity the higher its business value.

Here are the numbers to back it up – the market size in 2025 is expected to reach $140 billion. This means we can expect this surge in demand for data scientists to continue abated for years!

Data science is only getting started.

Different Roles in Data Science

So – you are now well acquainted with the power of data science. Honestly, the use cases of data science are ubiquitous now – it’s up to us to leverage our knowledge and put that to practical use.

Before you actually understand what tools or technical skills you need to transition into data science, you should be aware of which role you want to apply for. That’s right – contrary to popular belief, data scientist isn’t the only role in this field!

One of the things we have observed about these opportunities is the indistinguishable description of job roles. Even though the majority of recruiters use the right description for various data science job roles, the candidate might not be able to make that differentiation. Therefore, this confusion between the job role and job description might lead the aspirant to apply for the wrong jobs and missing out on appropriate opportunities.

Even in such a flourishing industry, there is confusion with respect to job roles. A loose understanding of job roles may cost data science transitioners their dream job. And this is precisely the driving force behind writing this section.

So, here are 8 data science roles that are widely acknowledged in the industry:

• Data Scientist (of course!)
• Data Engineer
• Data Analyst
• Business Analyst
• Statistician
• Data and Analytics Manager
• Database Administrator
• Data Architect

So, which role is for you? Here’s an awesome infographic that details the difference in a data scientist vs. data engineer vs. statistician role:

• Job Comparison – Data Scientist vs Data Engineer vs Statistician

I also highly recommend reading the below resource that goes into detail about each role we mentioned above:

• Top 8 Job Roles in Data Science Industry That You Should Know!

What Does a Data Scientist Role Look Like?

Let’s talk about the data scientist role here. This is the one most transitioners are looking for these days so let’s spend some time understanding what this role entails. We hope this will give you a good idea before you take the leap into data science.

Caution: These terms are loosely used in the industry. The exact role can depend on the maturity of your organization in data initiatives.

The role of a data scientist is fairly expansive and will depend majorly on the type of project that you are working on. Here, we will discuss the general lifecycle of a data science project.

• Understanding the problem statement – Seems really simple, right? Believe me, it isn’t. Understanding the problem statement will be the make-or-break situation for the complete duration of the project. At this stage, A team of data scientists and the concerned team go over the objectives and expected requirements of the project. It requires good communication skills, stakeholder management for this step. A good data scientist won’t hesitate to spend an ample amount of time on this step. Once the problem statement is clear, the data scientist can move on to the collection of data
• Gathering Data – Once the requirements are obtained and the hypothesis formed, the data scientist then proceeds to mine the needed data. The source of the data can vary such as company data warehouse, web scraping, and so on
• Data Cleaning – This is the most time-consuming process of the entire data science project. It may take up to 80% of your time. Here, the data scientist will be munging, manipulating, wrangling the data. The time and effort are worth it since the health of your data will reflect the health of your output model. During this stage, the data scientist deals with outliers, missing data values, correcting the data types, and many other operations. This is not the most exciting step but the most essential one
• Exploratory Data Analysis (EDA) – It is basically the step where the data scientist gets the “feel” of the data. It is at this stage that you can analyze each feature or multiple features in the dataset and check how they behave. You may also analyze the relationship of features with other features. You can expect a lot of data visualization at this stage. Be ready to gain some crucial insights during this stage that will help you in other steps
• Feature Engineering – Feature engineering is not so much of a step but an art. It is an iterative process, going one by one through all the features and applying operations to improve the performance of the model. For example, you can combine some of the strong features and try to improve the model. It will require a lot of trial and error
• Model Building – Model building in itself is relatively a fast step but planning is important. Do you want a model with high accuracy or a model that can return the importance of features? You will need to think upon and select your strategy for model building and its evaluation
• Deployment – Once you have built and evaluated your model, it is finally time to deploy it in the real world. This step typically requires the data scientists to work with data engineers or machine learning engineers

Key Skills you Need to Transition into Data Science

Ah, the key question that has stumped thousands of data science transitioners. There are so many resources and videos out there that talk about multiple skills you need to become a data science professional.

Here, we’ll sift through the noise and present a clear vision of what you should work on to succeed in data science.

We’ve divided the skills into two core areas:

• Technical skills
• Soft skills

Each category has its own unique advantage that you should be aware of. Let’s understand both skillsets individually.

