LinkedIn reports that 4,000 new data science jobs were created last year, in the U.S. alone while a recent report from Indeed showed a 29% increase in demand for data scientists year over year. The demand for Data Scientists continues to rise all over the world – along with their salaries.
No wonder, many of us are attracted to join this exponentially growing field. We continue to ponder and debate about it in our minds and with our colleagues!
Interestingly, today the core question in everyone’s mind has shifted from “Whether I should get into Data Science field?” to “Can I get into Data Science?”
Secretly everyone ponders in their mind “Whether I have what it takes to become a Data Scientist?“
As a practicing Industry consultant and trainer in Data Science, in this article I will help you to understand what it takes to become a good data scientist.
Though Data science is not Rocket science, it is not too far away. The main difference being that while Rocket Science needs not just knowledge but lot of capital to practice and build rockets, Data Scientists can practice their skills abundantly with freely available tools and techniques.
This has both good and bad repercussions. The Good outcome is that today there is a thriving and a very supportive data scientist community which includes experienced veterans as well as budding data scientist and novices with many more people joining the data scientist community every day.
However, the bad ramification is precisely due to the same reason- there are many in the community who with half-baked knowledge in Data Science knowingly or unknowing create myths about what it takes to be a Data Scientist.
So, to clarify some of these myths, that I am asked about often, here is my construct of what it takes to be a data scientist.
Click here for more details:
- Data Scientist-Sexiest Job of 21st Century or just plain Hype!
- What is Data Interpretation and how to do it well?
- Business Intelligence(BI) Vs. Data Analytics
- From Drowning in Data to Data-Curiosity
- Top Leading companies using Big Data in this region currently
- Adopting the Data Driven Culture
- Are you ready for the Data Revolution?
- From Ordinary to Extra Ordinary-Use of Big Data Analytics in Marketing !
- Big Data Analytics in Finance Industry- Here to stay!
- Big Data Analytics-An Illustration through Case Study
- Can you be a part of the Big Data Trend?
Prior IT Domain Experience
This is a popular Myth. Data Science has nothing or rather very little to do with IT domain. So, you need not have prior experience as an IT professional.
Prior Programming Skills
This is a half myth! While you do need some programming skills to become a Data Scientist – it is not necessary that you should know traditional programming languages like C++, Java, Sql etc.
Data Science is mostly based on languages like R and Python which are almost like English i.e. easy to use and hence suitable for Decision makers (rather than programmers). Being open source, they also have a thriving user community, ready packages/libraries and hence can be used very easily for data mining/cleaning, statistical analysis, visualization and data modeling. So even if you do not have prior programming skills, it is absolutely fine for you to get into the field of Data Science.
This is not a Myth! You do need some basic analytical skills to appreciate and use Data Science techniques. Most of us are anyways born with some analytical skills like logic, reasoning, judgment, and critical thinking and use them at almost every instance in our daily routines – whether working in a job or not.
So as long as you have been using some basic analysis and/or using tools like excel etc., you would be able to follow Data Science.
This is not a myth! You will have to certainly strengthen your analytical skills with understanding of Statistical concepts and learn to interpret data correctly using right statistical technique.
Statistical concepts are certainly a must as they provide the main foundation for Data Science, Machine Learning and AI. Though understanding statistical concepts may be little complex initially, it is heartening to know that compared to before using and applying statistics has become very easy with Data Science.
Business Domain Knowledge
This is a definite must for becoming a good Data Scientist. You must have the ability to apply data science to different Business Application. Without the business applicability Data Science has no value. You could just become a programming or statistics nerd without the holistic understanding of how your programming and statistics skills can be applied to enhance the business functionalities.
However, do note that Data Science is just a tool and not a business functionality. It can be applied and should indeed be used in all business functionalities like HR, Finance, Operations, Logistics, Marketing, Quality control etc. and across all industries like Healthcare, Engineering, Manufacturing, Retail, Travel etc. So, if you have business domain knowledge of a particular functionality/Industry, it will enhance your ability to apply suitable data science techniques to develop meaningful models and solutions for that domain.
As mentioned Data Science is a tool and if you have a mind that is curious to know more about business challenges and how to solve them, Data Science will work wonders for you. Curiosity is an essential ingredient for any scientist and without asking ‘Whys’, you will not be able to progress much as a Data Scientist.
So yes, you do need a mindset that asks questions and then looks for possible answers to be a good Data Scientist.
After all the hard work of analyzing data, creating beautiful Visualization, Training and Developing your predictions and models, you also need good communication skills to present your data driven Insights, conclusions and recommendations to stakeholders so you can drive decisions accordingly otherwise the contribution of data science will be zilch.
Hard Work and Discipline
Last but not the least, you need the discipline and hard work to pursue and practice Data Science skills so you can apply them meaningfully and successfully in your business objectives.
So, these are the most important ingredients to become a good data scientist.
Good news is that most of these can be learnt and enhanced so you can certainly make a start on the journey to be a data scientist.