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Acquiring a powerful place in contemporary times, pursuing a career in Data Science has created quite a buzz in the world and perhaps, will continue to stay in demand, thanks to the continuous technological advancements. If you possess the skills necessary for building a career in this field, such as problem-solving, persuasive and listening skills, critical thinking and analytical skills, then there are plenty of opportunities waiting for you to grab. Through this blog, let us take you on an educational tour on everything you want to know about a career in this field.
What is Data Science Course?
In simpler words, it is a study of data, which involves a blend of algorithms, principles of machine learning and various other tools that are used to record, store and analyze data to obtain significant and useful information. Data scientists extract and interpret data from an extensive range of sources like log files, social media, sensors, customer transactions, to unlock useful information to influence the decisions of a business and stimulate competitive advantage over other counterparts.
Why Pursue Data Science?
Itis among the most demanded careers. Graduates holding a degree in Data Science have numerous job opportunities as it is reported to be the fastest-growing job on LinkedIn and according to Forbes, demand for data scientists will increase by 28 percent by 2026. The fact that students from business and management backgrounds can also choose this course at Postgraduate levels instead of an MBA degree, makes it a great major to choose for your higher studies. In India, a DS graduate can expect to earn INR 15-19 Lakhs per annum, this is considered a higher amount than any other popular profession such as Engineering. The average salary for a Data Scientist in the United States is $109,666 (INR 82,65,636) per year.
How Does Data Science Work?
Data science includes plenty of disciplines and expertise areas to produce a comprehensive, thorough and a rectified look into raw data. A data scientist must have expertise in the field of data engineering, math, statistics, advanced computing and must be able to communicate only the major bits that’ll help to operate innovation and efficiency. A data scientist is highly reliable on AI- Artificial Intelligence, particularly for its specializations of machine learning and deep learning which is used to create models and make prognosis using algorithms and other techniques. Data science generally has a 5-stage life cycle that requires different techniques, programs and skill sets as depicted in the table below:
| Stage 1- Capture | Data acquisition, data entry, signal reception, data extraction |
| Stage 2- Maintain | Data warehousing, data cleansing, data staging, data processing, data architecture |
| Stage 3- Process | Data mining, clustering/classification, data modeling, data summarization |
| Stage 4- Communicate | Data reporting, data visualization, business intelligence, decision making |
| Stage 5- Analyze | Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis |
Data Science Uses
Data Science has a major contribution to providing convenience in today’s world, from the Healthcare sector to Cybersecurity.
- Anomaly detection to determine fraud, disease, crime and more.
- Automation and decision-making for background checks, credit worthiness etc.
- Classifications in an email server, for example classifying emails as “important” or “junk”
- Predicting sales, revenue and customer retention
- Pattern detection to find out weather patterns, financial market patterns and more.
- Recognition- facial, voice, text etc.
- Recommendations that are based on learned preferences such as recommendation engines can refer you to movies, restaurants and books you may like. For example, you search for a particular product and then you’re recommended with the same product of different brands.
Data Science Use Cases
A number of businesses can potentially benefit from Data Science. Almost every business can be made more systematic through data-driven optimization, and every type of customer experience can be improved with better targeting and personalization, all due to the opportunities created with the help of Data Science. Listed below are a few indicative use cases for AI- Artificial Intelligence-
- A mobile application created by an International Bank that offers immediate decisions to loan applicants by using learning-powered credit risk models and a hybrid cloud computing architecture that provides security as well.
- An ultra-powerful 3D-printed sensor is being developed by an Electronics firm which will guide driverless vehicles in the near future. The solution relies upon data science and analytical tools to increase its real-time object determining capabilities.
- An audience analytics platform has been created by a digital media technology company that allows clients to see the engagement of TV audiences as they’re offered a wide range of digital channels. The solution avails deep analytics and machine learning to accumulate real-time insights into audience behaviour.
- An urban police department created a statistical incident analysis tool which helps officers to understand when and where to utilize the resources to prevent crimes. The solution is data driven that creates reports and dashboards to increase circumstantial awareness for field officers.
Difference Between Data Science, Artificial Intelligence, and Machine Learning
It is equally important to understand other terms related to the field of Data Science, such as AI- artificial intelligence and machine learning. The table below depicts the difference between Data Science, Artificial Intelligence, and Machine Learning in simple terms.
| Data Science | Data Science means the intersection of various areas of statistics, scientific methods, and data analysis that are used to extract meaning and insights from data. |
| AI- Artificial Intelligence | AI is basically a computer or a robot controlled by a computer itself to do tasks that are usually done by humans. |
| Machine Learning | Machine Learning is one of the subsets of AI. Machine Learning involves different techniques that enable computers to figure out things from the data, solve complex problems through deep learning and deliver AI applications. |
The Data Science Lifecycle
The Life Cycle consists of 5 stages i.e. Capturing, Maintaining, Processing, Analyzing, and Communicating.
- Stage 1. This stage involves accumulating raw structured and unstructured data.
- Stage 2. This stage covers taking the raw data and putting it in a form that can be used further.
- Stage 3. Data Scientists take the prepared data and examine its patterns, ranges, and biases to detect how useful it will be in prognostic analysis.
- Stage 4. This stage involves performing various analyses of the data.
