Making a Data Science Career Transition? You Need to Know How to Overcome Key Challenges
“I have multiple years of work experience in a non-data science field. I want to transition into data science now. Can I make the transition? And will my previous work experience count?”
Does that question sound familiar? This is one of the most common questions we’ve been asked by data science transitioners over the years.
These aspirants see the allure of a data science job and start working towards the transition journey without covering the basics. A half-baked attempt or an unstructured approach towards learning is one of the most common traps data science transitioners tend to fall into.
We’ve interviewed hundreds of data science aspirants over the years and they seem to struggle with similar key transition challenges. Despite all the resources available these days, there are common obstacles aspirants always come up short against.
Stop us if these sound familiar as well:
- I don’t know any programming language. I’m destined to fail in data science
- I come from a non-technical field so I’ve been told I can’t transition into this field
- I have 10+ years of experience and I keep getting rejected by recruiters
- I’ll have to take a hefty pay cut to transition into data science
- I don’t know any math or statistics so should I even bother?
So how do you actually make the transition? Is it worth the nights you’ll spend in from of your computer?
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!
And if you are looking for a structured learning path or simply don’t have the time to go through this page, we have the perfect roadmap for your data science transition journey:
How this Data Science Career Resource is Structured
We have divided this resource into 4 categories for ease of reference. These are broadly 4 CRUCIAL questions you need to answer before taking the leap. Each question is then further divided into more granular questions to help you reach the answer that works for you. We even have a bonus section for you at the end!
Click on the one that you’re looking for:
- What is Data Science and the Spectrum of Data Science?
- Why do you want to Transition into Data Science?
- Should you Transition into Data Science?
- How can you Make a Successful Data Science Career Transition?
- Data Science Transition Success Stories!
1. What is Data Science and the Spectrum of Data Science?
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!
But before you dive into the granular details of what you need to cover to make your own data science career transition, you should first spend some time understand what data science actually is. And even more importantly, what is the spectrum of data science, and where you would potentially fit in.
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:
As you can see here, there is a LOT of value and a lot of roles under the data science umbrella!
2. Why do you Want to Transition into Data Science?
Yes, this is an actual question on a data science transitioner page. It might seem odd given that you’ve already chosen data science as your future profession.
Does it really matter what the reason behind making the transition is?
It absolutely does! The motivation behind switching to data science plays a key role in your eventual success. This motivation drives you to stretch yourself just that little bit more to achieve your dream role. It could make or break your potential career path and help you decide whether you are a good fit for data science in the long term.
So before we dive into the nuances of data science and look at the different aspects of what you would need to succeed, let’s cover our basics first and understand why you are looking for this transition in the first place.
We have seen so many people skip this step in their haste to break into this field and then eventually suffer rejection. Don’t make that same mistake!
2.1 Are you inspired by the impact and application of data science?
We saw the power of data science earlier. It is ubiquitous right now and organizations are accelerating the adoption of data science in every project possible.
We are seeing multiple industries and domains getting disrupted on a regular basis, from telecom all the way to manufacturing. Whether it’s an algorithm that can detect fraud in a matter of seconds or a technique that can predict the onset of an illness, data science is everywhere around us.
And the use cases are only going to go up in the foreseeable future!
This is an often-cited reason for changing roles and wanting to transition into data science. Data science is inevitably going to transform every function so you might as well get on the bandwagon in the early days and get a leg up on the competition.
Check out this awesome resource that lists down 13 popular data science applications you should be aware of:
2.2 Are you stagnant in your current role?
Ah – the good old professional growth ceiling. Most of us in our professional lives have felt at some point that we are at a crossroads in our careers. We’ve taken a certain job as far as we could and there’s no real learning or growth possible anymore.
That is a classic story of stagnancy hitting your career. And then you scamper around looking for new jobs that will fulfill the immense potential you have.
This is as good a reason as any to transition to data science. It is THE field to get into right now and if you can put in the disciplined effort to make the transition, you’ll find it a very fulfilling career move. Stagnancy is not something people complain about in the data science space!
2.3 Are you looking for a salary boost?
Ah, this is by far the most popular reason data science aspirants cite as their motivation for this transition.
Data Science has become a glamorous role over the years, and especially when HBR termed the role of the data scientist as the sexiest job of the 21st century. Today, the market size of data science stands at $38 billion and is expected to reach $140 billion by 2025! It is undeniably a high growth role.
That’s massive! And here comes the information you’ve been waiting for:
According to Glassdoor, the average pay scale of a data scientist is Rs. 900k per year in India whereas the average salary of a computer programmer is Rs. 400k per year.
That is the kind of scale we are talking about! So making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post transition. We will highlight that later on.
