Data science has rapidly become a popular career option over the years. With the growing possibilities of artificial intelligence and other areas, data science has come to be a relevant field of research and work.

If you too are interested in data science as a career, then this blog will help you provide information about its potential. Let us begin with the basics of data science and move on to other essential questions.

What is data science?

Data science is a multidisciplinary domain which makes use of statistics, algorithms, data analysis and machine learning techniques to study patterns and other phenomena. The insights gained from that are then used for problem-solving and further research in several different scientific and technical areas.

What does a data scientist do?

Data scientists study given data to discern trends or characteristics. They employ a number of relevant techniques and research models to make sense of the data. The conclusions they come to can then be used to provide businesses and agencies with important insights. These results can then be used by the businesses to strategize and design better policies or products.

Besides this, data scientists can also help in developing the tools used in data science. They play key roles in creating algorithms, testing, and research, and building other tools.

Role Major responsibilities
Data Scientist Studying and analysing data, Using and building research tools and algorithms, Providing companies or businesses with useful insights based on data

Why build a career in data science?

All sorts of businesses today invest in data science and analysis to make better decisions for both themselves and their customers. Data scientists have become an asset to most companies and teams.

Scope of data science

• In India, major sectors like healthcare, pharmaceuticals, banking, telecommunications, e-commerce, and media require data scientists.

• India is second only to the US when it comes to data science jobs. 1 out of every 10 data science or analytics jobs is accounted for by India. (Quartz India)

• There are up to 50,000 data science job vacancies in India right now, which means plenty of opportunities for skilled job seekers to look forward to. (Business Today)

Demand for data science

• Currently, the biggest employer of data scientists is the banking and finance sector, comprising about 44% of total data science jobs. By 2020, India will create 39,000 more data science jobs spanning sectors like agriculture and aviation.(Business Today)

• Evolving technologies mean that data science will see a big demand in fields like AI, cyber security, space exploration and driverless transportation too. (The Economic Times)

Pros and cons of a career in data science

Pros Cons
Plenty of job openings Requires good knowledge of multiple disciplines and tools
High demand in major sectors Low possibilities for independent research work
Creative application of mathematical and statistical skills The use of data analysis results are controlled by the sector or company’s needs and demands

How to become a Data Scientist?

Becoming a data scientist is an exciting journey that requires a combination of education, skills, and experience. Here is a detailed step-by-step guide on how to become a data scientist:

  1. Educational Background:

    • Bachelor's Degree: Start by obtaining a bachelor's degree in a relevant field such as computer science, statistics, mathematics, engineering, physics, or any other quantitative discipline. A strong foundation in mathematics and programming is essential.

    • Advanced Degree (optional): While not mandatory, having a master's or Ph.D. in data science, statistics, computer science, or a related field can significantly boost your chances of landing data scientist roles, particularly in more competitive job markets.

  2. Programming Skills:

    • Learn Python or R: Both Python and R are widely used programming languages in data science. Python is versatile, easy to learn, and has extensive libraries like NumPy, pandas, and scikit-learn. R is specialized for statistics and data analysis.

    • SQL: Understanding SQL is crucial for querying and manipulating data stored in databases, which is a common task in data science.

  3. Statistics and Mathematics:

    • Understanding statistics is vital for analyzing data, building models, and drawing meaningful insights. Focus on probability, hypothesis testing, regression, and other statistical concepts.

    • Linear algebra and calculus: Knowledge of linear algebra and calculus is essential to grasp the underlying concepts of machine learning algorithms.

  4. Data Manipulation and Visualization:

    • Data Cleaning: Data scientists spend a significant amount of time cleaning and preparing data. Learn techniques to handle missing data, outliers, and inconsistent formats.

    • Data Visualization: Master data visualization libraries like Matplotlib, Seaborn (Python), or ggplot2 (R) to create compelling visuals that aid in data exploration and communication.

  5. Machine Learning:

    • Familiarize yourself with various machine learning algorithms such as linear regression, decision trees, random forests, support vector machines, neural networks, etc.

    • Explore model evaluation techniques like cross-validation, confusion matrices, precision-recall curves, and ROC curves.

    • Practice implementing machine learning algorithms using libraries like scikit-learn (Python) or caret (R).

  6. Big Data Technologies:

    • Get acquainted with big data technologies like Apache Hadoop, Spark, or databases like MongoDB to handle large datasets efficiently.
  7. Data Science Tools and Libraries:

    • Familiarize yourself with popular data science tools and libraries such as Jupyter Notebook, Anaconda, TensorFlow, Keras, PyTorch, etc.
  8. Projects and Portfolio:

    • Work on personal data science projects that demonstrate your skills and understanding of various concepts. Create a portfolio to showcase your work to potential employers.
  9. Online Courses and Certifications:

    • Consider enrolling in online data science courses and certifications from reputable platforms like Coursera, edX, or Udacity. These courses can help fill in knowledge gaps and provide recognition for your efforts.
  10. Networking and Internships:

  • Attend data science meetups, conferences, and networking events to connect with professionals in the field.

