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A Successful Data Scientist Career Path – A Full Guide
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22nd Sep, 2023
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It’s not a hidden fact anymore that today’s economy is shifting increasingly toward analytics & data-driven solutions/decisions. Organizations, businesses & governments have spent recent years collecting & mining huge amounts of data. Data scientists nowadays play a very crucial role in the success or failure of any organization, and that’s why it won’t be far-fetched to say, “There is a data scientist behind every big successful company”.
A career in data science is exciting, fun, interesting, forward-looking, and rewarding. Importantly you don’t really require an established degree or specific educational background like other traditional jobs to start your journey in Data Science. You simply need the right skills, somewhat related experience, and a curious mind. Current market trends show that data science course fee is increasing, given the demand for data scientists.
In this guide, I’ll walk you through the ins and outs of the data scientist career path and the skills required for the same. Additionally, I’ll share some lights on how to decide which data science career is right for you.
Why Choose a Career in Data Science or Analytics?
Data science was hailed as the “Sexiest job of the 21st century”. I’m sure this in itself is quite a big factor for you to choose data science as a career. Nowadays, any business, big or small, is always on the hunt to find people who can comprehend and deconstruct data.
Choosing a data science as a career means respecting the various disciplines on which data science as a field has been built, such as statistics, maths & technologies, etc. The diversity of skills needed to become a data scientist can be seen as an asset.
Now, let me draw your attention to a few important factors for why you should choose data science as a career.
Impressive salaries.
Exceptional growth & demand in the market.
Endless career opportunities.
Constant challenging work or NO boring work.
Part of industry is changing human lives in every aspect.
high prestige.
Be part of the future.
Data Science has shown the capability that it can transform industries and our society. With a limited supply of specialized professionals in Data Science and rapid demand, it has become a lucrative career.
Prerequisites for Becoming a Data Scientist
Even though the data science domain open-heartedly welcomes professionals from various backgrounds & experiences, it’s worth knowing some prerequisites of becoming a data scientist before deciding to move to data science as a career option. These prerequisites are not mandatory to have. However, it will indeed make your data science journey simple.
Unconditional love for data.
Analytical mindset and quest for logical reasoning.
Hands-on with computers & programming languages.
Familiarity with Maths, Statics & Linear Algebra.
Understanding of business operations.
The attitude of a lifelong learner.
Excellent command of communication
What Are the Different Career Paths for Data Scientists?
Data science is a broad field that beholds a variety of different paths and career options within it. It’s quite natural if you’re confused or unsure what each role is about or which career path is more suitable for me.
You’ll not find a clear distinction between these roles in the industry. Hence I’ll try to explain the different data science career paths you’ve within data science and what each one of them means.
Let's explore them. !!
Data Analyst
This role is usually considered “Entry level” in the data science domain. A Data Analyst’s role is to collect information from various sources and, analyze its patterns & present it to stakeholders in an intuitive way.
Data analyst transforms and manipulates large data sets to match the requirement of the companies. A data analyst recommends different methods and techniques that can help a company improve the quality of data systems.
Data Scientist
American mathematician and computer scientist DJ Patil defined the role of a data scientist as “A unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.” Data scientists also architects & build machine learning or deep learning models for prediction, find patterns and trends in data, visualize data, and even pitch in with marketing strategies.
The data scientist also deals with stakeholders to understand business problems and data at hand and share analysis & findings with them in the most effective way.
Data Manager
Data Managers are the ones who’re responsible for building & managing systems around data as per the specifications from Data Architects. Their main focus is to organize & store data with attention to security and confidentiality. Data Managers work hard to ensure that information flows timely and securely to and from the organization as well as within.
Data Architect
Data architects create a blueprint for all the data management systems. The company’s every system & infrastructure related to data needs to be built and maintained by identifying all possible structural and installation solutions. Data architects are responsible for ensuring their company’s data solutions are built for performance & scalability and also to design analytics for multiple platforms.
Data Engineer
This is yet another very famous career path for a Data Scientist. A Data Engineer is responsible for creating, nurturing & managing data pipelines that help in making information available to data scientists at all times. They are also responsible for creating new & advanced solutions to support the increased data complexity & variability. These people work closely with front-end and back-end developers, product managers, and analysts.
