When in 2012 the Harvard Business Review dubbed data scientist as the sexiest job of the 21st century, it officially gave it an aura. Soon, the buzz around data scientist jobs spread and the aspiring technology professionals were intrigued. As time went by, much of this intrigue became myths — some harmless exaggerations, some downright misconceptions. Today, we’re busting common myths about data science jobs.
Data Scientist Jobs: Myths vs Reality
We spoke to some of Springboard’s mentors about the biggest myths around the data science career. Here’s what they had to say.
Myth #1: Data Scientist is Just a Fancy Name for a Business Analyst
The data scientist job description is fundamentally different from that of a business analyst. The role of a business analyst is to act as a bridge between business and IT. They typically gather functional and non-functional requirements, build use cases, communicate with stakeholders and manage project delivery. A data scientist, on the other hand, processes data to spot trends, glean insights and build predictive models. The below venn diagram will help you paint a clear picture.
Myth #2: Data Scientist, Data Analyst, Data Engineer are all the Same
The exact role of a data scientist is still fluid, naturally, as the field is still evolving. For example, if the organisation you work for is a small one, one person might do data cleaning, transformation, analytics, model building etc. But if you’re working for a large team of data scientists, you might specialise in any one of these areas. In general:
- A data scientist is concerned with collecting, analysing, interpreting and visualising large sets of data. They build hypotheses, test them, and learn from the data.
- A data analyst performs a subset of these tasks — like analysing data and visualising them. However, a data analyst might not write code or build predictive models.
- A data engineer works on designing and developing information systems.
Here is a source that might help you with an in-depth look into the differences between data science, data analytics, machine learning and artificial intelligence careers.
Myth #3: Data Science is all About Tools
Like every other job, a data scientist also uses tools to get the work done. Their armour will have sophisticated data mining, transformation, visualisation, and deployment tools, in addition to various development environments. But knowing to use these tools can only take you so far. Fundamentally, the role of a data scientist is to solve business problems using data. So, to be a good data scientist, you need to have skills in problem-solving, communication and logical thinking, as well as other data preparation, exploration, evaluation metrics and transformation.
Myth #4: Data Scientists don’t Code
The proof that this is one of the biggest myths of all is in every data scientist job description available today. Because, if you are a data scientist, you are responsible for taking your ideas to production. So, you need the programming skills to write production-ready code. The most common languages used are Python and R, but companies are known to use Java, SQL, Scala and others too.
Myth #5: Coding Background is a must-have for Data Scientist Jobs
It might be surprising that some people think data scientists don’t code, while others think coding is a must-have. But it happens. So, let’s clear this up too. Data scientists come from a variety of backgrounds — mathematicians, statisticians, engineers, as well as programmers. But we need to differentiate ‘skill’ from ‘background’. It goes without saying that data scientists need programming skills, but not necessarily several years of coding experience. If you’re not a programmer, you can learn to code quickly and improve steadily. Here is a simple guide on how to become a programmer.
Myth #6: Data Science is all About Predicting the Future
Predictive modelling is a part of data science, yes. But it is not the only part. In fact, it occupies a very small space in the world of data science. Today, techniques and technologies of data science are being used in a wide range of fields — from multi-language translations, image search, video analysis, to self-driving cars. If you’re interested in seeing the data science landscape, which extends from Uber to Delhi Police, read our blog post about real-life data science projects here.
Myth #7: You need to be a Mathematician or Statistician to Become a Data Scientist
This is a complex myth because it’s both true and false. Data science uses concepts of mathematics and statistics every single day. Without understanding the basics of probability, linear algebra and other statistical concepts, you can not become a good data scientist. However, you don’t necessarily need to hold a PhD in these fields either. Many entry or mid-level data scientist jobs don’t require a formal qualification in these areas. If you have a good foundational understanding and can apply these concepts to practice, you can become a data scientist.
Myth #8: Experienced Professionals can’t Transition into Data Science Jobs
Not true. As an emerging field, most senior data scientists you meet will have done some other kind of work before this. Whether you’re an experienced career professional with 20+ years under your belt or a beginner with less than 5 years, you can make a smooth transition into data science jobs.
Here is a stepwise guide on making that career transition in the data science field. Also, here are some mistakes to avoid in your career transition journey. If you’re looking for a clear and seamless way to kickstart your data science career, consider Springboard’s data science career track. This 1:1 mentoring-led project-driven program is the only data science program in India with a job guarantee. Apply Now!