2.4 Do you enjoy problem-solving and can’t apply that in your current role?
This is another common reason people want to switch to data science. A lot of roles in the industry are routine-based where you are stuck updating spreadsheets all day or shooting emails and sitting in meetings where you don’t have much to contribute or learn.
Imagine going through 4-6 years of rigorous education, obtaining a degree or two, and then never using it again. Most people settle for that but a select few want to apply their problem solving skills in their work.
Data science is the perfect foil for your problem solving skills.
We’ve heard a lot of folks mention that they want to get back to using math in their daily lives. They want to apply their education in their daily role. If you feel the same way, data science is definitely the field for you!
2.5 Data Science is a natural extension my current job role (MIS/ Reporting/ Dashboarding/ BI)?
Ah, the good old reporting question. A lot of folks we interview and come across mention that they are already working with data in a way. It could be that you are using Excel to generate reports or using Tableau to build reporting dashboards.
If you are coming from that kind of background, making the transition to data science makes absolute sense. You already have a sense of how the data science field works, you just need to get a holistic overview of the different parts and what comes after the reporting aspect.
We will dive deeper into this particular role later on in this page and give you a comprehensive 11-step process to help you transition into data science. Keep reading!
3. Should you Transition to Data Science?
This might seem an odd question on this page. Aren’t you already sure you want to transition into data science?
We want to present a different twist here. Data science, as you must already know, is not everybody’s cup of tea. You need to have a certain skillset along with a lot of discipline to learn and carve out a career in this space.
So here, we’re going to pen down answers to 5 key questions you should know the answer to BEFORE you take the giant data science-level career leap. These questions will help you understand what skills and experience you should have to ensure your data science career transition is a smooth one.
3.1 Are you good at logical thinking? How good are your Numerical ability and problem-solving skills?
In the current scenario, getting your first break in data science can be difficult. Around 30% of analytics companies (especially the top ones) evaluate candidates on their prowess at solving puzzles. It implies that you are logical, creative, and good with numbers.
The ability to bring a unique perspective into solving business problems can provide you a huge advantage over other candidates. Such abilities can only be developed with regular practice and consistent efforts.
And trust us – having a logical thinking mindset with solid numerical and problem-solving abilities is absolutely key to achieve success in the data science spectrum. This isn’t a routine job! You’ll be relying on your soft skills a lot more than you might expect.
Let me give you a quick example to illustrate this. Let’s say you are sitting with your team and your clients and you’ve just been given a business problem. As a data scientist, you’ll need to perform quick and dirty back of the envelope calculations to see if some things should be analyzed first or can be removed. As long as they are 80% right, go ahead.
For example, if charge-offs have doubled within a month, it is unlikely to be driven by unemployment (unless you are aware of huge scale layoffs in the economy). Similarly, if the Credit limit increase only impacts 2% of your portfolio, the charge offs from this program need to increase by 50x, if portfolio risk has doubled, which is unlikely unless you have given free Credit without looking at the population.
In data science parlance, you need to have a structured thinking mindset.
Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, but it also helps by identifying areas that require deeper understanding.
Without structure, an analyst is like a tourist without a map. He might understand where he wants to go (or what he wants to solve), but he doesn’t know how to get there. He would not be able to judge which tools and vehicles he would need to reach the desired place.
So how do you develop these skills? Well, practice, practice, and more practice! Here are a few excellent resources to get your mind into analytics shape:
- 20 Challenging Job Interview Puzzles which every analyst should solve at least once
- Commonly asked puzzles in analytics interviews
- The art of structured thinking and analyzing
- Tools for improving structured thinking
3.2 Do you have programming experience?
Here’s the thing – there is no one size fits all approach to data science. 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. 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.
If you’re coming from the IT field and have prior programming experience, then you’re already several steps ahead of most people! You can use your programming skills as a bridge between IT and Data Science to structure your machine learning code more logically.
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?
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:
- Python
- R
- Julia
- Java
- C/C++
To understand what each language brings to the table and see their comparison, I highly recommend checking out this excellent resource:
3.3 Do you have experience in data-related roles (MIS/ Reporting/ Dashboarding/ BI)?
We 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 of them.
They actually miss the fact that they are closer to data science opportunities than any other professional out there!
Why transition into data science in easier for a Business Intelligence (BI) professional:
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 businesses
- 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!
If you are one such transitioner looking to jump from a BI / MIS / reporting role to data science, we have the perfect learning path for you below. You can consider these 11 steps as a roadmap you can follow. In fact, I would strongly encourage you to implement these steps in your current BI role. Start where you are and practice till you break into data science!