  • Apply for internships or entry-level positions to gain real-world experience and exposure to industry practices.

  1. Continuous Learning:
  • Data science is an evolving field, so stay updated with the latest trends, research papers, and new tools.
  1. Apply for Data Science Roles:
  • Tailor your resume to highlight relevant skills and experiences.

  • Apply for data scientist positions in companies or organizations that align with your interests and career goals.

  1. Interview Preparation:
  • Practice technical interviews, including coding challenges and machine learning problem-solving.

  • Be prepared to answer behavioral questions that demonstrate your communication and teamwork skills.

  1. Continuous Improvement:
  • Data science is a continuous learning journey. Keep challenging yourself with new projects, learning opportunities, and problem-solving exercises.

Remember that becoming a data scientist takes time and effort. Stay persistent, keep learning, and build a strong foundation of skills and knowledge. With dedication and the right approach, you can succeed in this exciting and rewarding field.

Qualification required for data science

Since data science uses a multidisciplinary approach to problem solving and analysis, you need a strong background in mathematics, statistics, and computer science before you can become a data scientist. Preferences are given to candidates with a good knowledge of programming languages and relevant work experience too.

Degree Field of Study
Bachelors Maths, Statistics, Computer Science, IT, Engineering, Physics
Masters Data Science, Applied Maths, or related fields

A significant percentage of data scientists also earn a PhD in their fields. It is important to learn programming skills as part of your university or as add-on skills. Online courses and certifications, internships and work experience also go a long way in a data science career.

Data science courses and certifications

Here is a list of some popular data science courses and certifications.

  1. Website: Coursera/University of Michigan
    Course: Applied Data Science with Python Specialization
    Price: INR 3,474 per month
    Format: Online
    Learning duration: Self-paced (Approx. 5 months)

  2. Website: Udacity
    Course: Intro to Machine Learning
    Price: Free
    Format: Online
    Learning duration: Self paced (Approx. 6 months)

  3. Website: Dataquest
    Course: Data Analyst in R
    Price: Free
    Format: Online
    Learning duration: Self-paced

  4. Website: Udemy
    Course: Python for Data Science and Machine Learning Bootcamp
    Price: INR 12,480
    Format: Online
    Learning duration: Self-paced

  5. Website: Coursera
    Course: IBM Data Science Professional Certificate
    Price: INR 2,765 per month
    Format: Online
    Learning duration: Self-paced (2 months approx.)

  6. Website: Coursera/John Hopkins University
    Course: Data Science Specialization
    Price: INR 3,474 per month
    Format: Online
    Learning duration: Self-paced (8 months approx.)

  7. Website: Udemy
    Course: The Data Science Course 2019
    Price: INR 12,800
    Format: Online
    Learning duration: Self-paced

  8. Website: Datacamp
    Course: Introduction to Machine Learning
    Price: Subscription-based (INR 2020 per month for 247 courses)
    Format: Online
    Learning duration: 6 hours

  9. Website: Edureka
    Course: Data Science Certification Course using R
    Price: INR 24,899
    Format: Online
    Learning duration: 30 hours

  10. Website: Edx/Harvard University
    Course: Professional Certificate in Data Science
    Price: $441.9/INR 30,723(approx.) for 9 courses
    Format: Online
    Learning duration: Self-paced (2-4 months)

  11. Website: Digital Vidya
    Course: Data Science Master Program
    Price: INR 34,900 + GST
    Format: Online
    Learning duration: Self-paced (Minimum 21 weeks)

  12. Website: Simplilearn
    Course: Data Scientist Master’s Program
    Price: INR 44,999
    Format: Online
    Learning duration: Self-paced or online classroom

  13. Website: Coursera/Duke University
    Course: Statistics with R Specialization
    Price: INR ₹3,474 per month
    Format: Online
    Learning duration: Self-paced (Approx. 7 months per course)

  14. Website: Upgrad/IIIT Bangalore
    Course: PG Diploma in Data Science
    Price: INR 2,85,000
    Format: Online
    Learning duration: 11 months

Data science jobs in India

Let us look at the vacancies for data science and related jobs in India.

Jobs Naukri Indeed Monster LinkedIn Total
Data Science 5.2k 516 125 5.9k 11.7k
Data Analyst 2.7k 351 491 2.3k 5.8k
Data Engineering 6.9k 407 294 3.9k 11.5k
Business Analyst 9.4k 334 2.1k 4.5k 16.3k
Statistical Analyst 137 38 2 131 308
Machine Learning 6.4k 156 188 5.5k 12.2k

Note: The data provided above was last updated in March 2019. It is subject to change.

