February 3rd, 2021 · 9 minute read

Data Science - How to Start a Rewarding Career

Updated: February 4th, 2021

Data science

Data Science - How to Start a Rewarding Career 


Did you know that Data Science and being a Data Scientist is described as the sexiest job of the 21st century. However, for those professionals who are already working and considering a change to this highly desired career, it may seem there is no clear career path to becoming a data scientist. 


You’d be wrong. Let’s explore what Data Scientists do and how you become one!


What Data Scientist's Do

Data scientists work with big data with the aim to draw actionable insights from it. These they communicate to business leaders to help them make prudent business decisions. Data Scientists interact with all parts of the business to help answer questions that affect business strategy and also aide in providing input to help shape critical decisions.


In manufacturing, Data Scientists apply their knowledge to understand production run metrics, raw material requirements, and much more. In financials, Data Scientists analyze data to make predictions to inform investment decisions. In retail, Data Scientists analyze customer traffic, buying habits and attempt to predict product and service factors that improve profitability.


Here at Apption, a software and technology services company, Data Scientists create products that allow organizations to analyze their big data and extract insights involved with risk, privacy, sensitive data, and data quality for AI/ML and analytics use.


These are just a small sample of the essential roles Data Scientists play.


Don’t Start Over - Add Data Science to What you Already Do

As the field is still developing, Data Science often overlaps with traditional roles. Data Science started by cleaning datasets and using statistics to answer questions within specific business areas. Although it has evolved to become a discipline of its own, it still requires knowledge of business functions to be of greatest value.

If you are already working in supply chain, financials, operations, and logistics, or any area of the business that requires analysis, could you augment your existing role to include Data Science? You could become a data analyst, a data engineer, or a data architect that specializes in your area, merging your experience with Data Science



Where to Start

Technology has developed so fast that institutions are struggling to keep up with the technology and develop courses that will provide employers with suitably qualified individuals. Universities and colleges now offer Bachelor’s

degrees in Data Science. These degrees are good for those starting their careers and will create a base for continued learning. However, the pace of change dictates that this is not the only way to learn Data Science. That is good news for those already in the midst of your career and want to transition to data science.


Academic Degrees

Studies show a Master’s or a Ph.D. degree is not the only gateway to a career in data science. Many people interested in becoming data science professionals don’t get much further than dreaming about the possibility because they believe that they need a Master’s or a Ph.D. degree. 


However, research by 365 Data Science that looked at 1,001 profiles of data scientists on LinkedIn, found that a Bachelor’s degree will suffice for a data science career. The study found that while 27% of data scientists hold a Ph.D. degree, a greater percentage (48%) hold a Master’s degree. There are also data scientists (15%) who only have a Bachelor’s degree. And 2% of the professionals that work as data scientists have MBAs. Indeed, candidates who have an academic background in computer science, economics, finance, business studies, statistics, and mathematics will find it easier to find a position as a data scientist, but the research shows that this is not a hard and fast rule.


If you don’t have a degree in data science, which degrees are helpful? 


The researchers looked at what the data scientists on LinkedIn studied at university and what degrees they obtained. They found that data scientists studied computer science (20 %), statistics and mathematics (19 %), economics and social sciences (19%), data science and analysis (13 %), natural sciences (11 %), engineering (9%) and 9 % were denoted as ‘’other’’. In other words, professionals who already have degrees in these disciplines have a good chance of entering a data science career.


Did you notice that economics and social sciences are on a par with computer science and statistics and mathematics? That’s good news for those who sit with economics and social science degrees and are contemplating a career move to data science. 


Looking at these results, it seems that there is more than one way to become a data scientist academically. Are there other ways?


Previous jobs

The researchers gleaned interesting insights from looking at the previous jobs that data scientists held. This is also an indication of the route that individuals have taken to become data scientists. More than a third (356) held a previous job as a data scientist, 173 were data analysts, 115 came from academia, 77 had worked as interns, 68 were IT specialists, 48 worked as consultants, and 142 were classified as ‘’other’’. Interestingly, 22 were youngsters who had never held a previous position. So, the data scientists on LinkedIn came from a variety of positions, including analyst, academia, internship, IT, consulting, and others.


How did these people manage to convince companies to employ them as data scientists? How did they obtain the skills to do the job?


Online courses

According to the data, 43% of the data scientists with profiles on LinkedIn have completed at least one online course in data science. Data scientists have an average of three certificates. In the last four years, there’s been a proliferation of online data science courses like DataQuest. You need to do due diligence to determine which courses are worth your effort, time, and money because you will have to commit to several months of studying and practice to gain the necessary skills. However, if you are self-motivated, these are great ways to test your interest, and second, get the skills required to make it a career.


Even if you know nothing about coding, some courses will admit you and recommend you to do an introductory course in R or Python. Most do not even require previous knowledge and experience in coding!


Making the best of your online course

When enrolling for an online course, you should make the same commitment to studying hard as you would to learning for a university or college degree. Watching a few videos is not real learning. 

One of the hardest things about doing an online course, and staying committed to it, is the uneasy feeling that you are not learning enough or not learning the right things. This fact eats away at one’s motivation to keep studying, which is one factor causing the majority of learners to quit their online courses. 

The only remedy for this is to choose a course that involves projects that challenge you to apply what you have learned. If you apply the skills you have learned to real-life problems, it will bolster your understanding and confidence.

A few popular avenues for practicing newly acquired data science and machine learning skills is to apply them to something you are interested in. Areas like the stock market, your current job, and sports statistics are popular areas. Enthusiasts consult courses, textbooks, and other materials to teach themselves what they need to know and apply that to predict outcomes. This approach works because it doesn’t feel like hard work when you are working on something you’re passionate about.


Choosing a data science course

When you research data science courses, you will soon discover that they are not created equal. Very few of them go through every aspect of the data science process. For instance, even some highly recommended courses lack a comprehensive section on statistics, which is foundational to data science. This doesn’t mean that you should disregard these courses; you’ll just have to get your statistical knowledge elsewhere.

The course you choose will depend on the gaps that exist in your knowledge and experience in the different skills you need for data science. Just be mindful of the contents of the course and don’t hesitate to ask for reviews, feedback, and advice from those already doing Data Science.


Final thoughts

Individuals who have an academic background in computer science, economics, finance, business studies, statistics, and mathematics can enhance their skills by following one or more online courses that would put them in a position to break into data science.


The competitive advantage that you have is merging your existing skills and experience with Data Science. Your unique perspective will become a super-power with your new Data Science skills.


Here at Apption, we are always looking for talented Data Scientists or those who want to use Datahunter or help us take our products and services to the next level. We hope this blog post helps you to an exciting career in Data Science.


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