Stage 5. The final step is to have analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
Elements of Data Science
Being vast in nature, this field can be divided into three main elements, i.e. Business Intelligence, Machine Learning and Big Data. Let’s have a detailed look on these three components.
Business Intelligence
As every business is using computing technologies to operate, each organisation produces a large amount of data every day. On the careful analysis of this generated data, the data scientists present it in different graphs and charts. Thereafter, it helps the management make the best business decisions based on what the statistics represent.
Machine Learning
Involving mathematical and statistical models and algorithms, Machine Learning is adopted by almost all business organizations to prepare the machines into understanding and reacting to daily circumstances and progresses. Through the use of Machine Learning in the field of Data Science, a machine can predict various trends in the markets or financial systems, based on historical data patterns.
Big Data
With an exponential increase in internet users every day, the viewers or customers generate innumerable clicks in the form of videos, images, articles, comments, orders, etc. Generally, all these activities result in the generation of unstructured data.
Data Scientists are responsible for turning that unstructured data into structured one.
Also Read:
Technical Skills Required in Data Science
Being an interdisciplinary field, DS requires not just one or two, but a diverse set of technical skills and knowledge in the field of Computer Science. Here are the following skills that a Data Scientist must possess, irrespective of the experience gained.
- Python Coding: Python is the most useful coding language in Data Science as it helps in the mining of data, developing models of machine learning or web scraping. Taking various formats of data, python can help you create and find datasets and import SQL tables into your code.
- R Programming: R is a program, generally made for data analysis and provides the formulas and methods for information processing and statistical analysis.
- Machine Learning and AI: Learning different techniques of Machine Learning such as logistic regression, decision tree, supervised machine learning, time series, computer vision, outlier detection, survival analysis, natural language processing, etc is inherently significant to tackle several challenges in the field of Data Science.
- Hadoop Platform: When the volume of data is extremely massive, it can exceed the memory of the system and the Hadoop Platform helps Data Scientists to send or transfer the remaining data to different servers. This platform is also useful for data filtration, data sampling and summarization, exploration, etc.
- SQL: Structured Query Language (SQL) is a programming language which helps you communicate, access and manage the database by adding, subtracting or extracting the data. The Data Scientist needs to be proficient in SQL, as it is specifically designed to save time and reduce the amount of programming for difficult queries, through its compact commands.
Other skills that are essential to the field of Data Science include:
- Data Visualization
- Apache Spark
- Unstructured Data
- Business Acumen
- Communication and Persuasive skills
- Data Wrangling
- Algebra and Calculus
- Statistics
- Java
- Unix
- PHP
Roles and Responsibilities of a Data Scientist
After completing the education and acquiring the technical skills required in DS, here are some of the most common roles and responsibilities that you will be carrying out:
- Staying up-to-date with all the emerging tools and techniques in statistical modelling, machine learning, etc.
- Mine the data and generate a hypothesis to help achieve the high-level business objectives.
- Analyse and mine large data sets using Hadoop Platform.
- Work in collaboration with IT managers, statisticians, programmers and other experts for carrying out business decisions or creating products and services.
- To solve various analytical problems with incomplete datasets, data scientists develop specially tailored algorithms as well.
Eligibility Criteria for Data Science
To pursue a successful career in Data Science, the aspirants need to have graduated with certain specializations or subjects essential to the field. Here are the educational requirements that a student needs to fulfill.
- Higher secondary education (12th grade) with Physics, Chemistry and Maths, i.e. MPC subjects as compulsory.
- A B.Tech or B.Eng degree in Computer Science, Physical Science, Mathematics, Mathematics and Computing, Statistics or Engineering.
- An M.tech, MS or M.Eng degree in DS, Mathematics or any other related fields.
Best Courses in Data Science
- Computer Science – Data Science Concentration – BSc
- Computer Science (Data Science) – BSc (Hons)
- Master of Science [M.Sc] Data Science
- Master of Science [M.Sc] Applied Computation
- MSc Statistics- Data Science
- MPhil Machine Learning
- Master of Science- Social Data Science
- MA Statistics and Data Science
- MSc Engineering- Data Science
- MSc Health Data Analytics and Machine Learning
- MSc Applied Computing- Data Science
- MSc Artificial Intelligence
Diploma in Data Science
Several Diploma and PG Diploma Courses are available for students who want to pursue a Data Science Course without having to pursue full-time data science courses. The table below mentions the diploma course available for students:
| Diploma Course | Offered by |
| PG Program in Data Science | Praxis Business School, Kolkata |
| Postgraduate Diploma in Data Science | Symbiosis Centre for Distance Learning, Pune |
| Post Graduate Diploma in Data Science | Gujarat Technological University |
| Post Graduate Diploma in Data Science | MUIT Noida |
| PG Diploma in Data Science And Analytics | NIELIT Chennai |
Bachelors in Data Science
The table below mentions bachelor course offered in Data Science abroad:
| Bachelor’s Degree | University |
| B.Sc. Astrophysics and Data Science (Hons) |
Keele University, United Kingdom |
| B.Sc. Data Science & Analytics (Hons) |
Bournemouth University, United Kingdom |
| BSc Computer Science- Data Science | University of Ottawa, Canada |
| BSc Data Science | South Dakota State University, United States |
| Bachelor of Computer Science in Data Science (Honours) | University of Waterloo, Canada |
Masters in Data Science
The table below mentions the top universities offering masters in Data Science:
| University | Course |
| Manchester University, United Kingdom | MSc Data Science (Business and Management) |
| London School of Economics, United Kingdom | MSc Data Science |
| Stanford University, USA | MS in Data Science |
| Harvard University, USA | MS in Data Science |
| McGill University, Canada | Master of Data Science |
Data Science Course in IITs
The table below mentions the upcoming IIT data science certifications and data science course offered at the IITs along with the eligibility criteria for pursuing the course:
| Offered by | Course | Eligibility Criteria |
| IIT Delhi | MTech in Machine Intelligence and Data Science | You must be a graduate in science or designing field |
| IIT Indore | MSc in Data Science and Management | Working or fresh graduates, both are eligible to apply |
| IIT Guwahati | Bachelor’s in Data Science and Artificial Intelligence | On the basis of JEE Advanced 2021 counseling |
| IIT Madras | BSc in Programming and Data Science, Diploma in Data Science | Graduates or working professionals, both are eligible to apply |
| IIT Patna | BTech in Artificial Intelligence and Data Science | On the basis of JEE Advanced 2021 |
Data Science Courses Online
In today’s world of digital education, Online courses and certifications have become common for students. If you’re someone who wants to pursue an Online Data Science Course then you’re at the right place. Here we’ve mentioned the best Data Science Courses that are offered online.