Business Analyst
Business analysts are closely related to Data analysts, with fundamental differences in the way they operate and function. Business analysts are experts in business domains, their functions & processes; hence they analyze & prepare actionable insights by diving deep into it. Business analysts often assist data analysts by providing them with business insights, domain expertise, etc.
Machine Learning Engineer
Machine learning engineers are often one level down the line than Data scientists. The primary responsibility of an ML engineer is to write code and create data funnels & pipelines for Machine learning applications. They typically need strong programming skills, as well as a knowledge of software engineering. In addition to designing and building machine learning applications, ML engineers are responsible for model testing & model deployments.
Statistician
As the name suggests, a statistician has a very strong eye for detecting patterns in data & creating statistical solutions out it. They are mathematics and statistics experts who apply statistical methods to solve real-world problems.
Data Modeller
Data modelers are computer systems engineers who design and implement data modeling solutions using relational, dimensional, and NoSQL databases. They work closely with data architects to design bespoke databases using a mixture of conceptual, physical, and logical data models.
Freelance Data Scientist
Freelance data scientists are Data scientists only; however, they aren’t associated with any specific organization. They generally work independently and have a small team with them. In terms of skills, there isn’t any difference between data scientists. However, freelance people need to be more outspoken.
Clinical Data Manager
This role is tightly associated with the healthcare industry. Clinical data managers are responsible for collecting data from a variety of medical research projects, such as clinical and pharmaceutical trials. They work collaboratively to make sure data is collected, managed, and reported clearly, accurately, and securely.
Marketing Analyst
As guessed, this role works with the specific function of business, and that’s marketing. Marketing is the most cost-sensitive and intensive function of any business. In this connected world, how you market your product or services greatly impacts your overall business. This analyst helps you in designing effective methods regarding how to do marketing with marginal cost.
Big Data Developer
Big Data is yet another important technology in the arena of data science. This field largely deals with managing hundreds and thousands of petabytes of data in a secure and easy-to-access manner. Big data developers are technically savvy individuals with heavy knowledge of computer architecture.
Director of Data Science
This is a leadership role in the field of data science. The Director of Data Science leads the entire Data Science team. The Director of Data Science will lead the department’s engagement with business stakeholders and executives and partner with these stakeholders and executives in enhancing the existing data management methodologies and developing new approaches and methodologies.
Machine Learning Scientist
Research new data approaches and algorithms to be used in adaptive systems, including supervised, unsupervised, and deep learning techniques. Machine learning scientists often go by titles like Research Scientist or Research Engineer.
Business Intelligence (BI) Developer
BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end user’s understanding of their systems.
The Data Science Career Path: Skills Required for Data Scientists
By now, we know from Apple, Google, Twitter, Spotify, Swiggy, and Ola that everyone wants to get supremacy in Data Science and Machine Learning. There is no denying the fact that Data Science is one of the fastest-growing fields.
However, the data science industry has a massive shortage of skilled data scientists! Even though jobs in the field of data science are seeing a continuous upward trend, there is a noticeable shortage of data scientists with the right skills.
Below is the list of skills you ideally require to become a successful data scientist. Now gaining all of them is a long and difficult process, but it’s not impossible. With time & dedicated practice, you can learn and master them.
Understanding of fundamental concepts of Data Science
You can only become a master of the field if you know its roots & fundamentals of it. Hence it’s pivotal you understand the basics of the field.
Statistics
Machine Learning algorithms are created on the backs of statistics and mathematics. You need to have a good grasp of elementary-level of statistics and maths. You don’t need Ph.D. or masters in statics, but a general understanding is a must.
Programming knowledge
In order to instruct computers to convert your analysis into action, you need to have exceptional programming skills. You’ve to love computers and their language. In industry most widely used language is Python, so you need to be a master of Python. Apart from Python, you must learn other languages too, such as R, C, C++, Shell Scripting & SQL. These languages play a crucial role in your journey as Data Scientist.
Data Manipulation and Analysis
You need to have an experimental mindset, which will allow you to find & explore different ways to manipulate available data and extract the most juice out of it. In order for you to do this, you need to learn various data pre-processing operations & you can start this with SQL, which is an essential requirement of the Data science journey.