Core Technical Skills you Need to Transition into Data Science

There are certain key skills you need to learn (and master) in order to succeed in data science. While these skills can vary depending on your role and your project, there are certain skills that are usually applicable across domains.

I’ve taken this handy table drawn up in this article to explore the various technical skills you should be aware of:

Again, just want to reiterate that you won’t need to know ALL of these. It will depend on your role. But certain points like domain knowledge and programming will translate to most (if not all) data science roles.

Soft Skills you Need to Transition into Data Science

This is another underappreciated aspect of a data science professional’s skillset. Soft skills are absolutely key to getting your break in data science. As we mentioned at the start of this page, we have interviewed hundreds of data science aspirants and transitioners and these are the 3 key soft skills we found most were lacking.

1. Problem-Solving skills – The knowledge of statistics and computer science can be achieved by studying but it is the domain knowledge along with the problem-solving skills that will help you become a long shot. A majority of companies start their data science recruitment with problem-solving tests. You don’t need to be a master at it but a curious mind will help you in forming this skill.

2. Structured Thinking – The ability to structure your thoughts and map out each of them is certainly a must-have skill. Structured thinking is made of use in the initial steps of the project where the problem statement and hypothesis are to be formulated.

3. Storytelling Skills  – A key skill that all the data science and analytics professionals must have is the ability to express the data in a format that is understandable by the stakeholders – a story. It is this step that requires creativity and human skills.

Tools you Must Master as a Data Science Career Transitioner

In this section, we’ll understand the various tools you need to successfully transition into data science. I have mentioned 4 key tools here, that cover the length and breadth of a typical data scientist’s role.

Again, keep in mind that this might vary depending on your project or organization but these are core tools that most industries use.

• Microsoft Excel – Excel prevails as the easiest and most popular tool for handling small amounts of data. The maximum amount of rows it supports is just a shade over 1 million and one sheet can handle only up to 16,380 columns at a time. These numbers are simply not enough when the amount of data is big.

• SQL – SQL is one of the most popular data management systems which has been around since the 1970s. It was the primary database solution for a few decades. SQL still remains popular but there’s a drawback – It becomes difficult to scale it as the database continues to grow.

• Python – This is one of the most dominant languages for data science in the industry today because of its ease, flexibility, open-source nature. It has gained rapid popularity and acceptance in the ML community.

• Tableau – It is amongst the most popular data visualization tools in the market today. It is capable of handling large amounts of data and even offers Excel-like calculation functions and parameters. Tableau is well-liked because of its neat dashboard and story interface.

Now, we frequently receive queries around which tool to pick, which language to learn, what area to focus on, among other things. So now, we’ll discuss a few common questions transitioners have and help you answer them.

Do I really need to learn programming to break into data science?

Here’s the thing – there is no one size fits all approach to data science. We’ve already seen the different roles available in this field earlier. The skillset required for each role varies on the requirement for that role plus the project or organization you’re working for.

I suspect a lot of you reading this will want to become a data scientist (or a data analyst). So do you need to learn to program?

Yes, you absolutely do. We already saw the different steps data scientists work on earlier. You’ll need to lean on your programming skills at each stage – from gathering the data from different sources all the way to deploying your model. This is a key skill you need to learn and master. There is no getting away from it.

Now, what if you’re more inclined towards a business analyst or a senior manager role in data science and analytics? Programming isn’t a must for these roles but you’ll still need to at least become familiar with drag and drop tools like Tableau or Power BI. These are commonly used to map visualizations and extract useful insights for the business.

Which brings me to the next question – which programming language should you learn for data science?

Python vs. R vs. Other Languages – Which One Should You Learn?

Raise your hands if you’ve ever asked this question or have answered it before. I’m fairly certain all of you will have come across this eternal dilemma about choosing the “perfect” programming language to start your data science career.

Unfortunately, there is no so-called “perfect” language for data science. Each language has it’s own unique features and capabilities that make it work for certain data science professionals.

And the choice isn’t limited to Python, R, and SAS! We are living in the midst of a golden period in programming languages as we’ll see in this article.

Some languages may be suitable for fast prototyping while others may be good at the enterprise level. So let’s clear the confusion once and for all and see which is the best language that suits your data science career goals.

Here are the most popular programming languages being used in the data science industry right now:

1. Python
2. R
3. Julia
4. Java
5. C/C++

To understand what each language brings to the table and see their comparison, I highly recommend checking out these two excellent articles (both are downloadable!):

• • Python vs. R vs. SAS – Which Language Should you Learn?
  • 5 Popular Data Science Languages

Is my experience in Excel going to count?