Udemy
The table below mentions the online Data Science Courses offered by udemy along with the course fees:
| Course Offered | Course Fee |
| Data Science and Machine Learning Bootcamp with R | INR 3,499 |
| Python for Data Science and Machine Learning Bootcamp | INR 3,499b |
| Complete Machine Learning & Data Science Bootcamp 2022 | INR 3,499 |
| Python A-Z: Python For Data Science With Real Exercises | INR 3,499 |
| Data Science A-Z: Real-Life Data Science | INR 3,499 |
IBM
Listed below are the online Data Science Courses offered by IBM:
- IBM Data Science Professional Certificate
- Advanced-Data Science with IBM Specialization
- Applied Data Science Specialisations
- Data Science Fundamentals with Python and SQL Specialisations
- Introduction to Data Science Specialisation
Coursera
The table below mentions the Online Data Science Courses offered by Coursera along with the course duration:
| Course | Duration |
| IBM Data Science Professional Certificate | 11 months |
| Google Data Analytics Professional Certificate | 6 months |
| Applied Data Science with Python Specialization | 5 months |
| Data Science: Foundations using R Specialization | 5 months |
| Data Science: Statistics and Machine Learning Specialization | 6 months |
UpGrad
The table below mentions the online Data Science Courses offered by UpGrad along with the course fee:
| Course Offered | Course Fee |
| Executive PG Programme in Data Science | INR 2,99,000 |
| Master of Science in Data Science | INR 4,00,000 |
| Advanced Certificate Programme in Data Science | INR 99,000 |
| Professional Certificate Programme in Data Science for Business Decision Making | INR 1,50,000 |
edX
The table below mentions the edX Data Science Courses:
| Offered by | Course |
| Harvard University | Professional Certificate in Data Science |
| Harvard University | Data Science Machine Learning |
| Harvard University | Data Science: R Basics |
| IBM | Python Basics for Data Science |
| University of California, Berkeley | Professional Certificate in Foundations of Data Science |
| Massachusetts Institute of Technology | MicroMasters Program in Statistics and Data Science |
Simplilearn
The Online Data Science Courses offered by Simplilearn are as listed below:
- Data Science with R Certification Courses
- Artificial Intelligence and Data Science
- Introduction to Data Science
- Data Science with Python Certification Course
- Data Scientist
Google Data Science Certification
The details for the Google Data Science Certification are as mentioned:
| Offered by | |
| Prerequisites | There are no specific requirements to apply for Google data science certification, however, a professional with 3 or more years of experience in the industry with 1 or more years experience in designing and managing solutions using Google cloud is considered beneficial. |
| Validity | 2 years |
| Google Professional Data Engineer Exam Fee | INR 15,010 |
| Available Languages for Exam | English and Japanese |
| Exam Duration | 2 Hours |
| Exam Pattern | MCQs |
| Exam Mode | Remote Or At the Exam Centre |
Data Science Course Syllabus
Data Science Courses are offered at various levels such as undergraduate, postgraduate, Diplomas and certifications, and in various specializations so the syllabus for Data Science may vary. However, here we’ve mentioned general subjects that are taught in every Data Science Course:
- Introduction to Data Science
- Statistical Foundations for Data Science
- Mathematics and Statistics
- Coding
- Domain Expertise
- Machine Learning Algorithms
- Data Analysis
- Data Wrangling
- Data Visualization
- Data Structures and Algorithms
- Scientific Computing
- Optimization Techniques
- Experimentation, Evaluation, and Project Deployment Tools
- Predictive Analytics and Segmentation using Clustering
- Exploratory Data Analysis
- Business Acumen & Artificial Intelligence
- Matrix Computations
- Scholastic Model
Top International Universities for Data Science
| University | QS World University Rankings 2022 | Average Fees |
| Massachusetts Institute of Technology | #1 | US$ 55,510 (INR 41,80,902) |
| Stanford University | #3 | US$ 52,857 (INR 39,83,779) |
| Carnegie Mellon University | #53 | US$ 57,560 (INR 43,38,239) |
| National University of Singapore | #11 | SGD 38,000 (INR 21,16,676) |
| University of California, Berkeley | #32 | US$ 29,754 (INR 22,42,529) |
| University of Oxford | #2 | £30,330 (INR 30,66,739) |
| Harvard University | #5 | US$ 50,000 (INR 37,68,450) |
| University of Cambridge | #3 | £35,517 (INR 35,00,000) |
| EPFL Switzerland | #14 | CHF 1,400 (INR 1,14,167) |
| ETH Zurich | #8 | CHF 1,598
(INR 1,30,314) |
Career in Data Science in India
For all the students looking forward to starting a career in data science in India, listed below are some of the popular courses they can pursue-
