With the advent of technology, we are generating humongous volumes of data every day. And it is the need of the hour to gather, process and analyze this data to gain deep insights into patterns and trends so that one can understand and improve the processes and solutions.
This is exactly what a data scientist does and no wonder their demand is rising exponentially in the market. Every organization wants a brilliant scientist in their team who can play around with the data and help them improve their product development, customer acquisition, retention and much more.
But in order to become a data scientist, one needs to have a certain set of skills, both behavioral and technical. The major behavioral or soft skills that a data scientist must have are; scientific curiosity, excellent communication skills, critical thinking and of course, a data-driven analytical mind.
In this article, the focus is on the technical skills that a data scientist must have in order to succeed and get those heavy pay-checks that this new field has gained a reputation for.
1. Knowledge of algorithms
Besides possessing a data-driven mindset that believes only in the quantitative approach, you must have the know-how of algorithms ingrained in your neurons.
Having a tight grasp on various algorithms is the primary requirement to become a data scientist. Every programming language, software, and even the world wide web runs on algorithms. You should have the ability to understand which algorithm should be deployed in which case. Sometimes you’ll have to reverse engineer to get the algorithm of a certain process.
Besides formulating algorithms, you’ll have to optimize the same too so that the results are accurate and fast. This is the basis of analytics, the foundation of your data scientist career and if you strengthen this analytical part of your brain you’ll grasp the other concepts pretty easily.
2. Mathematical and Statistical Modeling
Once you grasp the basics of algorithms, you’ll need to have a knowledge of various mathematical models that will be helpful for you to visualize and analyze the patterns in your huge quantities of data. You will be using various software programs most of the time to do the same, but your basics of how they work should be clear. The software is just a tool to speed up the process, but a sound understanding of models lets you rise and shine exponentially fast.
3. Programming Languages
3.1. R (programming language)
This simple yet powerful language has broken its shackles of being restricted to academic uses. It has fast become the preferred language for social networking applications, financial data powerhouses, as well as marketing and media outlets. It is an invaluable tool for statistical diagnosis and data processing and visualizing. Besides, it is great for predictive modeling too, which makes it an indispensable skill for every data scientist
This high-level language was not even designed for analyzing data, but it has come out as one of the most powerful languages of the same. Thanks to the several data processing libraries that have been developed for this language. Several major corporations, including Facebook, are using this fast, friendly and easily learnable language for their data analytics requirements.
3.3. SQL (Structured Query Language)
This is another staple of every data scientist’s arsenal of tools. SQL is used specifically for managing large volumes of data that is stored in database management systems. Although the work that it does can even be done in Microsoft Excel or programming languages like R and Python, SQL code makes the process much more easy and efficient.
4. Data analytics tools
Two of the most famous tool for Business Intelligence (BI) and analytics are Microsoft Power BI and Tableau. Although if you know python or R, you don’t need the help of this software, they have their own advantages too. They are great at data mining and visualization through their beautiful graphical interfaces that make it easier to present data in an understandable way. Many companies use this software, so a proficiency in using them would add up to your credentials.
5. Apache Hadoop
This open-source framework has risen in popularity since its inception a few years back. It utilizes the MapReduce programming model to ease the storage and processing of distributed data across several computer clusters and commodity hardware. Its popularity can be gauged from the fact that more than 50% of the Fortune 50 companies use this framework.
If you have set your mind on becoming a data scientist, it’s the right time to go for it. There are several good websites and tutorials that can help you learn these skills from the comfort of your home.
Is there any other skill that you would like to add to this list? Leave in the comments below.