Ah, good old Microsoft Excel. Honestly, Excel is still the most popular data analysis tool in the industry. Yes, after all this time, Excel still holds its own compared to all the other tools that have come out since.

Microsoft Excel is the gold standard in data analysis tools. There’s no question about it – industry experts, professionals and veterans still lean heavily on Excel’s prowess and Swiss Army Knife nature to slice and dice their data.

I still remember my initial days learning Excel – I was mesmerized by the incredible array of formulas and functions Excel offered. This remains unparalleled in the industry. No matter what task you’re working on, data manipulation, exploration, visualization, etc., Microsoft Excel seemingly has a solution for everything!

Excel provides tons of applications and functionalities that we can use according to our own use case. And yes, you can even use Excel for predictive modeling!

Wait – Excel for predictive modeling? Really?

That’s typically the first reaction I get when I bring up the subject. This is followed by an incredulous look when I demonstrate how we can leverage the flexible nature of Excel to build predictive models for our data science and analytics projects.

Let me ask you a question – if the shops around you started collecting customer data, could they adopt a data-based strategy to sell their goods? Can they forecast their sales or estimate the number of products that might be sold?

Now you must be wondering how in the world will they build a complex statistical model that can predict these things? And learning analytics or hiring an analyst might be beyond their scope. Here’s the good news – they don’t need to. Microsoft Excel offers us the ability to conjure up predictive models without having to write complex code that flies over most people’s heads.

We can easily build a simple model like linear regression in MS Excel that can help us perform analysis in a few simple steps. And we don’t need to be a master in Excel or Statistics to perform predictive modeling!

I highly recommend going through the below resources to get the hang of Excel:

• Collection of 25+ free Excel articles and tutorials
• Free course on Excel Formulas and Functions

Work Experience Questions – How to Leverage your Previous Work Experience in Data Science

I’m sure a lot of you would’ve been waiting for this section. I can’t tell you how many times I’ve heard transitioners ask:

“How can I use my previous work experience in data science? Will it even count?”

It’s a valid question! You’ve worked hard to be in your current position so you should do your best to use that to find a data science role. But here’s what we want you to know – that might not always work out for you.

The data science industry will only consider your previous work experience if it’s related to this field or if you’re coming from the same domain. So in this section, we’ll aim to answer key questions transitioners have about using their previous work experience.

The Big Question – Will Your Previous Work Experience Translate to the Data Science Field?

You have a solid 5-10 years of experience in *some* industry. You are a well-respected professional who’s calling the cards. But you’ve recently become enamored with data science and all it can do for your business and career. You can’t wait to bring all that experience to your new field.

Sounds familiar? Good. But if you are of the thought that your entire experience will translate to your new role, I suggest you re-think.

There are two sides to this story:

• You are changing your domain entirely to get into data science
• You are sticking to your previous domain, but are looking for a data science role

Let’s understand the implications of each of these points.

Changing your Domain Entirely

If you are changing your domain entirely (for example, coming into data science after years of software testing), your work experience will most likely count for nothing. Not only are you switching to an entirely new line of work, you are also looking for a new role. When the recruiter looks at your resume, the first thought is – “what value will he/she add to the organization/project?”. Unfortunately, the answer in this case is usually close to zero.

Why? Because as a newcomer, you don’t have any experience of how that domain works. When you’re given real-world data, how will you work with it if you don’t understand how those features impact the final decision?

This is a reality most people skim over or prefer not to face. It’s an entirely wrong way to go about your career switch and will only end up harming your prospects. Understand the situation, talk to people who have made this switch, and align your expectations accordingly. Going in blind into such a big decision is a one-way ticket to failure.

Staying in the same Domain

Coming to scenario #2 – what should you expect if you’re staying in the same domain but switching to data science? Then your prospects look a lot sunnier. You have the added advantage of knowing the industry. You should already be aware of the nuances present in the domain so you will understand the data you’re working with. That is a MASSIVE benefit. The recruiting manager will factor that into the final decision.

I highly recommend the second scenario if at all possible. Stay in the same field you have always worked with and understand how you can apply data science there.

This article by Kunal Jain contains plenty of practical tips and tricks to help you overcome a lack of experience in this field.

So Then How Do I Get Recognized by Data Science Recruiters?