- Advanced Program in Data Science by IIM Calcutta
- Post Graduate Program in Data Science from Purdue University and Simplilearn
- Post Graduate Program in Data Science by Praxis Business School
- Post Graduate Program In Data Analytics by Imarticus
- Post Graduate Program in Data Science and Business Analytics by Great Learning
- Post Graduate Diploma in Data Science by Upgrad
- Post Graduate Certification in Data Science by Edureka
- Post Graduate Diploma in Data Science by Jigsaw Academy
- Post Graduate Diploma in Data Science
- PG Diploma in Data Science and AI
- PG Diploma in Data Science And Analytics
Top Colleges for Data Science in India
- SRM University Chennai – SRM Institute of Science and Technology
- VIT Vellore – Vellore Institute of Technology
- Jamia Hamdard, New Delhi
- JNU Delhi – Jawaharlal Nehru University
- Dr MGR Educational and Research Institute, Chennai
- HITS Chennai – Hindustan Institute of Technology and Science
- Presidency University, Bangalore
- Annamalai University, Annamalai Nagar
- DEI Agra – Dayalbagh Educational Institute
- Jain University, Bangalore
- Central University of Rajasthan, Ajmer
Career in Data Science after Class 12th
Here is a career map for you if you want to get started in the field of data science after class 12th-
- In order to pursue a career in this field after class 12th, it is mandatory that you have studied Maths, Physics and Computer Science as your core subjects in class 12th.
- You can go on to pursue any UG level course in this field like BSc Data Science, BCA Data Science, BTech Big Data Analytics, etc
- Once you have completed your UG degree, you can either choose to work in this domain or pursue higher education.
- There are a variety of Masters courses available like MSc Statistics- Data Science, Master of Science- Social Data Science, MA Statistics and Data Science, MSc Engineering- Data Science, etc through which you can learn more about this field
Data Science Courses After Graduation
The table below mentions Data Science Courses after graduation along with the fees:
| Offered by | Course Name | Fees excluding GST |
| Purdue University (Simplilearn) | Post Graduate Program in Data Science | INR 2,25,000 |
| IIM Calcutta | Advanced Program in Data Science | INR 4,40,000 |
| Praxis Business School | Post Graduate Program in Data Science | INR 5,10,000 |
| Edureka | Post Graduate Certification in Data Science | INR 2,85,000 |
| Imarticus | Post Graduate Program in Data Analytics | INR 2,10,000 |
Best Book for Data Science
Listed below are some of the best books for data science courses recommended by top data scientists:
- Think Bayes – Bayesian Statistics Made Simple by Allen B Downey.
- Python Data Science Handbook by Jake VanderPlas.
- Think Python by Allen B Downey.
- R for Data Science Garret by Grolemund and Hadley Wickham.
- Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Bayesian Methods for Hackers by Cam Davidson – Pilon.
- Think Stats by Allen B Downey.
- Understanding Machine Learning– From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David.
- Advanced R by Hadley Wickham.
Career Scope in Data Science
Studying DS brings along a plethora of career opportunities in varied sectors. Not just limited to being a Data Scientist, you can go opt for various other job profiles under this vast domain.
Data Analyst
A Data Analyst is responsible for turning the datasets into a usable structure, such as presentations, reports, graphs, etc. They are the ones who gather, refine, perform and analyse the statistical data to support and influence the objectives of a business. Being an entry-level position in the organizational chart of the business, a data analyst should have deep knowledge in Python, R, C, C++, HTML, SQL, Machine Learning, Excel, Probability and Statistics. They work closely with different departments and experts in the business and identify and extract key business risks and performance, in compliance with the data and convert it into a simple and legible format.
Business Analyst
Though a Business Analyst is technically less skilled than its other counterparts in Data Science, yet they have a strong knowledge of all the commercial procedures and has a solid business intelligence. Serving as a nexus between IT and Business Administration, a business analyst is responsible for processing basic data through different data visualization tools and data modelling. They mostly focus on producing the data in the form of graphs, charts, reports, etc., which can be easily read and eventually work towards the interest of the business. If you are planning to work as a business analyst, you will need a strong educational background in Computer Science, Statistics, Mathematics, Business Administration, Economics, Finance or other related fields.
