Consultant with 11+ years of experience in Technology & Services. I bring customer-centric mindfulness that enables firms to innovate and thrive. Certified in Data Science, Machine Learning, Artificial Intelligence & Alteryx
It’s not hidden fact anymore that today’s economy is shifting increasingly toward analytics & data-driven solutions/decisions. Organizations, businesses & governments, have spent recent years in collecting & mining huge amounts of data. Data-scientist’s now a days plays very crucial role in 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 established degree or specific educational background like other traditional jobs to start your journey in Data Science. You simply need right skills, somewhat related experience and curios 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 data scientist career path and skills required for the same. Additionally, I’ll share some lights on, how to decide which data science career is right for you.
In my opinion, it’s really important & pivotal to obtain answer to this question before you decide to purse your journey in data science. Unfortunately, many articles on internet implies that field of data science is full of demand, high salaries & respect, however reality is; your path to data science is not at all easy; it requires continues learning & un-learning of complex topics & concepts of multiple fields, you need to be technical savvy during your career.
In this section, I will help you with some pointers which will eventually lead you to the answer of this question. Fundamentally I believe, 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.
Below are the 9 questions/statements and I need you to honestly put True/False against each one of them and 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 analyse things?
In general sense, if you’re interested in analysing 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 you check statistics, too?
Do you enjoy mathematics and statistics?
It goes without saying that, numbers & calculations are at the heart of data science career. You don’t need PhD in maths or statistics but you need to have 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?
Its tricky but equally important to understand that, even if you’re great at numbers; those numbers will only make sense when it has business story or justification. You need to dive into various business aspects and understand how business operates at each level. Once you’ve 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 screen for hours)?
In order for data scientist to convert his thoughts (analysis, ideas) into actions (algorithms, predictions), he/she needs to write computer programs/scripts. Data scientist spends, large portion of everyday in front of computer writing scripts/programs for analysing 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 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 simpler and present it in most effective way to them. In other words, you have to be a good story-teller in order to be a great data scientist!
If you’re 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’ve 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 framework.
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 group 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.!!
If you’re here, I am assuming you’ve decided or on the urge of deciding career path for yourself. Let me draw your attention to few other important factors, which can help you decide the further.
In 2021 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 !!
Data science was hailed as “Sexiest job of 21st century”. I’m sure this in itself is quite a big factor for you to choose data science as a career. Now a days, any business, big or small, is always on 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 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 towards few important factors for why you should choose data science as a career.
Data Science has shown the capability that it can transform industries and our society. With limited supply of specialized professionals in Data Science and a rapid demand, it has become a lucrative career.
By now we know from Apple, Google, Twitter, Spotify, Swiggy, ola and everyone wants to get supremacy in Data Science and Machine Learning. There is no denying to the fact that Data Science is one of the fastest-growing field.
However, In industry we’ve massive shortage of skilled data scientists! Even though the jobs in the field of data science is seeing continues upward trend, there is noticeable shortage of data scientists with the right skills.
Below here, 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 surely 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 master of the field if you know the roots & fundamentals of it. Hence it’s pivotal you understand the basics of the field.
Machine Learning algorithms are created on the backs of statistics and mathematics. You need to have good grasp on elementary level of statistics and maths. You don’t need PhD or masters in statics but general understanding is must.
In order to instruct computers to convert your analysis in action, you need to have exceptional programming skills. You’ve to love computers and its language. In an industry most widely used language is Python, so it goes without saying, you need to be master of Python. Apart from Python, you must learn other languages too such as R, C, C++, Shell Scripting & SQL. These languages play crucial role in your journey of Data Scientist.
Data Manipulation and Analysis
You need to have experimental mindset, which will allow you to find & explore different ways to manipulate available data and extract the most juice out 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 Data science journey.
The saying “Picture says 1000 words” is ideal for Data science field. You need create effective and impactful graphs/charts out data, which conveys the pattern by themselves. There are various paid & free tools available in market for you to pick. Some examples are PowerBI, Tableau, QlikSense etc. You can also try open-source library of Python like Matplotlib & Seaborn.
ML is heart of Data Science. So you need to grab exceptional knowledge about different types of algorithms, how do they work on supplied dataset, how do you evaluate the effectiveness of algorithms and finally which algorithms to use and when.
Deep learning is an advanced version of Machine learning, which draws its inspiration from human brain; for the complex use-cases and datasets. To be good Data Scientist you need learn and understand the complex concepts of Deep learning.
The size & variability of data has changed a lot since last decade. Data scientist are expected to understand the journey of data and how effectively we manage it for any tasks.
The skills of application building comes really handy during envisioning end-to-end working of any ML application. You understand how data & operations will proceed from one stage to another.
