The Data Science domain deals with huge datasets, finding ways to make use of them in order to integrate them into real-world applications. With its multitude of research, business and daily life benefits, digital data is considered the oil of this century. From your social media posts to your latest Google search, the data scientists make use of every data in one way or another.
The process of sifting through giant sets of data is what data scientists are specifically trained for. They give important insights which help a business in providing better services and improving its bottom lines. Every company today benefits from using some form of Data Science.
In general terms, data science encompasses the task of extracting clean data from raw data and further analysing these data to make sense of them. Simply put, it is the process of generating actionable insights through visualization.
What is a Data Scientist?
A data scientist is someone who has specialized in analysing and interpreting valuable data. They make use of their skills to help businesses come up with better decisions and improve their operations. Data scientists usually have a strong background in statistics, mathematics and computer science. They make use of this knowledge together with their programming skills to analyse large sets of data and find trends and patterns. In addition, they also find innovative ways to extract and store data.
How to Become a Data Scientist?
Educational Qualification
A data scientist is expected to have at least a Bachelor’s Degree. As most employers look for advanced skill sets in this field, higher-level degrees are not mandatory. That being said, educational requirements may include advanced degrees in mathematics, computer science, statistics or data science. There are also different kinds of certification data science courses such as Certified Analytics Professional, Dell EMC DECA-DS and Microsoft MCSE Data Management and Analytics.
Mathematical Skills
Applying the right statistical techniques in analysing and interpreting data is a great skill to have for a data scientist. The key aspect here is understanding the underlying nuances behind the statistical techniques. Without this skill, a data scientist would struggle to produce accurate and meaningful insights from the underlying data. Advanced mathematical concepts in linear algebra, set theory, optimization, causal analysis, probability, and graphs are necessary to cement your skill set for securing the senior data scientist’s position. They should have an understanding of basic algorithms in forecasting classification and clustering approaches. Having knowledge about emerging Artificial Intelligence (AI) / Machine Learning techniques helps in solving newer business use-cases. Applying the right mathematical algorithm and fine-tuning those models, taking the business requirements into consideration, is a skill that is acquired by practice.
Data Skills
Business data is the raw material for analysing and generating business insights. A data scientist is required to have an understanding of the nuances behind data collection, business context, and business glossary. Knowing the quality of data, they can build accurate machine learning models. Additionally, a data scientist is expected to be well-versed in performing exploratory data analysis to have an idea of business data characteristics. This would involve checking for outliers, data sampling, data distribution etc.
Programming Skills
Mastering Python programming skills are a must when Python is known as the language of choice for data scientists. Data scientists are to create APIs for machine learning models based on python frameworks. Other than that, they should also have sound knowledge of cloud technologies, Jupyter notebooks and the Spark computing framework. The “R” language is another famous pro programming language among statisticians that is to be learned.
Business Domain Skills
Understanding the business domain is critical in getting a holistic view of the business problem that one is trying to solve. These issues could be anything relating to user experience, validating a business hypothesis, revenue generation mechanism and forecasting sales. The Subject Matter Experts or SMEs offer valuable information about the business including the business landscape, limitations on data collection, reporting, and tactical insights. Asking probing questions is an important skill to develop as it helps in understanding why the business problem exists.
Communication Skills
Data scientists must possess good communication skills as they are vital for articulating complex insights into simple terms that decision-makers can understand and act on. Data scientists should be capable of explaining the internal working of the algorithm for the non-technical audience to understand the impact of their solution.
Data Scientist Job Description
The data scientist’s job responsibilities include:
• Collection, pre-processing and analysis of raw data
• Building models to solve business problems
• Presenting information with the help of data visualization techniques
• Collaborate with product development and/or engineering teams
Data Scientist Salary
India ranks second when it comes to the availability of job opportunities for data scientists. On average, the data scientist salary in India is around Rs. 10.5 LPA. With less than a year of experience, an entry-level job can offer approximately Rs. 500,000 per year. Data scientists who have 1 to 4 years of experience can earn about Rs. 610,811 per year. However, this will also depend on their location, skill sets they possess and the employer.