Job roles in data science

Role Major Responsibilities
Data Scientist Analyzing raw data, using data analysis techniques and tools, sharing insights with companies, strategizing
Data Analyst Processing data sets, visualization, optimisation, creating algorithms, performing queries on databases
Data Engineer Using Big Data technology and Hadoop, creating useful software, working with SQL technologies, providing data warehousing solutions
Business Intelligence Professional Identifying how Big Data can be used, interpreting high volumes of data, providing relevant insights for business solutions
Statistician Using statistical tools, organizing data, extracting information from data sets, creating statistical theories and methodologies
Machine Learning Engineer Carry out A/B testing, building and implementing algorithms and data pipelines, producing data-based products or services, helping with operations,

Data science salaries

Average data science job salaries can vary according to skills and experience. Here is an average annual salary data for data science and related job roles.

Job Role Average Annual Salary (0-3 Years of Experience) Salary Range (LPA)
Data Scientist 6.3 lakhs 3 to 20
Data Analyst 4.9 lakhs 1.9 to 8.2
Data Engineer 5 lakhs 3.4 to 20
Business Analyst 5.8 lakhs 2.5 to 10
Machine Learning Engineer 7 lakhs 3.2 to 20
Statistical Analyst 5.8 lakhs 1.9 to 10

Data source: AmbitionBox

Skills to become a Data Scientist

Take a look at the skills necessary to work in data science.

  • Core skills:
Skill Details
Mathematics Strong understanding of multivariable calculus and linear algebra
Statistics Knowledge of tools and techniques to find out patterns and co-relations in data
Programming and languages Ability to use programs like R, Python, SQL and Hadoop
Data wrangling Dealing with imperfect or inconsistent data and unstructured data for analysis and extracting useful information
Data visualization Using visualization tools to present the information found from data analysis and communicating them to the company
Machine learning Working knowledge of algorithms and other facets like neural networks and adversarial learning
  • Soft skills:
    Curiosity, creativity, communication, critical thinking, business sense, and teamwork

Data science tools

Job Role Tools
Data Scientist Apache Giraph, Hadoop, Apache Pig, Apache Storm, D3.js, Network X, GNU Octave, Rapid Miner, etc.
Data Analyst Spark, Excel, KNIME , pandas, Spotfire, Bokeh, etc.
Data Engineer Hive, Mesos, HBase, Cascading, R Studio, Scala, etc.
Machine Learning Engineer Scikit-learn, BigML, Data Robot, GraphLab Create, Logical Glue, ML Base, Tensor Flow, etc.

Common career paths to become a data scientist

Here are some common ways to become a data scientist.

Career Path 1:
Earn a Bachelors degree in Computer Science → Get certification in Big Data/Data Analytics → Join as a Data Scientist or Engineer intern or employee at a firm

Career Path 2:
Earn a Bachelors and/or Master’s degree in Applied Mathematics or Statistics → Complete online courses in Programming languages and Data Science/analytics → Complete projects on platforms like Kaggle and build a portfolio → Apply for jobs in Data science

Career Path 3:
Earn a Bachelors degree in Physics → Take online courses in Programming languages→ Collect professional certifications in Data Science and Machine Learning → Intern or get a job as a Data Scientist or Machine Learning Engineer

Career Path 4:
Earn a Bachelors degree in Business Administration → Opt for a Master’s degree in Data Science or Marketing/Business Analytics → Intern or find a job as a Data Analyst

Note: The above career paths are just examples of common career paths. There is no fixed career path to start a career in data science. The career steps can vary as per the background, interests, and skills of the individual.

Tips to get a job in data science

These are some essential skills you must have to make a career in data science.

1. Build strong mathematical and statistical skills

A solid base in applied math and statistics can be very helpful in data science. It is a key skill for analysing the large volumes of data and data trends collected by big companies.

2. Learn programming languages

Languages like Python and R are very relevant for getting a head-start in a data science career. Knowing how to code is also essential for working with big data.

3. Courses and certifications

There are a whole variety of pertinent and interesting data science courses and programs, both online and offline. These programs include data mining, statistical tools, coding, machine learning, etc. A couple of such certifications in your pocket will help you increase your employability chances as a skilled data scientist.

4. Work on data science projects

Learning how to use tools and techniques are not the only way to become a data scientist. Building a portfolio by working on real projects, either as an intern in a relevant company or independently on platforms like Kaggle can be very beneficial.