3.4 I have 10+ years of experience in a non-data science field. Should I transition to data science?
Ah, the oft-asked yet elusive question! We cannot tell you the number of times we’ve heard this one. Here are a few samples:
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, we suggest you spend the time to understand how this works.
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, but 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 to 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:
3.5 What is the potential time required to become a hands-on data scientist?
If you’re looking for a specific timeline – that’s not quite how this works. The answer is that the time varies from person to person. We’ve already understood that your previous work experience might play a part in your data science transition journey, right?
Well, that also plays a part in deciding how long it might take you to become a data scientist.
A lot of organizations have conducted polls around this and each poll has a different answer! One poll conducted last year by a respected organization concluded that it takes 5 years for a beginner to transition into data science. Another poll by a different pollster concluded it takes 3 years.
You can see why this varies from person to person right?
- Your previous work experience
- How much time you can dedicate to studying the various aspects of data science
- How good you are with logical thinking and number crunching
- If you’ve learned to program before
These are just the core questions that’ll help you come to a potential timeline. We at Analytics Vidhya put together a comprehensive roadmap at the start of every year, called a learning path, that takes you on a step-by-step journey to become a data scientist. That’s a one-year plan and assumes you can dedicate a significant amount of time to each topic (and each step).
Additionally, we have put together the most comprehensive data science program in the industry called the AI and ML BlackBelt+. This comprehensive certified program combines the power of data science, machine learning, and deep learning to help you become an AI & ML Blackbelt! Go from a complete beginner to gaining in-demand industry-relevant AI skills.
4. How can you Make a Successful Data Science Career Transition
So, having learned all the core aspects of making your data science transition, we come to the crucial step. How can you actually make this career transition? What are the key skills you’ll need to do this?
Let’s find out in this section!
4.1 First, what are the different roles in data science?
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:
I also highly recommend reading the below resource that goes into detail about each role we mentioned above:
4.2 Why you should build a strong programming base?
Machine Learning has seen a great jump only because of the boost in computing power. Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with being hands-on.
First of all, choose the programming language of your choice. Python, R, or Julia are to name a few and each has its own set of Pros and Cons.
Python is a general-purpose programming language having multiple data science libraries along with rapid prototyping whereas R is a language for statistical analysis and visualization. Julia offers the best of both worlds and is faster. If you are confused about which language to choose, here’s a resourceful article for you:
4.3 What skills do you need to be good at understanding and sharing insights from the data?
In simple words, Data Science is the field of converting data into insights. A good data scientist is the one who is able to unearth insights and communicate them well to the stakeholder. So what are the skills you will require here?
Statistics
It is said that statistics is the grammar of data science. Machine Learning starts out as statistics and then advances. Even the concept of linear regression is an age-old statistical analysis concept.
The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT, skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.
Statistics is a MUST concept to become a data scientist. You can deep dive into some of these concepts with these clear articles and their examples –
- Statistics for Data Science: What is Normal Distribution?
- Statistics for Analytics and Data Science: Hypothesis Testing and Z-Test vs. T-Test –
- Statistics for Data Science: What is Skewness and Why is it Important?
Machine Learning
For a data scientist, machine learning is the core skill to have. Machine learning is used to build predictive models. For example, you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.
You can start with a simple linear and logistic regression model and then move ahead to advanced ensemble models like Random Forest, XGBoost, CatBoost, and so on. It’s a good thing to know the code for these algorithms (which just takes 2-3 lines) but what’s most important is to know how they work. This will help you in hyperparameter tuning and ultimately a model that gives a low error rate. Here are some free courses to get you hooked –
- Fundamentals of Regression Analysis
- Ensemble Learning and Ensemble Learning Techniques
- Getting Started with scikit-learn (sklearn) for Machine Learning
Curiosity
Why did this happen? How did this happen? If I tweak this, will it affect the overall results? Continuously asking questions is one of the most crucial soft skills of a data scientist. If you are dull, you may follow all the steps of the machine learning project lifecycle but you won’t be able to reach the end goal and justify your result.
Data Science is still evolving and it let me tell you the most important thing – Learning never stops in this field. You master the tool one day and it gets run over by an advanced tool the next day. A data scientist needs to be curious and always learning.
Structured Thinking
This keeps coming up, doesn’t it? That’s because structured thinking really is THAT important for a data science professional.
Let us say that you want to become a data scientist – you will break this large goal into multiple parts like training, preparing your resume, applying for a job likewise the ability to break down a problem into multiple parts so as to efficiently solve it is Structured thinking.
A Data Scientist always looks at problems from different perspectives. This is an acquired skill but you can definitely work on it. Kunal Jain, Founder, and CEO of Analytics Vidhya has created a great course on it. You can check it out here:
4.4 Why do you need to have a good understanding of Machine Learning techniques?
As a data scientist, you must be able to understand the fundamentals of machine learning techniques such as regression, decision trees, ensemble learning models, etc. but you must also be able to explain them well.