Data Visualization
The saying “Picture says 1000 words” is ideal for data science. You need to create effective and impactful graphs/charts of data that convey the pattern by themselves. There are various paid & free tools available in the market for you to pick from. Some examples are PowerBI, Tableau, QlikSense, etc. You can also try open-source libraries of Python like Matplotlib & Seaborn.
Machine Learning
ML is the heart of Data Science. So you need to grab exceptional knowledge about different types of algorithms, how they work on supplied datasets, how you evaluate the effectiveness of algorithms, and finally, which algorithms to use and when.
Deep Learning
Deep learning is an advanced version of Machine learning, which draws its inspiration from the human brain; for complex use cases and datasets. To be a good Data Scientist, you need to learn and understand the complex concepts of Deep learning.
Big Data
The size & variability of data have changed a lot since the last decade. Data scientists are expected to understand the journey of data and how effectively we manage it for any task.
Software Engineering
The skills of application building come really handy during envisioning the end-to-end working of any ML application. You understand how data & operations will proceed from one stage to another.
Model Deployment
Building an accurate model is just one part of the process. You need to have skills in order to put this model into action. You need to learn and execute different strategies for deploying your model in real-time production systems.
Communication Skills
I rate this skill as THE MOST important skill to have, as you’ve to communicate your findings and analysis in a really simple and effective way to a broader audience who generally don’t come from technical or data solution backgrounds.
Structured Thinking
Data science is a field of experimentation. Hence you need to learn and apply a clear & structured thinking process in evaluating and experimenting with different approaches. You’ll be lost if you plan to progress on random plans.
Curiosity & Learning attitude
As the field of data science is continuously evolving, every day we’re witnessing a tremendous amount of progress. So in order to keep yourself on top of the game, you need to learn & apply new things on a daily basis. Therefore you must be open to learning new things.
By no means is this a perfect guide or exhaustive list of skills needed for you to become a Data Scientist. However, you can consider this as a base, and in the process of learning these skills, you’ll encounter and learn new skills.
Recent Job Growth Statistics in Data Science Careers
If you’re here, I am assuming you’ve decided or are on the urge of deciding a career path for yourself. Let me draw your attention to few other important factors, which can help you decide further.
In 2022 alone 137,000+ open jobs were available in the field of data science. You can also witness growth of stunning 47% in analytical jobs compared last year.
Its indeed in great demand.
Above statistics demonstrates the growth & demand of data science professionals across different business domains, geographical locations and even experience ranges. With more organizations adopting data based solutions, we’re going to witness continue upward trend in the demand of data science jobs.
So be rest assured, you’re on right path !!
Is Data Science Right for You?
In my opinion, it’s really important & pivotal to obtain an answer to this question before you decide to pursue your journey in data science. Unfortunately, many articles on the internet imply that the field of data science is full of demand, high salaries & respect. However, the reality is; that your path to data science is not at all easy; it requires continuous learning & unlearning of complex topics & concepts of multiple fields, you need to be technically savvy during your career.
In this section, I will help you with some pointers which will eventually lead you to the answer to this question. Fundamentally, anyone can learn & practice any data science skills if he/she is really dedicated to it.
Simply put: if you want to learn data science, you can learn data science.
Which Data Science Career is Right for You?
Choosing the right data science career path may not be straightforward; however, if you follow your instincts, do a self-evaluation with respect to the required skills for each path and marry them with your aspirations, I am sure you’ll make the right choice.
Below are the 9 questions/statements, and I need you to honestly put True/False against each one of them If most of them are True – plus you are ready for hard work – that means that you could become a great Data Scientist!
Do you like to analyze things?
In general, if you’re interested in analyzing the rational side of everything – and not just the emotional – that’s a good sign that you would enjoy data science.
Example: When you read news on market, do you only read the story, or do you check statistics, too?
Do you enjoy mathematics and statistics?
Numbers & calculations are at the heart of a data science career. You don’t need a Ph.D. in maths or statistics, but you need to have a genuine love for numbers, maths, and statistics.
So, if you like maths and stats, that’s another indicator that you would enjoy being a data scientist!
Do you have business thinking?