5. Develop business acumen

Companies want to hire data scientists for the valuable inputs they can provide during the process of strategizing and product-building. Knowing how to use the results of data analysis and present them as creative insights or business solutions is a brilliant way to sharpen your credibility as a data scientist.

Find and apply for Data Science jobs.

Data science blogs

Here is a list of popular data science blogs:

  1. Data Science Central
  2. NYC Data Science Academy
  3. Dataconomy
  4. Data Camp
  5. Inside Big Data
  6. No Free Hunch
  7. Smart Data Collective
  8. KD Nuggets
  9. Dataversity
  10. Revolutions
    1. Towards Data Science

Data science books

Here is a list of popular data science books:

  1. What is Data Science by Mike Loukids
  2. The Master Algorithm by Pedro Domingos
  3. Business Analytics: The Science of Data-driven Decision Making by U Dinesh Kumar
  4. Data Structures and Algorithms Made Easy by Narasimha Karumanchi
  5. Marketing Data Science by Thomas W. Miller
  6. Big Data Demystified by David Stephenson
  7. Machine Learning by Tom M. Mitchell
  8. Python Machine Learning By Example by Yuxi (Hayden) Liu
  9. R for Data Science by Hadley Wickham
  10. Data Structure and Algorithmic Thinking with Python by Narasimha Karumanchi

Data science YouTube videos and channels

Here is a list of popular data science videos and channels on Youtube:

  1. Data science tutorial for beginners by Edureka
  2. Introduction to date science by Simplilearn
  3. StatQuest with Josh Starmer
  4. Itversity
  5. Data School
  6. Siraj Raval

Data science forums

Here is a list of popular data science forums:

  1. IBM Data Science Community
  2. Data Science on Reddit
  3. Data Science Central
  4. Analytics Vidhya
  5. Quora – Data Science
  6. MachineLearning on Reddit
  7. Kaggle Forum
  8. Data Science Stack Exchange
  9. R Nabble

Data science webinars

Here is a list of popular data science webinars:

  1. Dataversity
  2. DataScience.com
  3. KDnuggets
  4. RapidMiner
  5. R and Tableau
  6. Data Science Central
  7. SAS
  8. TDWI
  9. Anaconda

Top companies to follow

Here are some companies which are working with big data and machine learning:

IBM , Oracle , Amazon , Microsoft , Netflix , Google , Pinterest , Twitter , TCS, MakeMyTrip, HDFC, Manthan, Razorthink , SocialCops, Sigtuple, Fractal Analytics

FAQs: What Is Data Science And How To Become A Data Scientist?

  1. What is data science?
    Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data using various scientific methods, algorithms, and processes. It combines elements of statistics, mathematics, computer science, and domain expertise to make informed decisions and predictions.

  2. What do data scientists do?
    Data scientists analyze data, build predictive models, and use statistical techniques to gain actionable insights. They work on data cleaning, data visualization, and apply machine learning algorithms to solve complex problems and aid in decision-making processes.

  3. What skills are essential to become a data scientist?
    To become a data scientist, you need proficiency in programming languages like Python or R, a strong foundation in statistics and mathematics, data manipulation, data visualization, machine learning, and big data technologies. Effective communication and problem-solving skills are also crucial.

  4. What educational background is required to become a data scientist?
    While a bachelor's degree in a quantitative field such as computer science, mathematics, statistics, engineering, or physics is a common starting point, having a master's or Ph.D. in data science or related fields can enhance job prospects and opportunities.

  5. Are there any online courses or certifications for data science?
    Yes, there are numerous online courses and certifications available on platforms like Coursera, edX, Udacity, and others that offer comprehensive data science training.

  6. Do I need to know programming to become a data scientist?
    Yes, programming is a fundamental skill for data scientists. Learning languages like Python or R is essential for data manipulation, analysis, and building machine learning models.

  7. What kind of projects should I work on to become a data scientist?
    Aspiring data scientists should work on projects that involve data cleaning, analysis, visualization, and machine learning. Personal projects that demonstrate practical skills and problem-solving abilities can be a valuable addition to your portfolio.

  8. Is a data science internship necessary to get into the field?
    While not mandatory, a data science internship can provide valuable real-world experience and exposure to industry practices. It can enhance your resume and increase your chances of landing a full-time data scientist role.

  9. What are the typical interview questions for data science positions?
    Data science interviews often include technical questions related to programming, statistics, and machine learning. Behavioral questions may assess communication skills and ability to work in a team. Sample problems and projects may be given to evaluate problem-solving abilities.

  10. How can I keep up with the latest trends and advancements in data science?
    Staying up-to-date in data science requires continuous learning. Follow industry blogs, attend conferences, read research papers, and participate in online communities to keep yourself informed about the latest trends and technologies.

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