Some of the questions asked in interviews go as follows:
- What is regression analysis?
- Can you write the equation for a simple linear regression model?
- Great! Can you show the regression chart in the choice of your language?
Let us understand the type of questions. The first question specifically targets your conceptual knowledge, whereas the second question goes deeper and tests your deeper understanding. The third question tests your concepts along with the programming skills.
Through these 3 questions, the interviewer was able to understand your core knowledge, programming skills, and communication skills.
Storytelling is one of the most crucial skills of a data scientist and for that, you must have a good hold of your machine learning concepts, communication skills, and structured thinking.
Here’s one of the most popular tutorials to get you started:
4.5 How much experience with solving real-life projects do you have?
“How many data science projects have you completed so far?” – This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question.
This is especially true if you’re a fresher or a relative newcomer to data science.
Data science (Machine Learning) projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it but you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications.
4.6 Why do you need to perform profile building activities like blogging, speaking at meetups, and participating in Data Science competitions?
Let us say that you are interested in cricket, you learn and practice cricket daily but how will you grow yourself? It won’t happen by practicing in nets daily! You must be recognized and get noticed for your talent by participating in a competition and getting in touch with potential trainers. Similarly, you must be recognized by potential recruiters and enthusiasts to grow yourself. Let us see how:
- 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.
- 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.
- Speak at meetups – Data science is a growing field and we have an ever-growing community, Many important data scientists, researchers, thought leaders attend them. These platforms are a great platform to grow yourself as a data science professional.
5. Data Science Career Transition Success Stories!
We’ve covered a LOT of ground in this page so far. Now, let’s dive into stories! That’s right – in this section, we will hear from folks who made the career transition to data science from various backgrounds, both technical as well as non-technical.
There is no better proof of success than hearing from these transitioners themselves on what challenges they faced, how they overcame them, and what their advice to other transitioners is. Honestly, this section is packed with wisdom – so soak it all in and use it in your own transition journey!
5.1 Landing a Role as a Data Science Analyst from a Software Developer
This story is for all of you who are currently working in the IT sector as software developers. This is one of the most common job functions we’ve seen who want to transition into data science.
This is a really comprehensive story with a step-by-step breakdown of the author’s journey:
5.2 Becoming a Data Scientist after 8 years working as a Software Test Engineer
Another inspiration tale from the lands of software engineering! This is a slightly shorter version but offers a different perspective from the above article. Soak in all the wisdom!
5.3 Becoming a Data Scientist after working for 10 years in the IT Industry
Ah, this is for all of you who have significant experience in IT and are wondering if you can transition into data science. The short answer – of course you can! And this story serves as evidence (and inspiration) of how you can make the leap yourself.
5.4 Transitioning to Data Science after 6 years in Data Warehousing
Do you have experience with data but aren’t sure if it’s relevant for data science? Then this story is for you! The author was under the impression that data can be used only for presenting or analyzing what had happened. He did some research on Analytics and Predictive Modelling and realized that even though it is an extension of BI it requires different skill sets, i.e. Statistics, Machine Learning, SQL, and Business acumen in the industry one is working in!
5.5 How to Become a Machine Learning Expert in 10 Months
The title might sound catchy but this article is a comprehensive step-by-step breakdown of how this person managed to overcome barriers to transition into a machine learning role. There is a lot of advice for transitioners and even beginners.
5.6 Inspiring story of Deepak Vadithala – from a Paper delivery boy to a Lead Data Engineer & QlikView Luminary
This is the most popular story we have ever posted on Analytics Vidhya. The heading says it all – this is a unique journey and a truly inspirational one for anyone who is wondering is data science is for them. A must-read!
Final Thoughts
Take a deep breath and congratulations on making it to the end of this comprehensive knowledge-packed resource! This is not an exhaustive list of questions transitioners ask but it does cover the absolutely KEY questions you should be able to answer in your journey.
From understanding the skillset you need to knowing how to present your previous experience, you should be aware of how to showcase your talent and your passion for data science.
To summarize, we covered the below four questions every data science career transitioner should know:
- What is Data Science and the Spectrum of Data Science?
- Why do you want to Transition into Data Science?
- Should you Transition into Data Science?
- How can you Make a Successful Data Science Career Transition?
Analytics Vidhya also has a comprehensive industry-relevant program called the AI and ML BlackBelt+ that combines the power of data science, machine learning and deep learning to help you become an AI & ML Blackbelt! Go from a complete beginner to gaining in-demand industry-relevant AI skills.
