The race to win over the best data scientists is getting fiercer each day.
Reports show a 29% increase in demand for the role of data scientists year over year and a 344% increase since 2013.
We find the demand for data scientists becoming more insatiable as organizations move forward in their digitization journeys and begin to leverage the power of data to fuel their business decisions and improve competitiveness.
Just as organizations are accelerating their hunt to scope out and recruit ace data scientists, some want to leverage the opportunity in the data science landscape and become data scientists. Owing to this need, we have witnessed the rise of several data science programs, some more coveted than the other.
The Harvard Business Analytics Program is one such program that has managed to generate much interest in those wanting to ace their journey as data scientists. Great, comprehensive, thorough, with a cross-disciplinary curriculum, this analytics program promises to propel data scientists to become data leaders. Those who can attend this program are sure to benefit from it. However, what about those who cannot partake in this program? What can these people do, the ones who cannot venture through the hallowed portals of Harvard Business to open the door to the promised land of data science?
I’m not going to delve into the details of what educational background you need to start your journey as a data scientist. You have Google for that. Along with the basic technical education, data scientists wanting to become the new ninjas have to look at honing multiple skills needed for the job – statistical analysis, programming skills, data management, data wrangling, machine learning, data intuition, etc. are important areas that where you’ll need in-depth knowledge. You also need a good knowledge of analytical tools, (R is probably one such platform that fits like a glove in the data science narrative), and strong coding skills (knowledge of Python will help here) are also needed.
Having experience in the Hadoop platform also is an important skill set to have as a data scientist. In fact, according to a study, “3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.” That is a long laundry list, and it keeps getting more comprehensive and a great way to home these skills are to look online for comprehensive data science courses that cover all this and more.
Along with this, it also helps to look at specific areas of improvement to further your journey as a data scientist in a more personalized fashion. For example, knowledge of machine learning concepts such as neural networks, reinforcement learning, adversarial learning, etc. can be of great use when you want to stand out as a data scientist since many data scientists do not have this skill.
Improving proficiency in SQL is also immensely helpful even in the NoSQL landscape as SQL is designed specifically to help data scientists to access, communicate, and work with data. Knowing the concise commands saves time and reduces the volume of programming needed to run difficult queries. Knowledge of the Apache Spark platform other than Hadoop, for example, can be another area to upskill as the former makes it easier to run complicated algorithms faster and supports data scientists handle more complex unstructured data sets.
If you thought Hackathons should be attended only when you are on the prowl to land a great new job, then think again. Hackathons are great for both tech-enthusiasts and newbies to check their skill levels, learn new things, and score a hands-on experience in problem-solving real-life business problems. Want to know what the latest and trending thing is? Go to a Hackathon.
Hackathons like MachineHack, TechGig, Kaggle, etc. are great places for data science enthusiasts to elevate their skills by participating in challenges set on real-world problems. Hackathons are also great to identify how and where your skills need updating since the data science industry is dynamic and prone to change. All in all, these challenges are a great place to learn by working on real-world problems, upskill yourself, and showcase and build expertise.
You cannot learn everything there is to learn about data science in a class because data science is all about solving real-world problems. Data science is both a science and an art – data scientists have to wrestle with the large data volume, create models, and provide statistical analysis. This is the science part of it. The art in data science lies in the data scientists’ capability to understand the business problem to design the right solution and communicate the data results in a language that non-data scientists can understand.
Data science is more than just building a data model – it is about understanding a business problem and then building a data model to play around with that data and glean intelligent insights. If you don’t have the opportunity to work on ‘real-world’ projects, you can always self-start with your own data science project. Find a data set that interests you, ask and try to answer questions around it, write out the results, and then rinse and repeat to develop real-world experience. Or you could look at organizations looking for help in their data science initiatives to further your learning.
Data science is a niche field. It is also a democratic field that allows those with skills, knowledge, and curiosity to thrive. So just like how data scientists find the solution to every problem, finding out how to hone your data science skills to be the coveted data scientists doesn’t have to be difficult only because Harvard is not within hands reach.