Data Engineer
Skilled in coding languages such as Python, SQL, R, Java, Ruby, MATLAB, Hive, Pig, SAS, etc., Data Engineers design, construct and manage the information or bigger chunks of data. This is one of the most extraordinary careers in DS, as a Data Engineer focuses on the hardware systems that facilitate the data activities of a business. They are responsible for developing an architecture that further helps in processing and analysing the data in such a manner that is best suited for a business organization. After acquiring an advanced degree and significant years of experience in the field of DS, one can secure a senior position under this career profile as well.
Apart from these three main and most demanded career outlooks in DS, here are some other common job profiles you can consider:
- Marketing Analyst
- Data Architect
- Data and Analytics Manager
- Statistician
- Machine Learning Engineer
- Database Administrator
- Data Mining Specialist
Top Recruiters
With a huge amount of production of data on a daily basis, there is a special demand for them in businesses and multinational companies. Here are the top international recruiters of DS professionals.
- Amazon
- Microsoft
- Deloitte
- KPMG
- Verizon
- Apple
- Walmart
- Accenture
- JP Morgan Chase
Data Science Jobs & Average Salary
FAQs
A Data Scientist is supposed to examine which questions need answering and where to find the related data.
Data Science is a technical field, hence it is recommended to learn Data Science through structural learning programs i.e. a bachelor’s or master’s degree, a diploma or a certificate course.
IIT Kharagpur offers courses such as an MTech in Data Science.
If you’ve studied science preferably with maths, then you’re eligible to pursue a Data Science Course. You must have obtained 50% in the same.
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Acquiring a powerful place in contemporary times, pursuing a career in Data Science has created quite a buzz in the world and perhaps, will continue to stay in demand, thanks to the continuous technological advancements. If you possess the skills necessary for building a career in this field, such as problem-solving, persuasive and listening skills, critical thinking and analytical skills, then there are plenty of opportunities waiting for you to grab. Through this blog, let us take you on an educational tour on everything you want to know about a career in this field.
What is Data Science Course?
In simpler words, it is a study of data, which involves a blend of algorithms, principles of machine learning and various other tools that are used to record, store and analyze data to obtain significant and useful information. Data scientists extract and interpret data from an extensive range of sources like log files, social media, sensors, customer transactions, to unlock useful information to influence the decisions of a business and stimulate competitive advantage over other counterparts.
Why Pursue Data Science?
Itis among the most demanded careers. Graduates holding a degree in Data Science have numerous job opportunities as it is reported to be the fastest-growing job on LinkedIn and according to Forbes, demand for data scientists will increase by 28 percent by 2026. The fact that students from business and management backgrounds can also choose this course at Postgraduate levels instead of an MBA degree, makes it a great major to choose for your higher studies. In India, a DS graduate can expect to earn INR 15-19 Lakhs per annum, this is considered a higher amount than any other popular profession such as Engineering. The average salary for a Data Scientist in the United States is $109,666 (INR 82,65,636) per year.
How Does Data Science Work?
Data science includes plenty of disciplines and expertise areas to produce a comprehensive, thorough and a rectified look into raw data. A data scientist must have expertise in the field of data engineering, math, statistics, advanced computing and must be able to communicate only the major bits that’ll help to operate innovation and efficiency. A data scientist is highly reliable on AI- Artificial Intelligence, particularly for its specializations of machine learning and deep learning which is used to create models and make prognosis using algorithms and other techniques. Data science generally has a 5-stage life cycle that requires different techniques, programs and skill sets as depicted in the table below:
| Stage 1- Capture | Data acquisition, data entry, signal reception, data extraction |
| Stage 2- Maintain | Data warehousing, data cleansing, data staging, data processing, data architecture |
| Stage 3- Process | Data mining, clustering/classification, data modeling, data summarization |
| Stage 4- Communicate | Data reporting, data visualization, business intelligence, decision making |
| Stage 5- Analyze | Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis |
Data Science Uses
Data Science has a major contribution to providing convenience in today’s world, from the Healthcare sector to Cybersecurity.
- Anomaly detection to determine fraud, disease, crime and more.
- Automation and decision-making for background checks, credit worthiness etc.
- Classifications in an email server, for example classifying emails as “important” or “junk”
- Predicting sales, revenue and customer retention
- Pattern detection to find out weather patterns, financial market patterns and more.
- Recognition- facial, voice, text etc.
- Recommendations that are based on learned preferences such as recommendation engines can refer you to movies, restaurants and books you may like. For example, you search for a particular product and then you’re recommended with the same product of different brands.
Data Science Use Cases
A number of businesses can potentially benefit from Data Science. Almost every business can be made more systematic through data-driven optimization, and every type of customer experience can be improved with better targeting and personalization, all due to the opportunities created with the help of Data Science. Listed below are a few indicative use cases for AI- Artificial Intelligence-
- A mobile application created by an International Bank that offers immediate decisions to loan applicants by using learning-powered credit risk models and a hybrid cloud computing architecture that provides security as well.
- An ultra-powerful 3D-printed sensor is being developed by an Electronics firm which will guide driverless vehicles in the near future. The solution relies upon data science and analytical tools to increase its real-time object determining capabilities.
- An audience analytics platform has been created by a digital media technology company that allows clients to see the engagement of TV audiences as they’re offered a wide range of digital channels. The solution avails deep analytics and machine learning to accumulate real-time insights into audience behaviour.
