Your Guide to Data Science Careers (+ How to Get Started)

Data science continues to rise as one of the most in-demand career paths in technology today. Beyond data analysis, mining, and programming, data scientists code and combine it with statistics to transform data. These insights can help businesses derive their return on investment (ROI) and organisations measure their social impact.

Data science is interdisciplinary and integral to society’s basic functions, such as restocking grocery stores, tracking political campaigns, and keeping medical records. Participating in this growing field can yield a fascinating and fulfilling career.

You’ll find many career opportunities within data science. Here’s a guide to data science, the skills required, job types, and how to get there.

What is data science? Definition, skills, and job outlook

Data science grew out of statistics and data mining. It is a new specialty, with the Data Science Journal debuting only in 2002. It sits at the intersection of software development, machine learning, research, and data science, and it falls under the combined categories of computer science, business, and statistics. Data professionals create algorithms to translate data patterns into research that informs government agencies, companies, and other organisations.

Data science exists because information technology is evolving rapidly, with a growing need for professionals to make sense of it all. 

Skills required in data science

In a field like data science, it helps to have several technical skills before diving in, such as:

• Deep knowledge and familiarity with statistical analysis

• Machine learning

• Deep learning

• Data visualisation

• Mathematics

• Programming

• Ability to manage unstructured data

• Familiarity with SAS, Hadoop, Spark, Python, R, and other data analysis tools

• Big data processes, systems, and networks

• Software engineering

A career in data science is wider than technical knowledge. You’ll work on teams with other engineers, developers, coders, analysts, and business managers. These workplace skills will help take you further:

• Communication skills

• Storytelling

• Critical thinking and logic

• Business acumen

Data science job outlook

Data science jobs are booming in India. The industry has seen 650 percent growth since 2012, and experts project an increase of 11 million data scientist jobs in India by 2026, representing an industry worth 230.81 billion USD [1].

The demand for data scientists is so high that their average salary is significantly higher than that of roles with similar skills. For example, India Today reported that moving from a marketing analytics job to a data science job can call for a 37 percent raise. A digital analytics professional who switches to data science can look forward to an average raise of 31 percent [2]. 

Data science job roles

You can choose from many data science jobs. All of them are integral to making key business decisions. Several job types below will often work together on the same team.

Data scientist

Data scientists build models using programming languages such as Python. They then transform these models into applications. Often working as part of a team, for example, with a business analyst, a data engineer, and a data (or IT) architect, they help solve complex problems by analysing data and making predictions. This role is typically considered an advanced version of a data analyst.

Average Indian salary per year: ₹12,60,127 [3]

Skills needed: Statistics, mathematics, machine and deep learning, programming skills, data analysis, big data processes, and tools like Hadoop, SQL, and more.

Education: While you won’t find absolute requirements to become a data scientist, 34 percent of India’s data scientists have a bachelor’s degree, 50 percent have a master’s degree, and six percent have a Ph.D. [4]. Professional certificates can also help career switchers reach their goals. 

Data analyst

Data analysts use structured data to solve business problems. Using tools such as SQL, Python, and R, statistical analysis, and data visualisation, data analysts acquire, clean, and reorganise data for analysis to spot trends to drive various business insights. They tend to bridge the gap between data scientists and business analysts.

Average Indian salary per year: ₹8,52,000 [5]

Skills needed: Programming languages (SQL, Python, R, SAS), statistics and maths, data visualisation

Education: Bachelor’s or master’s degree in mathematics, computer science, finance, statistics, or a related field

Data Architect

Data architects create the blueprints for data management systems, designing plans to integrate and maintain all data sources. They oversee the underlying processes and infrastructure. Their main goal is to enable employees to gain access to information when they need it. 

Average Indian salary per year: ₹23,50,000 [6]

Skills needed: Coding languages such as Python and Java, data mining and management, machine learning, SQL, and data modelling

Education: A bachelor’s degree in data, computer science, or a related field plus additional certification. If you are switching careers, a bootcamp or Professional Certificate can help develop your skills in data management.

Data Engineer

Data engineers prepare and manage large amounts of data. They also develop and optimise data pipelines and infrastructure, getting the data ready for data scientists and business analysts to work with. Data Engineers make the data accessible so businesses can optimise their performance.

Average Indian salary per year: ₹18,25,395 [7]

Skills needed: Programming languages such as Java, understanding of NoSQL databases (MongoDB), and frameworks like Apache Hadoop

Education: A bachelor’s degree in maths, science, or a business-related field is helpful. Professional Certificates and bootcamps are also an option to brush up on skills.

