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What is Data Science and what does a Data Scientist do?

Yes, you must have heard that the most sought-after job in the 21st century is working as a “Data Scientist”. Today, Silicon Valley is booming with data scientist jobs and in the coming years several more jobs would be floating in the industry. We will delve into what it takes to become a Data Scientist, how does one transition into the job role, and the applications of data science in relation to our business.

What if someone foretold your future! Would you not plan your life in accordance? Yes, you read it right, FUTURE; this is what the data science field is in a nutshell. Where you can see your prediction values with the help of different AI/ML algorithms. Data Scientist’s roles and responsibilities include the items listed below:

  1. Data understanding: Being a consultant to the data set and study the various descriptive statistics in the data.
  2. Data preparation: Data molding or mining for the modelling process (with the aid of algorithms to achieve our use-case)
  3. Exploratory data analysis (EDA): Study of data in a visual form to identify the trends and important characteristics of the data set.
  4. Data modelling: It is the process of data science algorithm application to achieve our outcome.
  5. Dashboarding or BI: This is crucial step to represent our model outputs to businesses in a user-friendly form that utilizes easily interpretable dashboards.
Data Science img 1
Source: KDnuggets


The process followed here is important to attain the right outcomes for our business KPI’s. In the next sections we will learn the benefits of implementing data science for generating better ROI’s for your business.

How data science helps business take impactful decisions?

We see a lot of industries leveraging the power of Data Science today. Let’s gain an insight into how different industries use data science in order to achieve results for their businesses.

  1. Manufacturing industry: The demand and supply are the core key areas which play a major role in managing the inventory in the manufacturing industry. To solve this Data Scientists help us predict the required amount of product, the right time and ways to help reduce wastage leading to an increase in revenue.
  2. Automobile industry: The automobile sector was badly hit by the pandemic; however, our data science practices can help get the right set of leads or customers based on previous trends in data and harnessing the power of AI/ML. To help reduce the extra expenditure on cold leads.
  3. Retail industry: Today the main retail giants like Amazon, Flipkart etc. are harnessing the power of Data Science to see the whole customer journey on their platforms and give their customers recommendations according to their preferences. A better understanding of their customers leads to an increase in sales.
  4. Financial industry: There is a vast application of data science to help the financial industry reduce the defaulters in the banking finance sector by finding the right set of customers for different products which helps to optimize the leads in the industry.
Data Science img 2
Source: DataFlair


Data Science can help businesses in several other industries to optimize their KPI’s too.

Who and how can you transition your career into data science?

Having seen the use cases and the professionals working in the field of Data Science - this section will help us in seeing how we can transition our careers too.

Let us begin with studying the basic components required to be a data scientist:

  1. Business / Domain Knowledge
  2. Statistics and Probability
  3. Computer Science knowledge
  4. Communication to help the business understand technical outcomes.

These are the pillars which are the crux of Data Science.

Data science img 3
Source: Research Gate


Data Science seems to be a very lucrative field to enter however many of us face difficulties in choosing the right path to set our data scientist careers in motion. What is required to become a successful data scientist?

  1. Pay attention to details, especially when it comes to numbers, a statistician can be a very good data scientist but not vice-versa.
  2. Be at the top of game when it comes to coding! Make sure you build your R/ Python & SQL skills.
  3. Be open-minded to learning more, this path always has something new to offer.
  4. Excellent logic building and decision-making skills.
  5. Choose this field because you are passionate about data science.

Coming to the most common question in data science today, “How can we become a Data Scientist?”

There are millions of resources floating on the internet today making it tougher to choose where to start learning. Let us see a simple yet successful solution to this:

  1. Gain a basic understanding of statistics (mean, median, mode etc.)
  2. Move onto growing your knowledge in concepts related data manipulation languages (R/Python)
  3. Build a very strong foundation in SQL.
  4. Start with the ML algorithms foundations do NOT directly jump to learning about the OCR models.
  5. Read a lot of research papers this will help understand real-life examples better and aid in grasping business knowledge and its applicability.
  6. Last and the most importantly, is the hands-on practice! Codes should be the final steppingstone, with at least 10-15 different hands-on ML problems.

Be a star in the field of Data science and get prepared to have one of the most demanding job roles of Silicon Valley.