Data Scientist

In this career guide, you’ll learn everything you need to know about data science as a career, from roles and responsibilities, to work environment and roadmap to becoming a Data Scientist.

Who's a Data Scientist?

Now, why has data scientist been referred to as the sexiest job of the 21st century by none other than Harvard Business review?

Consider this:

90% of the data in the world was created in the past two years (or so IBM has estimated). The world is slowly shifting online and the internet is becoming more accessible. It is estimated that every second 8 new people join the internet. Can you imagine the amount of data that is going to be generated in the future? All of this data can be analysed, understood and used by companies to understand their customers better. For example, if a coffee machine manufacturing company knew that you had just Google searched coffee machines, and best ones to buy, it would help them instantly target you through social media and other digital properties.

Major internet corporations like Amazon and Google want to keep an eye on the activities of the people on the internet for one simple reason- people are the currency of the internet. Meaning that data scientists work to figure out what people are doing online, what tools they are using and how these impact their behavior.

What you do online, where you do it, what device you use- everything is a data insight. In the world of data science, there are three core problems: acquiring data, doing the math and taking action.

Think you’d like to know more about what a Data Scientist does? Then read on…

What will you do?

Solving analytical problems

Understanding the business problem and convert it into an analytical problem. All businesses want to harness data to come up with solutions for their company.

Identifying data sources

Identify the right data sources to solve the problem.

Extracting data from different sources

Extract the data from data sources that are identified. A lot of data scientists create their own programs to analyse data. But some of them use big data models. These models are industry standard and can help you when you are working with large amounts of data.

Analysing data

Analyse the data. Use the correct validation methodology and the metrics for this. You will be working with an extremely large bunch of numbers and it can get overwhelming if you don’t use the correct processes. Do exploratory analysis and understand more about the data.

Creating hypothesis/models

Create a hypothesis/model. Evaluate the results and rebuild the models- this helps you to be more accurate. Say users buy more household items during the Diwali season and prefer cash on delivery since they are expensive items, you figure out their buying patterns online and come up with a hypothesis. Similarly, do users come on the website before Diwali, or on the day?

Coordinating with engineers

Co-ordinate with engineering teams and put the model in a production environment. This is a key part of your job. What happens after this will affect the company on a core level so you need to do a good job of it.The results of your hypothesis will be used by the sales team, the marketing team and the advertising team. They will come up with innovative strategies to attract consumers during the Diwali season to your website. Maybe they will push more discounts on household goods. Maybe they will increase advertising budgets for the “Cash on Delivery” promise. Or if the average age is 40 and above, their advertisements will be made to relate to that age group.

Measuring the performance

Continuously measure the model performance over time. You will also be involved in maintaining and rebuilding it as required. What if the consumer patterns changed over the next year? What if more consumers get accustomed to paying by card for expensive items too? This needs to be incorporated so that your model is useful later too.

Where will you work?


You will work in modern offices with high-end computer systems. Usually, your job timings will stay fixed. However, in case of an immediate requirement of data, you may need to put in some extra time.

How do you get there?

This stream won’t help you make an entry into this field.

This stream won’t help you make an entry into this field.

STEP 1: Class XI-XII/Junior College

Go to high school or junior college and study science and mathematics.

STEP 2: Graduate Degree

Get a Bachelor in Science Information Technology (B.Sc. or B.Tech) or B.Sc in Computer Science/IT/Computer Application/Software Engineering/Mathematics/Statistics. You should also study online courses in programming software like Python and Hadoop.

STEP 3: Internship

Many companies offer internships for data analysts. You can join any e-commerce or information technology (IT) company and do an internship to gain practical knowledge about your work.

STEP 4: Land a Job

After completing your education, join a reputed data or business analytics company as a junior data analyst. Congratulations, you are now officially a Data Scientist!

STEP 5: Postgraduate Degree

A postgraduate degree helps in getting better jobs and making more money. You can pursue a Diploma in Data Science (PGDDS), a full-time Post Graduate program in Data Science Business Analytics and Big Data (PGP-BA-BigData), or a Masters in Data Science (MS).

What skills would you need?

Technical Skills

Technical Skills

As a marketer, you will work on different platforms to communicate with people. If you don’t understand how they work, you won’t be able to come up with the best strategy. You will also use various kinds of software like Google Adwords, MailChimp, SurveyMonkey, etc., during your promotions. You need to know how they work, and how to use them to your advantage.

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Analytical and Data Skills

Analytical and Data Skills

You will receive huge amounts of data from your client. This data will have to be analysed effectively so you can find ways that will help your client reach their customers is the best way possible. While it is true that the more data you analyse, the more insights you generate; analytical thinking will help you narrow down your search and find your key actionable items, thus saving you a lot of time and resources.