- An urban police department created a statistical incident analysis tool which helps officers to understand when and where to utilize the resources to prevent crimes. The solution is data driven that creates reports and dashboards to increase circumstantial awareness for field officers.
Difference Between Data Science, Artificial Intelligence, and Machine Learning
It is equally important to understand other terms related to the field of Data Science, such as AI- artificial intelligence and machine learning. The table below depicts the difference between Data Science, Artificial Intelligence, and Machine Learning in simple terms.
| Data Science | Data Science means the intersection of various areas of statistics, scientific methods, and data analysis that are used to extract meaning and insights from data. |
| AI- Artificial Intelligence | AI is basically a computer or a robot controlled by a computer itself to do tasks that are usually done by humans. |
| Machine Learning | Machine Learning is one of the subsets of AI. Machine Learning involves different techniques that enable computers to figure out things from the data, solve complex problems through deep learning and deliver AI applications. |
The Data Science Lifecycle
The Life Cycle consists of 5 stages i.e. Capturing, Maintaining, Processing, Analyzing, and Communicating.
- Stage 1. This stage involves accumulating raw structured and unstructured data.
- Stage 2. This stage covers taking the raw data and putting it in a form that can be used further.
- Stage 3. Data Scientists take the prepared data and examine its patterns, ranges, and biases to detect how useful it will be in prognostic analysis.
- Stage 4. This stage involves performing various analyses of the data.
Stage 5. The final step is to have analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
Elements of Data Science
Being vast in nature, this field can be divided into three main elements, i.e. Business Intelligence, Machine Learning and Big Data. Let’s have a detailed look on these three components.
Business Intelligence
As every business is using computing technologies to operate, each organisation produces a large amount of data every day. On the careful analysis of this generated data, the data scientists present it in different graphs and charts. Thereafter, it helps the management make the best business decisions based on what the statistics represent.
Machine Learning
Involving mathematical and statistical models and algorithms, Machine Learning is adopted by almost all business organizations to prepare the machines into understanding and reacting to daily circumstances and progresses. Through the use of Machine Learning in the field of Data Science, a machine can predict various trends in the markets or financial systems, based on historical data patterns.
Big Data
With an exponential increase in internet users every day, the viewers or customers generate innumerable clicks in the form of videos, images, articles, comments, orders, etc. Generally, all these activities result in the generation of unstructured data.
Data Scientists are responsible for turning that unstructured data into structured one.
Also Read:
Technical Skills Required in Data Science
Being an interdisciplinary field, DS requires not just one or two, but a diverse set of technical skills and knowledge in the field of Computer Science. Here are the following skills that a Data Scientist must possess, irrespective of the experience gained.
- Python Coding: Python is the most useful coding language in Data Science as it helps in the mining of data, developing models of machine learning or web scraping. Taking various formats of data, python can help you create and find datasets and import SQL tables into your code.
- R Programming: R is a program, generally made for data analysis and provides the formulas and methods for information processing and statistical analysis.
- Machine Learning and AI: Learning different techniques of Machine Learning such as logistic regression, decision tree, supervised machine learning, time series, computer vision, outlier detection, survival analysis, natural language processing, etc is inherently significant to tackle several challenges in the field of Data Science.
- Hadoop Platform: When the volume of data is extremely massive, it can exceed the memory of the system and the Hadoop Platform helps Data Scientists to send or transfer the remaining data to different servers. This platform is also useful for data filtration, data sampling and summarization, exploration, etc.
- SQL: Structured Query Language (SQL) is a programming language which helps you communicate, access and manage the database by adding, subtracting or extracting the data. The Data Scientist needs to be proficient in SQL, as it is specifically designed to save time and reduce the amount of programming for difficult queries, through its compact commands.
Other skills that are essential to the field of Data Science include:
- Data Visualization
- Apache Spark
- Unstructured Data
- Business Acumen
- Communication and Persuasive skills
- Data Wrangling
- Algebra and Calculus
- Statistics
- Java
- Unix
- PHP
Roles and Responsibilities of a Data Scientist
After completing the education and acquiring the technical skills required in DS, here are some of the most common roles and responsibilities that you will be carrying out:
- Staying up-to-date with all the emerging tools and techniques in statistical modelling, machine learning, etc.
- Mine the data and generate a hypothesis to help achieve the high-level business objectives.
- Analyse and mine large data sets using Hadoop Platform.
- Work in collaboration with IT managers, statisticians, programmers and other experts for carrying out business decisions or creating products and services.
- To solve various analytical problems with incomplete datasets, data scientists develop specially tailored algorithms as well.
Eligibility Criteria for Data Science
To pursue a successful career in Data Science, the aspirants need to have graduated with certain specializations or subjects essential to the field. Here are the educational requirements that a student needs to fulfill.
- Higher secondary education (12th grade) with Physics, Chemistry and Maths, i.e. MPC subjects as compulsory.
- A B.Tech or B.Eng degree in Computer Science, Physical Science, Mathematics, Mathematics and Computing, Statistics or Engineering.
- An M.tech, MS or M.Eng degree in DS, Mathematics or any other related fields.