1. Build your GitHub profile – Github is the place where you keep your projects. Other people can go through your project, add improvements, and so on. It is a great place to get recognized by critical people and network with them. Start your project and upload it to Github. This will help you in building a strong foundation.

2. Keep updating your resume – It is a natural tendency for humans to go towards perfectionism but it can be harmful. Instead of adding Python, machine learning, and SQL together in your resume with half-baked knowledge, it is advised to add skills one-by-one after perfecting it. For example, add Python when you are comfortable with it and only then move on to machine learning.

3. Participate in competitions – Data Science competitions are a sure shot way to improve your performance as a data scientist. Although it may take you a while to get adjusted, it will help you in the long run. You can go on the DataHack platform and pick a problem statement of your choice and get started. Recruiters love the candidates who have built their knowledge through practical applications.

4. Start writing articles – If you have a knack for data science and a passion for writing then what is a better way to express yourself than writing articles? Article writing helps you learn all the hard technical concepts and turn them into easy-to-grasp topics. Article writing is another great way to help you catch the eyes of potential recruiters.

I’m a software engineer by profession. How can I transition into data science?

Let me take a very specific example to illustrate what you can expect during your transition.

“To start with, if you really enjoy software engineering, then you should consider becoming a data engineer or a machine learning engineer.”

How about an Application Developer?

I’ll lean on an excellent answer by Ankita Ghoshal that we featured here. This is her very valid take on how application developers can transition into data science.

• How I became a Data Scientist after working for 10 years in the IT Industry

I’m from a reporting/MIS/Business Intelligence background. How can I transition to Data Science?

I regularly encounter talented business intelligence (BI) professionals looking to land their first data science role. They are often frustrated by the perceived lack of opportunities for them. A lot of them feel that their role is repetitive, or they just need to perform whatever has been asked from them.

They actually miss the fact that they are closer to data science opportunities than any other professional out there.

Business Intelligence (BI) professionals hold a massive advantage over almost anyone trying to transition into data science because of the following reasons:

• BI professionals already have access to data scientists in various projects
• Existing know-how of how to manage and handle data (at times at scale)
• BI professionals have the business context and they work closely with business.
• They have experience with basic data exploration steps as very often business asks for these in addition to the reports they use.

In other words, these folks work in the “first half” of a data science project. That’s already more industry experience than most aspiring data scientists!

Check out this in-depth explainer that answers this exact question in a step-by-step manner:

• 11 Easy-to-Achieve Steps to Transition into Data Science (for Reporting and BI Professionals!)

Data Science Interview Tips for Transitioners

Here comes the make or break moment for most data science transitioners. You’ve worked hard on your skillset, polished your technical and soft skills, and feel you’re ready to break into data science.

Now comes the interview round – the key to the portal that holds your data science career. For the scope of this article, we will also consider resume preparation as part of the interview process. After all, if your resume isn’t up to scratch, landing your dream data science role will feel like a distant dream!

So in this section, we will aim to answer common questions transitioners have about the data science interview process.

How Do I Create my Data Science Resume?

The million-dollar question! An impactful resume is sure to land you an interview while a poorly designed resume will almost always end up in the rejection pile.

Given that recruiters have to sift through hundreds of resumes in a day, how can you make your CV stand out?

Here’s the good news – crafting the perfect data science resume is a skill you can learn! Once you know how to expertly update your resume, you’ll be able to effectively market your skills when applying for your next data science job.

If you’re struggling to land or clear data science interviews, we have curated the perfect course for you. The ‘Ace Data Science Interviews‘ course is an amalgamation of our combined experience of taking hundreds of interviews to help you land your dream data science role.

A good way to think about your resume is to look at it as real estate.

This is a very intuitive way of crafting your resume. Let me explain. In any house, you have a fixed area and a floor plan to work with. You need to make sure that things fit in neatly in whatever space that is available to you.

Similarly, your resume has limited space which you should use judiciously and tell your story effectively.

There are broadly 4 key areas where you should focus on:

• Structure of your Data Science Resume
  • What is the right length of the resume?
  • Create Differentiated Areas
• Adding Content and Information to your Data Science Resume
  • Information Prioritisation
  • Make your Content Crisp and Clear
• Get Feedback from Industry Experts
• Build your Digital Presence

Want to understand more about each point along with examples? Head over to this wonderful article:

• How to Build an Effective Data Science Resume? 4 Key Aspects that Will Make or Break your Application

Key Things to Keep in Mind for Data Science Interviews

Getting a break in the data science field can be difficult. Doubly so, if you’re coming from a non-data science background (which in all likelihood you are).