Machine learning engineer

This role is not entry-level but one you can build toward as a data scientist or engineer. Machine learning uses algorithms that replicate how humans learn and act, interpreting data and building accuracy over time. As part of a data science team, machine learning engineers research, make, and design artificial intelligence that facilitates machine learning. They also liaise between data scientists, data architects, and more. 

Average Indian salary per year: ₹11,00,000 [8]

Skills needed: Knowledge of tools such as Spark, Hadoop, R, Apache Kafka, Tensorflow, Google Cloud Machine Learning Engine, and more. Understanding data structures and modelling, quantitative analysis, and computer science basics is also helpful. 

Education: You’ll need a bachelor’s degree in computer science and a diploma or postgraduate degree to complete your education. Often, a master’s degree or even a Ph.D. in computer science or related fields is expected. Gain an introduction to this field by enrolling in one of Coursera’s popular Courses, Machine Learning.

Business analyst

As a business analyst, you’ll use data to form business insights and make recommendations for companies and organisations to improve their systems and processes. Business analysts identify issues in any part of the organisation, including staff development and organisational structures, so businesses can increase efficiency and cut costs.

Average Indian salary per year: ₹8,42,000 [9]

Skills needed: Using SQL and Excel, data visualisation, financial modelling, data and financial analysis, business acumen

Education: Bachelor’s degree in economics, finance, computer science, statistics, business, or a related field, plus a postgraduate diploma or certificate. 

The path to a data science career

With so many exciting options in data science, you may be wondering where to begin. Whether you are just starting your career or switching from another one, here are the steps you can take to build toward your future in big data or machine learning.

Education: What should I learn?

A degree or certificate can be a great entry point for any data science role. However, these aren’t your only options.

Bachelor’s degree: Many data science jobs require at least a bachelor’s degree in data science, business, economics, statistics, maths, information technology, or a related field. These programmes teach you to analyse data and use numbers, systems, and tools to solve problems. 

But if your bachelor’s degree is in the arts or humanities, don’t fret. You can apply your critical and creative thinking skills to a data science career. If you don’t have a degree, you can also find several options to get started.

Online courses and Professional Certificates: Whether or not you have earned a bachelor’s degree, an online course or Professional Certificate can be helpful when applying for entry-level data science-related jobs.

You can list these courses on your resume or LinkedIn for additional credibility. Typically, these courses take a few months to complete (on a part-time basis) and will set you up for at least an entry-level position.

If you want to dive into a data science career, consider IBM's Data Science Professional Certificate or Stanford University’s Machine Learning Course.

Bootcamps: If you are willing to spend a few weeks or months pursuing a bootcamp, you have plenty of options to pivot and gain the necessary skills for a data science career. Some bootcamps are in-person with a cohort over a few weeks or months, while others are completed online or at your own pace. The benefits of an in-person bootcamp are the community and network you’ll have access to upon completion. 

Some popular options include:

• Online data science bootcamp from Analytics Vidha, an eight-month programme that lets you learn from industry experts in addition to getting hands-on experience

• Online data science bootcamp from Springboard, a programme that includes one-on-one mentorship 

• Online data science bootcamp from General Assembly, which offers an online data science course and an immersive data science bootcamp in New York and other cities. 

• Data science bootcamp from Data Science Dojo is entirely online but features live instructions and class schedules.

Skills needed

You’ll need technical and workplace skills to succeed in a data science career. Here are some of the common skills employers look for in data science roles, whether you're aiming to become a data scientist or a machine learning engineer:

Technical skills

• Statistics

• Mathematics

• Data visualisation

• Machine and deep learning

• Data wrangling

• Programming

• Data engineering

• Cloud computing

• Business acumen

 Workplace skills

• Curiosity

• Communication

• Storytelling

• Adaptability and flexibility

• Critical thinking

• Problem-solving

• Teamwork

Experience: How do I get a job?

Once you’ve completed a course or certificate and gained the necessary skills, you’ll want to get some work experience.

Entry-level job or internship: To land your first job or internship, you’ll want to rely on applying to jobs that specifically cater to those starting in the data science field. That way, you can feel supported as you prove your worth, develop your skills, and advance your career.

Some job seekers report applying for hundreds of jobs before obtaining an interview. But don’t be discouraged because data science roles are also in demand. Your hard work will pay off.

Interviews: Once you’ve secured an interview, practice communicating with a non-technical friend about your process. Pretend your interviewer has no idea about your project so you can talk through your decisions about which tools you choose and why you coded an algorithm in a certain way. You’ll want to prove you are familiar with the languages and systems you use on the job.

Explore data science with Coursera

Boost your career in data science by enrolling in IBM’s Data Science Professional Certificate programme. You’ll learn to analyse data and communicate results to inform data-driven decisions in 11 months or less, all at your own pace.