It's tricky but equally important to understand that, even if you’re great at numbers, those numbers will only make sense when it has a business story or justification. You need to dive into various business aspects and understand how the business operates at each level. Once you have that visibility, you can convert numbers into actionable insights.
If you prefer being practical and business-minded? Then you are on the right track.
Do you like computers & coding (and watching unfriendly screens for hours)?
In order for a data scientist to convert his thoughts (analysis, ideas) into actions (algorithms, predictions), he/she needs to write computer programs/scripts. A data scientist spends a large portion of every day in front of a computer writing scripts/programs for analyzing the data or validating assumptions or for even evaluating new ideas & experiments.
If you can imagine yourself coding for long hours and you actually love to do that, that’s another green signal for you.
Do you like working in cross-functional teams?
As data scientist, you’ll largely engage with non-data people from various functions like sales, marketing or compliance. One of your job will be to listen them carefully and understand their mind-sets & problems related to business. Sometimes it can annoying too.
But if working with marketer or a strong-willed leader is not your thing, then most probably you wouldn’t enjoy your career, either.
Do you love to communicate in a simple yet meaningful way?
The most important part of your job will be to present your analysis & findings to a larger audience who are essentially decision-makers but don’t share data literacy of your level. Hence, you’ve to make stories from the insights you obtain & present it in a simpler and present them in the most effective way to them. In other words, you have to be a good storyteller in order to be a great data scientist!
If you’re a good communicator, then, you can take it as another good sign.
Are you a life-time student?
Data-science is THE most evolving field in today’s time, where we are witnessing significant development & progress every single day. In order for you to be relevant and most effective in your role, you should have an attitude of learning, unlearning and learning again. You’ll have to dedicatedly spend your time, for keeping an eye on new developments in the field and making yourself aware of new tools, technologies or frameworks.
Thus learning is the #1 thing you have to enjoy if looking for a data scientist career.
Am I a team player?
No great success has been achieved without a great team. Similarly, data-science projects are complex, lengthy, confusing & with full of experiments. You need to have temper and empathy to work with large groups of people with different skills like data engineer, DevOps, leaders, domain experts etc.
If you think you’re this kind of person, put one more True for yourself.
Am I Ethical?
“With great power, comes the great responsibilities”. When you start playing with data & advanced algorithms, you may feel overwhelmed and realise in which all ways you can manipulate things. Hence you need to be ethical and make sure you don’t get involved in any fabrication of data or wrong doing.
We can clearly see, a great data scientist requires combination of skills from variety of domains. We can improve these skills with practice, but the biggest part is “Do you want to”. If yes, You can start your journey with applied data science with Python course.
I hope this little quiz gave you clear picture and can help you determine the answer.!! As a suggestion, you can refer KnowledgeHut applied data science with Python, which dives deep into every aspect of data science career path.
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Conclusion
Data science is the most demanded job of this decade and will be even for next. With growing awareness of the field, competition in securing jobs between professionals is also at its peak. If you follow this guide and do genuine self-evaluation, I am sure you’ll make the right choice in choosing the optimal path for you.
Just remember, Choosing the right career path is just a start; your journey starts from there.
Frequently Asked Questions (FAQs)
1. Is Data Scientist a Good Career Path?
Data science is considered as “sexiest job” of the 21st century. Moreover, the recent trend regarding job opportunities and salaries of data scientists makes it evident that it’s one of the best career paths; if you’ve what it demands.
2. What Is the Career Path of a Data Scientist?
In this guide, we’ve discussed every aspect of the data science career path in length. It basically revolves around the skills you require to get into this career and what are the various options you’ve.
3. How Do I Start a Career in Data Science?
I guess doing self-evaluation and ensuring you possess the basic skills and interests to get into data science is a good starting point. Post that, you may enrol in various courses and acquire formal education.
4. Is Data Science Still in Demand in 2023?
Yes, indeed. Data science as a skill & career has been in good demand since early 2012. The trend is linearly going upwards and with more & more companies investing heavily in their digital transformation & data solutions strategies, we will see more demand for the job.
5. Can a Data Scientist Become a CEO?
There are no specific roadblocks or checklists for data scientists to become a CEO. However, they have to prove their skills in a lot of areas such as management, operations, business strategies & deep knowledge in financial management. There are already tons of example in the real world where data scientist has become CEO.
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