The stories you hear from other aspiring data scientists can make interviews feel more intimidating and daunting. So you better be prepared before facing the interviews.

What kind of questions can be asked? How can you prepare and what are the resources you should refer to? What is the structure of a typical data science interview? How should your body language be? These are just some of the questions you’ll have in mind.

There are 7 key areas we want you to focus on:

• Be thorough with your resume
• Study up on your data science projects
• Practice solving puzzles – a key data science skill!
• Prepare to face case studies for data science roles
• Research the job profile and the organization
• Review confusing data science terms
• Brush up on your database, programming, and software engineering skills

Here’s an outstanding article that covers these points in-depth plus gives you practical advice + resources to combat each point:

• 6 Key Points you Should Focus on for your Next Data Science Interview

What Does a Typical Data Science Interview Process Look Like?

Additionally, you should be aware of how a typical data science interview process goes.

We have put together a 7 step process that starts right from the stage you start researching the different roles that interest you. And it goes all the way up to the completion of in-person interviews.

Keep in mind that this is a comprehensive framework. You might not have to go through each and every step in your interview journey.

1. Understand the Different Roles, Skills, and Interviews
2. Getting Ready for Interviews – Build your Digital Presence
3. Prepare your Resume and Start Applying!
4. Telephonic Screening
5. Getting through the Assignments
6. In-Person Interaction(s)
7. Post-Interview Steps

You can read more about this process in our illustrated guide here.

Common Myths About Data Science Transition You Should Avoid

Transitions into data science are tough, even scary! And it is not because you need to learn maths, statistics, and programming. You need to do that, but you also need to battle out the myths you hear from people around you and find your own path through them!

Stop us if you’ve heard this before:

“You need a Ph.D to have a chance of becoming a data scientist. Two is even better!”

 

“Participate in data science competitions, that will tell you how the industry works.”

 

“You need tons of computational resources to build deep learning models. You can only get that at the top tech firms.”

These myths often make you feel like only geniuses can work in data science. This is just not true. Whether you’re a recent graduate, an experienced professional, or a leader, it’s important to understand how data science works and you will find your place in the industry.

Here are the most common myths you should avoid at all costs:

• A Ph.D. is mandatory to become a data scientist
• A full-time data science degree is a must for making the transition
• All your previous work experience will translate to the data science domain
• It’s necessary to have a computer science/mathematics/statistics/programming background
• Learning a tool is enough to become a data scientist
• Deep learning requires computational power that only top companies have
• Once built, AI systems will continue to evolve and generalize by themselves
• Data scientist is the only job in AI
• Data science is only about building predictive models
• Participating in data science competitions translates to real-life projects
• Data collection is a breeze, the focus should be on building models

You can check out the full list along with how you can avoid these myths in your own transition journey:

• Busted! 11 Data Science Myths You Should Avoid at All Costs

Common Mistakes Data Science Transitioners Make and How to Avoid Them

If you are from a non-technical and non-mathematical background, there’s a good chance a lot of your learning happens through books and video courses. Most of these resources don’t teach you what the industry is looking for in a data scientist.

This is one of the reasons why aspiring data scientists are struggling to bridge the gap between self-education and real-world jobs. Here, I have listed down the top mistakes amateur data scientists make (I have made some of them myself):

1. Learning Theoretical Concepts without Applying Them
2. Heading Straight for Machine Learning Techniques without Learning the Prerequisites
3. Relying Solely on Certifications and Degrees
4. Assuming that what you see in ML Competitions is what Real-Life Jobs are Like
5. Focusing on Model Accuracy over Applicability and Interpretability in the Domain
6. Using too many Data Science Terms in your Resume
7. Giving Tools and Libraries Precedence over the Business Problem
8. Not Spending Enough Time on Exploring and Visualizing the Data (Curiosity)
9. Not Having a Structured Approach to Problem Solving
10. Trying to Learn Multiple Tools at Once
11. Not Studying in a Consistent Manner
12. Shying Away from Discussions and Competitions
13. Not working on Communication Skills

You can check out the entire comprehensive article below. I have also provided resources wherever applicable with the aim of helping you avoid these pitfalls on your data science journey:

• 13 Common Mistakes Amateur Data Scientists Make and How to Avoid Them?