Best Courses in Data Science
- Computer Science – Data Science Concentration – BSc
- Computer Science (Data Science) – BSc (Hons)
- Master of Science [M.Sc] Data Science
- Master of Science [M.Sc] Applied Computation
- MSc Statistics- Data Science
- MPhil Machine Learning
- Master of Science- Social Data Science
- MA Statistics and Data Science
- MSc Engineering- Data Science
- MSc Health Data Analytics and Machine Learning
- MSc Applied Computing- Data Science
- MSc Artificial Intelligence
Diploma in Data Science
Several Diploma and PG Diploma Courses are available for students who want to pursue a Data Science Course without having to pursue full-time data science courses. The table below mentions the diploma course available for students:
| Diploma Course | Offered by |
| PG Program in Data Science | Praxis Business School, Kolkata |
| Postgraduate Diploma in Data Science | Symbiosis Centre for Distance Learning, Pune |
| Post Graduate Diploma in Data Science | Gujarat Technological University |
| Post Graduate Diploma in Data Science | MUIT Noida |
| PG Diploma in Data Science And Analytics | NIELIT Chennai |
Bachelors in Data Science
The table below mentions bachelor course offered in Data Science abroad:
| Bachelor’s Degree | University |
| B.Sc. Astrophysics and Data Science (Hons) |
Keele University, United Kingdom |
| B.Sc. Data Science & Analytics (Hons) |
Bournemouth University, United Kingdom |
| BSc Computer Science- Data Science | University of Ottawa, Canada |
| BSc Data Science | South Dakota State University, United States |
| Bachelor of Computer Science in Data Science (Honours) | University of Waterloo, Canada |
Masters in Data Science
The table below mentions the top universities offering masters in Data Science:
| University | Course |
| Manchester University, United Kingdom | MSc Data Science (Business and Management) |
| London School of Economics, United Kingdom | MSc Data Science |
| Stanford University, USA | MS in Data Science |
| Harvard University, USA | MS in Data Science |
| McGill University, Canada | Master of Data Science |
Data Science Course in IITs
The table below mentions the upcoming IIT data science certifications and data science course offered at the IITs along with the eligibility criteria for pursuing the course:
| Offered by | Course | Eligibility Criteria |
| IIT Delhi | MTech in Machine Intelligence and Data Science | You must be a graduate in science or designing field |
| IIT Indore | MSc in Data Science and Management | Working or fresh graduates, both are eligible to apply |
| IIT Guwahati | Bachelor’s in Data Science and Artificial Intelligence | On the basis of JEE Advanced 2021 counseling |
| IIT Madras | BSc in Programming and Data Science, Diploma in Data Science | Graduates or working professionals, both are eligible to apply |
| IIT Patna | BTech in Artificial Intelligence and Data Science | On the basis of JEE Advanced 2021 |
Data Science Courses Online
In today’s world of digital education, Online courses and certifications have become common for students. If you’re someone who wants to pursue an Online Data Science Course then you’re at the right place. Here we’ve mentioned the best Data Science Courses that are offered online.
Udemy
The table below mentions the online Data Science Courses offered by udemy along with the course fees:
| Course Offered | Course Fee |
| Data Science and Machine Learning Bootcamp with R | INR 3,499 |
| Python for Data Science and Machine Learning Bootcamp | INR 3,499b |
| Complete Machine Learning & Data Science Bootcamp 2022 | INR 3,499 |
| Python A-Z: Python For Data Science With Real Exercises | INR 3,499 |
| Data Science A-Z: Real-Life Data Science | INR 3,499 |
IBM
Listed below are the online Data Science Courses offered by IBM:
- IBM Data Science Professional Certificate
- Advanced-Data Science with IBM Specialization
- Applied Data Science Specialisations
- Data Science Fundamentals with Python and SQL Specialisations
- Introduction to Data Science Specialisation
Coursera
The table below mentions the Online Data Science Courses offered by Coursera along with the course duration:
| Course | Duration |
| IBM Data Science Professional Certificate | 11 months |
| Google Data Analytics Professional Certificate | 6 months |
| Applied Data Science with Python Specialization | 5 months |
| Data Science: Foundations using R Specialization | 5 months |
| Data Science: Statistics and Machine Learning Specialization | 6 months |
UpGrad
The table below mentions the online Data Science Courses offered by UpGrad along with the course fee:
| Course Offered | Course Fee |
| Executive PG Programme in Data Science | INR 2,99,000 |
| Master of Science in Data Science | INR 4,00,000 |
| Advanced Certificate Programme in Data Science | INR 99,000 |
| Professional Certificate Programme in Data Science for Business Decision Making | INR 1,50,000 |
edX
The table below mentions the edX Data Science Courses:
| Offered by | Course |
| Harvard University | Professional Certificate in Data Science |
| Harvard University | Data Science Machine Learning |
| Harvard University | Data Science: R Basics |
| IBM | Python Basics for Data Science |
| University of California, Berkeley | Professional Certificate in Foundations of Data Science |
| Massachusetts Institute of Technology | MicroMasters Program in Statistics and Data Science |
Simplilearn
The Online Data Science Courses offered by Simplilearn are as listed below:
