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About Data Science

Data Scientist (n.): A person who is better at statistics than any software engineer and better at software engineering than any statistician.

Who is a Data Scientist?

“Data Scientist (n.): A person who is better at statistics than any software engineer and better at software engineering than any statistician.” - Josh Wills - Ex-Director(Data Engineering), Slack

In the modern world, about 2.5 quintillion bytes of data are processed every day.

From organizing and analyzing this huge amount of data to make it accessible to lead a profitable business - That’s what a Data Scientist is all about!

A computer professional possessing skills for collecting, analyzing, processing a large set of structured and unstructured data. In this time of computers, most organizations are collecting a huge amount of data in their daily operations. In almost every interaction with technology, data is interchanged.

A successful data science career would require you to be a jack of all trades – programmer, analyst, engineer, mathematician, statistician, and strategist. But above all, a data scientist needs to love data. Their curious mind revolves around information; they formulate patterns, identify trends, analyze data, and solve business challenges.

Data Scientists not only play a key role in business analysis, but they are also responsible for building data products and software platforms. Truly speaking, data science is a combination of computer science, statistics, and mathematics.

Related Departments

Computer Science & Information Technology 14677 Students Shortlisted

Top Colleges

India

Duration: 4 years.

Fee per course/annum: Rs.4 - 5 Lakh Per Annum

USA

Duration: 4 Years

Fee per course/annum: Rs.45 - 60 Lakh Per Annum

UK

Duration: 4 Years

Fee per course/annum: Rs.30 - 45 Lakh Per Annum

Canada

Duration: 4 Years

Fee per course/annum: Rs.15 - 20 Lakh Per Annum

Career Prospects

Data Scientists

“Information is the oil of the 21st century, and analytics is the combustion engine.” - Peter Sondergaard, Global Head of Research - Gartner, Inc.

So we have oil, and a combustion engine, who’s going to drive the vehicle?

A Data Scientist. 

From exploring various data patterns to measuring the impact on an organization, data scientists do it all. One of your key roles as a Data Scientist is the ability to explain the importance of data in a simpler method, so that others understand it too!

Not only talking about it, data scientists must have statistical knowledge of different programming languages to solve complex problems.

Data Scientists are like Buzz Lightyear - They go to infinity and beyond to analyze big data for addressing real-world business problems. With Artificial Intelligence, the data is automatic. However, providing trends, checking patterns across data and offering actionable insights and strategies is what data scientists are great at!

Now these insights? They have a direct impact on strategic business decisions. From being an excellent communicator, to a business strategist, and even better analyst and statistician are some of the skills expected from a data scientist.

Do you think you have what it takes?

Data Analyst

“Hey Siri, can you analyse this data for me?”

“I’m not sure, I understand”

Yes, because only Data Analysts do!

Data Analysts are required in EVERY department of the company. At one point, the marketing department may require their services for some time to understand consumer behaviour and reactions to different marketing strategies. At the same time, you may have to analyse data for the business intelligence team to figure out trends in sales.

Analyzing data to figure out a market trend is in essence what Data Analysts do. You will help in providing a clear picture of the company’s standing in the market. Depending on project-based goals, you as a Data Analyst will provide datasets to achieve the required goal.

For instance, most data scientists start as data analysts and data engineers at the beginning of their careers. They work directly with raw data collected through the systems and with various teams like marketing, sales, customer support, finance to process information.

They are the Data Cleaners! Not only that, they also study the data, create reports using various tools to develop strategy!

By 2026, due to the demand, the Data Analytics industry is projected to create 11.6 million jobs!

Data Engineer

Data Engineers are the backbone of a company.

With the responsibility of building, designing, and managing a large database, they also build data pipelines to enable correct data flow, and ensure the data reaches the relevant departments.

To summarise, a data engineer has to share his insights with the company through data visualisation, helping the organisation grow.

Data engineers are experts at accessing, and moreover, processing vast amounts of real-time data. Vital to technology-driven companies and tech departments, they interpret unformatted and unverified data. Thus, daily tasks include maintenance of high data volumes as well as creating data pipelines to make data accessible for further analysis with the data teams. Data engineers set up the infrastructure using programming languages (Python) and advanced SQL, NoSQL.

Business Intelligence Analyst

Strategists at heart and analysts by mind - A Business Analyst

A business intelligence analyst helps in analysing the collected data to maximise the company’s efficiency, eventually generating more profits. The role of a business analyst is more technical in nature than analytical, requiring more knowledge of popular machines. They also serve as a bridge between business and Information Technology.

Business analysts process enormous amounts of data and scout opportunities to improve business revenue and growth.

Some of the job titles held by business analysts are:

  • Business intelligence (BI).
  • Developers.
  • Business consultants.

Marketing Analyst

A marketing analyst assists companies in their marketing division by analysing and suggesting which product to produce in large quantities and which product to discontinue.

They also monitor customer satisfaction reports to help improve existing products and services. Marketing analysts are the ones who decide which products to sell with the targeted customers and at which price.

For example, if you paid Rs.10 for a Cadbury Dairymilk, and now you pay Rs.20, a marketing analyst is behind that price change!

Identifying shifting consumer behaviour and examining new buying trends, along with analysing the digital universe for a business is all the work of a market analyst. With businesses selling online, marketing analysts have access to large amounts of data across platforms and devices. This helps them create strong go-to-market strategies, and evaluate marketing campaigns.

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Jobs & Salary

Top Industries that provide a scope for Data Science -

  • Media and Entertainment
  • BFSI
  • Healthcare
  • Telecommunications
  • Automobile

On an average, in India, data scientists get paid between Rs.5 - 8 Lakh Per Annum for those who have an experience of between 1-2 years. The highest average salary for experienced scientists are Rs.15 - 25 Lakh Per Annum.

Pros & Cons

Pros
  • High Demand
  • Well Paid
  • Versatile Jobs
  • Challenging
Cons
  • Jobs are not well-defined
  • Difficult to upskill and master
  • Data privacy issues

Famous Personalities

Yann LeCun

Yann LeCun is one of the most well known data scientists of our time. He is well known as the Director of AI Research at Facebook, but has made industry changing inventions that earned him a spot at the top of the list for best data scientists in the world. Yann has 14 registered US patents and created CNN (convolutional neural networks) which is basically one of the reasons deep learning is what it is today.

Dr. DJ Patil

Dr. DJ Patil essentially labelled the job category for a “data scientist” in his days as chief scientist at Linkedin. He has been a leader in the field, and has shaped the future of data policy in the U.S, serving as the first Chief Data Scientist at White House for Obama. He has been responsible for spreading Big Data across industries and government agencies, and even helped Facebook create their beginning data science programmes.

Caitlin Smallwood

Caitlin Smallwood is the VP of Data Science and Analytics, Netflix. She has applied data science with real life business value with her team that drives predictive decision models, algorithm / machine learning research, and experimentation science for all parts of the Netflix business.

Timeline

1977 the International Association for Statistical Computing (IASC) was founded.

The 1990s and early 2000s Data science has evolved to be a recognized and specialized field.

2005 Big data makes its debut.

2015 Artificial Intelligence (AI), Machine Learning, and Deep learning all make their debut.

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