Top 5 Technical Skills you need to be a Data Scientist

So, here is a list of the top 5 must-have technical skills for a data scientist.

1. Programming – A data scientist will be proficient in one of the programming languages like R, Python, SAS, Hadoop, etc. It is not just about writing code but being comfortable with using different programming environments to analyze data. With the field of data science seeing unprecedented interest and value in businesses around the world, a command over programming languages and the ability to adapt to the changing technology is key to a data scientist’s success. Any hesitation with using programming tools can turn into a deal breaker for a company relying on your work to accelerate their business growth.
2. Quantitative Analysis – This is what defines the essence of a data scientist’s job. Having a calculated and visceral understanding of a complex environment and its behavior, munging data that is messy and difficult to work with, creating prototypes and models to test assumptions is part of a data scientist’s profile. Must-know concepts include how to build predictive and regression models, machine learning – supervised and unsupervised learning algorithms, time-series forecasting, data-reduction techniques, neural networks, etc.
3. Statistical Knowledge – Without statistics, a data scientist and the future of an organization is at sea. Generating hypothesis based on how a system will behave with changes, making assumptions of statistical significance about variations in data, defining metrics to layout objectives and measuring success, and drawing accurate conclusions from the dataset will all be impossible without math and statistics. Writing code or using functions effectively will also become a challenge if a strong foundation in math and statistics is missing.
4. Visualization Skills – It’s a known fact that humans absorb information faster in the form of pictures as compared to words and numbers. A working knowledge of data visualization tools like Tableau, Qlikview, Plotly, or Sisense will ensure that a data scientist is confidently able to present insights to both a technical and non-technical audience convincing them of the business value their insights can draw. Acquainting themselves with the principles of visualizing and presenting compelling data to stakeholders can go a long way in determining a data scientist’s success.
5. Linear Algebra and Multivariable Calculus – It may or may not be asked in an interview directly, but at some point, a data scientist may have to build their implementation models in-house. This is particularly true where products that are defined by data can lead to transformational gains for the organization. Data science is a comparatively newer field, and no job descriptions are set in stone. A working knowledge of linear algebra and multivariable calculus can thus come handy when developing out-of-the-box models. Also, an interviewer may stun you with a calculus question. A confident data scientist will tell them to bring it on!