Data Science As a career

Posted on by Bhavani Singh

Data Science as a career


With the increased usage of the internet, voluminous data is generating every day across the globe. Enterprises are looking for a solution to organize, process, sort and clean the huge amount of data to extract meaningful information from it. This is when Data Science comes into the picture. In this digital era, data science has become a revolutionary technology. Industries are embracing this technology to get key insights into their businesses. In this way, they can make better decisions and run their business efficiently. If you are planning to pursue the data science field as your career, then this article is going to be very beneficial for you. It will provide you with essential details about Data Science.


Before delving into details about Data Science career scope and required skills you need to have to become a data scientist, let’s understand what is Data Science and what exactly the data scientist do?


What is Data Science?


Before getting started with the Data Science term, look at the graph given below. It shows how data trends have been changing from the year 1990 to 2020. 


DataScience growth over time


Earlier, it was easy to analyze data by using Business Intelligence (BI) tools because most of the data used to be small in size and in a structured format. However, in today’s modern world, most of the data is semi-structured and unstructured. In the years to come, more than 90 % of data (generated from sources like: text files, multimedia forms, financial logs, devices, sensors) will be incoherent, complex, unstructured and semi-structured. Therefore, conventional BI tools are not capable to process this wide variety of voluminous data. This is the reason why Data Science is becoming so popular in this business world. It is a multidisciplinary blend of advanced algorithm, tools, and technology that can analyze and process the complex data to extract meaningful insights out of it.   


What do data scientists do?


  • Use tools, methods, algorithms and machine learning to extract meaning from the data.
  • Identify and analyze data trends, data variables, and data sets.
  • Clean and process the data in order to maintain uniformity, completeness, and accuracy.
  • Use software engineering skills to debug data.
  • Communicate with the team member and use data visualization technique to present the information
  • Gather a wide variety of unstructured and structured data from diverse sources. 
  • Analyze and interpret data to uncover business opportunities and solutions. 
  • Collaborate with stakeholders to discuss the data findings.


Required skills for Data science that will get you hired



1. Programming language


 Statistical programming languages, including database query language (SQL)R or Python and Hadoop, are essential tools for data scientists to learn. Whatever the companies you’re interviewing for, they will expect you to have a profound knowledge of these tools. 


 2. Machine learning

If you are working in large enterprises that-

  • generates or gather a huge amount of data, or 
  • Produces data-driven products, like, Google Maps, Uber, Netflix, etc.

Then you should know machine learning techniques (including k-means clustering, neural networks and more). You are not required to become an expert in machine learning methods and going deep-dive into how they actually work. But, you should know the appropriate time when to implement these methods and techniques.


3. Statistics

Being a data scientist or to get yourself hired as a data scientist, you should have a good understanding of statistics. You should have acquainted with maximum likelihood estimators, statistical tests, distributions, etc. 

Statistics is important for all types of companies, but it has a special significance in data-driven companies where stakeholders are dependent on the-

  • decisions made by data scientists and
  • how data scientists are designing or evaluating experiments. 


4. Multivariable calculus and linear algebra

Considering the fact that data scientists rely on Python and R for most of their implementation process, but many product-based companies prefer building their own implementations in-house. Therefore, understanding the concept of multivariable calculus and linear algebra is important if you are working or going for an interview in a company where the product is defined by data. 


5. Data visualization

With data visualization techniques, organizations can be able to quickly get key insights into their business, maintaining a competitive edge in the marketplace. As data scientists, you should how to visualize data with the help of data visualization technique, including Tableau, Matplottlib, ggplot and more. With the help of these tools, you can convert complex information into an easily understandable format. 



6. Dealing with unstructured data (Data wrangling)

Data scientists often require to deal with unstructured data or imperfect data, such as missing values, inconsistent string formatting, etc. Sometimes, analyzing such type of data turns into a mess. To address this challenge, data scientists should know how to manipulate unstructured data from different platforms. 


7. Software engineering

Mostly small enterprises prefer hiring data scientists with a strong software engineering background. They will be responsible to predict, classify, evaluate the data results. At the same time, they should know when to use particular algorithms and tune algorithms performance.


8. Data intuition

Being good in data intuition means you possess data-driven problem-solving skills. In an interview, they may ask you the methods to solve some high-level problems, for instance, problems related to the data-driven products that the company want to develop. 

Apart from all this, as data scientists, they would like to see-

  • How you interact with product managers, stakeholders, and engineers?
  • Which methods or algorithms you use to solve data-related issues?



Lok at the given image below that shows response to the importance of each field in your data science career.


Data Science roles

(image credit:


In conclusion: Data Science career scope


  • Data Science as a career will continue to accelerate in the years to come. With continuously blooming of a large amount of data in our everyday life, companies are looking for skilled data scientists who can extract sensible information from generated data. 


  • Job portal like states that industry leaders such as IBM, Airbnb, PayPal, Twitter, Facebook, Google, Apple, LinkedIn, etc. are in a rush to hire data scientists. 


Let’s have a look at the following data science job roles in the market:


  • Data Scientist
  • Data Administrator
  • Data Architect
  • Business Analyst
  • Data Analyst
  • Data Analytics Manager
  • Business Intelligence Manager


About Bhavani Singh

Author View all posts by Bhavani Singh →

Leave a Reply

Your email address will not be published. Required fields are marked *