The field of data science deals with huge sets of data, finding ways to use them in order to integrate them into real-world applications. With its multitude of benefits for research, business and everyday life, digital data is considered the oil of this century. From your social media posts to your latest Google search, data scientists use all data in one way or another.
The process of sifting through giant datasets is what data scientists are specifically trained for. They give important information that helps a company provide better services and improve its bottom line. Every business today benefits from using some form of data science.
In broad terms, data science encompasses the task of extracting clean data from raw data and further analyzing that data to make sense of it. Simply put, it is the process of generating actionable insights through visualization.
What is a Data Scientist?
A data scientist is someone who specializes in analyzing and interpreting valuable data. They use their skills to help companies make better decisions and improve their operations. Data scientists usually have a strong background in statistics, mathematics, and computer science. They use this knowledge along with their programming skills to analyze large data sets and find trends and patterns. Additionally, they are also finding innovative ways to extract and store data.
How to become a Data Scientist?
Qualification for Education
A data scientist should have at least a bachelor’s degree. As most employers are looking for advanced skills in this area, higher level degrees are not required. That being said, education requirements may include advanced degrees in math, computer science, statistics, or data science. There are also different types of certification data science course such as Certified Analytics Professional, Dell EMC DECA-DS and Microsoft MCSE Data Management and Analytics.
Applying good statistical techniques in analyzing and interpreting data is a great skill to have for a data scientist. The key aspect here is to understand the underlying nuances behind 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 consolidate your skills in order to obtain the position of senior data scientist. They should have an understanding of basic algorithms in forecast classification and clustering approaches. Having knowledge of emerging artificial intelligence (AI) / machine learning techniques helps solve new business use cases. Applying the right mathematical algorithm and fine-tuning those models, taking into account business needs, is a skill that comes with practice.
Business data is the raw material for analyzing and generating business insights. A data scientist must understand the nuances behind data collection, business context, and business glossary. Knowing the quality of the data, they can create accurate machine learning models. Also, a data scientist should be well versed in exploratory data analysis to get a feel for the characteristics of business data. This would involve checking for outliers, data sampling, data distribution, etc.
Mastering Python programming skills is a must when Python is recognized as the language of choice for data scientists. Data scientists need to create APIs for machine learning models based on Python frameworks. Apart from this, they should also have good knowledge of cloud technologies, Jupyter laptops and Spark computing framework. The “R” language is another famous pro programming language among statisticians that needs to be learned.
Skills of the field of activity
Understanding the business domain is key to getting a holistic view of the business problem one is trying to solve. These issues can be related to user experience, business hypothesis validation, revenue generation mechanism, and sales forecasting. Subject matter experts or SMEs offer valuable insights into the business, including business landscape, data collection limitations, reports, and tactical insights. Asking probing questions is an important skill to develop as it helps to understand why the business problem exists.
Data scientists must have good communication skills, as they are essential for articulating complex information in simple terms that decision makers can understand and act on. Data scientists must be able to explain the inner workings of the algorithm for the non-technical audience to understand the impact of their solution.
Data Scientist Job Description
Responsibilities of the Data Scientist position include:
• Collection, pre-processing and analysis of raw data
• Build models to solve business problems
• Present information using 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 around Rs. 500,000 per year. Data scientists who have 1 to 4 years of experience can earn around Rs. 610,811 per year. However, it will also depend on their location, the skills they have, and the employer.