Building 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.
I rate this skills as THE MOST important skills to have, as you’ve to communicate your findings and analysis in real simple and effective way to broader audience who generally don’t come from technical or data solution background.
Data science is an field of experimentation. Hence you need learn and apply clear & structured thinking process in evaluating and experimenting 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, everyday we’re witnessing tremendous amount of progress. So in order to keep yourself top of the game, you need to learn & apply new things on daily basis. Therefore its mandatory that you’re open to learn new things.
By no means this is perfect guide or exhaustive list of skills needed for you to become Data Scientist, however you can consider this as base and in process of learning these skills, you’ll encounter and learn new skills.
Even though data science domain open heartedly welcomes professional from variety of background & experience, it’s worth knowing some prerequisites of becoming a data scientist before taking decision of moving to data science as an career option. These prerequisites are not mandatory to have, however it will indeed make your data science journey simple.
Data science is a broad field which beholds variety of different paths and career options within it. It’s quite natural if you’re confused or not sure what each role is about or which career path out of it is more suitable for me.
In an industry you’ll not find clear distinction between these roles, hence I’ll try to explain what the different data science career paths you’ve within data science are and what each one of them means.
Let's explore them. !!
This role is usually considered as “Entry level” in data science domain. A Data Analyst’s role is to collect information from various sources and analyses its patterns & present it to stakeholders in intuitive way.
Data analyst transforms and manipulates large data sets to match the requirement of the companies. A data analyst recommends the different methods and techniques which can help a company in improving the quality of data systems.
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 & builds machine learning or deep learning models for prediction, find patterns and trends in data, visualise data, and even pitch in with marketing strategies.
It's the data scientist, who’s dealing with stakeholders too for understanding business problems, data at hand and share analysis & findings with them in most effective way.
Data Manager are the one’s 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 works hard to ensure that information flows timely and securely to and from the organization as well as within.
Data architects creates a blueprint for all the data management systems. Company’s every system & infrastructures 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.
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 time. 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 analysts are closely related to Data analysts with fundamental difference in the way they operate and function. Business analyst are more of experts in business domains, it’s functions & processes; hence they analyse & prepare actionable insights by diving deep into it. Business analyst often assist data analyst by providing them business insights, domain expertise etc.
Machine learning engineers are often one level down the line than Data scientist. Primary responsibility of ML engineer is to write code, 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 model testing & model deployments.
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 modelers are computer systems engineers who design and implement data modelling 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 are Data scientist only; however they aren’t associated with any specific organization. They generally work independent and have small team with them. In terms of skills there isn’t any difference then data scientist, however freelance people needs to be more outspoken.
This role is tightly associated with health-care 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.
As guessed, this role works with 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 makes huge difference on your overall business. This analyst helps you in designing effective methods regarding how to do marketing with marginal cost.
Big Data is yet another important technology in the arena of data science. This field largely deals with managing 100’s and 1000’s of petabytes of data in secured and easy to access manner. Big data developers are technical savvy individuals with heavy knowledge on computer architecture.
This is a leadership role in field of data science. The Director of Data Science leads the entire Data Science team. The Director Data Science will lead the department’s engagement with business stakeholders and executives and partners with these stakeholders and executives in enhancing the existing data management methodologies and developing new approaches and methodologies.
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.
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-users’ understanding of their systems.
Choosing right data science career path may not be straight forward, however if you follow your instincts, do self-evaluation with respect to required skills for each path and marry them with your aspirations, I am sure you’ll make the right choice.
As a suggestion, you can refer KnowledgeHut applied data science with Python, which dives deep into every aspect of data science career path.
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 job between professionals is also at the peak. If you follow this guide and do genuine self-evaluation, I am sure you’ll make right choice in choosing optimal path for you.
Just remember; Choosing right career path is just a start, your journey starts from there.
Data science is considered as “sexiest job” of 21st century. Moreover, recent trend regarding job opportunities and salaries of data scientist makes evident that it’s one of the best career paths; if you’ve what is demands.
In this guide, we’ve discussed every aspect of data science career path in length. It basically revolves around skills you require to get into this career and what are the various options you’ve.
I guess doing self-evaluation and ensuring you possess basic skills and interest to get into data-science is good starting point. Post that you may enrol into various courses and acquire formal education.
Yes, indeed. Data science as skill & career is in well demand since early 2012. The trend is linearly going upwards and with more & more companies are investing heavily in their digital transformation & data solutioning strategies, we will see more demand in the job.
There are no specific roadblocks or checklist for data scientists to become a CEO, However, they have to prove their skills in lot of areas such as management, operations, business strategies & deep knowledge in financial management. There are already tons of example in real world, where data scientist has become CEO.
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