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Communication Skills

Communication Skills

It’s harder than ever to get people’s attention, what with so much information being thrown at them. But, people cannot resist a good story. Every good marketing campaign has a story to tell their customers and relate to them on an emotional level. Marketers who tell great stories through their marketing campaigns are always in great demand.

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How do you make it to the top ranks?

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Junior Data Analyst

Your career in data science starts as a junior data analyst. You will learn to extract data from various sources and combine all the data in the desired format. You will follow the instructions given by senior data analysts.

Senior Data Analyst

As a senior data analyst, you will follow the instructions given by the data scientist. You will train the junior data analysts and supervise their work. You will create tools and software for collecting data and get them approved by the data scientist.

Chief Data Scientist

As you progress, your responsibilities will increase. You will head a team of junior and senior data analysts and oversee their work. You will also need to conduct meetings to give your team instructions on what has to be done.

Pursuing your career locally VS abroad

Data science combines elements from computer science, mathematics and statistics. So, it is common for students to study a related bachelor’s degree in mathematics or computer science and then pursue a master’s in data science for one or two years. A data science degree can cost roughly somewhere between INR 1,00,000 – 14,00,000 in India. You can do a BSc, BTech, BBA related to the field of Data Sciences. For a masters or PG, you would require a UG degree in a related field.

Click here to look at the best data science programmes in India.

Students can choose to study an undergraduate degree in data science, which will take around three or four years, depending on the country. The average tuition fees for a bachelor’s in data science in the US will cost you around US$35,100 (INR 25 lakhs a year). In Australia, the average tuition fees is AU$ 38,900 (INR 19.29 lakhs a year) and in Canada, the average tuition fees will cost you around C$ 35,400 (INR 19.59 lakhs a year).

A master’s degree in data science is much more common, with postgraduate science degrees available in many countries around the world. A master in sciences should cost you anywhere between INR 10.9 lakhs to INR 45 lakhs a year, with the average being around INR 23.7 lakhs for a year. To apply abroad, you would be required to show that you are proficient in English through your IELTS, PTE, or TOEFL scores. Additionally, you might be asked to furnish GRE or GMAT scores to the institute.

Click here to look at the best data science programmes abroad.

How much would you get paid?

The exact number will depend on your skill set, relevant work experience, and your qualifications. But we can give you a general idea.

What are your career options?

Machine Learning Engineer

Machine learning engineers create data funnels and deliver software solutions. By combining software engineering and data analysis, you will enable machines to learn without the need for further programming. In addition to a master’s degree that has machine learning as an element, it is essential to have experience in computer programming to get into this field.

Application Architect

The job of an application architect lies in the design and analysis of software projects. You will create new applications or improve existing applications, run software tests, develop product prototypes and create technical documents and manuals related to application development. A master’s degree in data science or computer science should be enough but in some cases, application architects need to possess industry certification in programming languages and architecture design.

Enterprise Architect

An enterprise architect is responsible for the upkeep and maintenance of an organisation’s IT networks and services. As an enterprise architect, your primary duty lies in overseeing, improving and upgrading enterprise services, software and hardware. You will need around five to ten years of IT experience before you can step into the role. Most employers are looking for someone who has experience with SQL, data sourcing, enterprise data management, modelling, business strategy, auditing and compliance.

Data Architect

A data architect is required to collaborate with IT teams and management to devise a data strategy that addresses industry requirements. You will be responsible for visualising and designing an organisation’s enterprise data management framework. Some companies need data architects who specialise in data modelling techniques; others may want experts in data warehousing, ETL tools, SQL databases or data administration.

Infrastructure Architect

Infrastructure architects design and implement information systems to support the enterprise infrastructure of an organisation. You must ensure that all systems are working at optimal levels and support the development of new technologies and system requirements. To be qualified for the job, it is necessary to have many years of experience in developing network infrastructure solutions and need proficiency in ITIL strategy, network security, network components, active directory and protocols.

Data Engineers

Data engineers build reservoirs for data and play a key role in managing the reservoirs as well as the data churned out by digital activities. They develop, construct, test, and maintain data-storing architecture — like databases and large-scale data processing systems. Much like constructing a physical building, a big data engineer installs continuous pipelines that run to and from huge pools of filtered information, from which data scientists can pull relevant datasets for their analyses. In addition to a background in computer science, engineering, applied mathematics or a degree in other related IT fields, you’ll need experience with multiple programming languages, including Python and Java, and knowledge of SQL database design.

Business Intelligence Developer

The Business Intelligence (BI) developer works collaboratively with end users to develop reporting systems that provide accessible information for decision-making. The BI developer uses warehouse data to solve organisational problems through reports, analysis and data visualisation. Some of the most beneficial features of business intelligence are the ability to recognise business growth opportunities, raise profit shares, determine employee productivity, detect risks and threats, and reduce wastage and costs. While most of these skills are tech-related, a BI developer also needs to have strong communication skills to describe complex technical information to the non-BI developers in the company.

You’ve only scratched the surface.

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