- Data Science with R Certification Courses
- Artificial Intelligence and Data Science
- Introduction to Data Science
- Data Science with Python Certification Course
- Data Scientist
Google Data Science Certification
The details for the Google Data Science Certification are as mentioned:
| Offered by | |
| Prerequisites | There are no specific requirements to apply for Google data science certification, however, a professional with 3 or more years of experience in the industry with 1 or more years experience in designing and managing solutions using Google cloud is considered beneficial. |
| Validity | 2 years |
| Google Professional Data Engineer Exam Fee | INR 15,010 |
| Available Languages for Exam | English and Japanese |
| Exam Duration | 2 Hours |
| Exam Pattern | MCQs |
| Exam Mode | Remote Or At the Exam Centre |
Data Science Course Syllabus
Data Science Courses are offered at various levels such as undergraduate, postgraduate, Diplomas and certifications, and in various specializations so the syllabus for Data Science may vary. However, here we’ve mentioned general subjects that are taught in every Data Science Course:
- Introduction to Data Science
- Statistical Foundations for Data Science
- Mathematics and Statistics
- Coding
- Domain Expertise
- Machine Learning Algorithms
- Data Analysis
- Data Wrangling
- Data Visualization
- Data Structures and Algorithms
- Scientific Computing
- Optimization Techniques
- Experimentation, Evaluation, and Project Deployment Tools
- Predictive Analytics and Segmentation using Clustering
- Exploratory Data Analysis
- Business Acumen & Artificial Intelligence
- Matrix Computations
- Scholastic Model
Top International Universities for Data Science
| University | QS World University Rankings 2022 | Average Fees |
| Massachusetts Institute of Technology | #1 | US$ 55,510 (INR 41,80,902) |
| Stanford University | #3 | US$ 52,857 (INR 39,83,779) |
| Carnegie Mellon University | #53 | US$ 57,560 (INR 43,38,239) |
| National University of Singapore | #11 | SGD 38,000 (INR 21,16,676) |
| University of California, Berkeley | #32 | US$ 29,754 (INR 22,42,529) |
| University of Oxford | #2 | £30,330 (INR 30,66,739) |
| Harvard University | #5 | US$ 50,000 (INR 37,68,450) |
| University of Cambridge | #3 | £35,517 (INR 35,00,000) |
| EPFL Switzerland | #14 | CHF 1,400 (INR 1,14,167) |
| ETH Zurich | #8 | CHF 1,598
(INR 1,30,314) |
Career in Data Science in India
For all the students looking forward to starting a career in data science in India, listed below are some of the popular courses they can pursue-
- Advanced Program in Data Science by IIM Calcutta
- Post Graduate Program in Data Science from Purdue University and Simplilearn
- Post Graduate Program in Data Science by Praxis Business School
- Post Graduate Program In Data Analytics by Imarticus
- Post Graduate Program in Data Science and Business Analytics by Great Learning
- Post Graduate Diploma in Data Science by Upgrad
- Post Graduate Certification in Data Science by Edureka
- Post Graduate Diploma in Data Science by Jigsaw Academy
- Post Graduate Diploma in Data Science
- PG Diploma in Data Science and AI
- PG Diploma in Data Science And Analytics
Top Colleges for Data Science in India
- SRM University Chennai – SRM Institute of Science and Technology
- VIT Vellore – Vellore Institute of Technology
- Jamia Hamdard, New Delhi
- JNU Delhi – Jawaharlal Nehru University
- Dr MGR Educational and Research Institute, Chennai
- HITS Chennai – Hindustan Institute of Technology and Science
- Presidency University, Bangalore
- Annamalai University, Annamalai Nagar
- DEI Agra – Dayalbagh Educational Institute
- Jain University, Bangalore
- Central University of Rajasthan, Ajmer
Career in Data Science after Class 12th
Here is a career map for you if you want to get started in the field of data science after class 12th-
- In order to pursue a career in this field after class 12th, it is mandatory that you have studied Maths, Physics and Computer Science as your core subjects in class 12th.
- You can go on to pursue any UG level course in this field like BSc Data Science, BCA Data Science, BTech Big Data Analytics, etc
- Once you have completed your UG degree, you can either choose to work in this domain or pursue higher education.
- There are a variety of Masters courses available like MSc Statistics- Data Science, Master of Science- Social Data Science, MA Statistics and Data Science, MSc Engineering- Data Science, etc through which you can learn more about this field
Data Science Courses After Graduation
The table below mentions Data Science Courses after graduation along with the fees:
| Offered by | Course Name | Fees excluding GST |
| Purdue University (Simplilearn) | Post Graduate Program in Data Science | INR 2,25,000 |
| IIM Calcutta | Advanced Program in Data Science | INR 4,40,000 |
| Praxis Business School | Post Graduate Program in Data Science | INR 5,10,000 |
| Edureka | Post Graduate Certification in Data Science | INR 2,85,000 |
| Imarticus | Post Graduate Program in Data Analytics | INR 2,10,000 |
Best Book for Data Science
Listed below are some of the best books for data science courses recommended by top data scientists:
- Think Bayes – Bayesian Statistics Made Simple by Allen B Downey.
- Python Data Science Handbook by Jake VanderPlas.
- Think Python by Allen B Downey.
- R for Data Science Garret by Grolemund and Hadley Wickham.
- Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Bayesian Methods for Hackers by Cam Davidson – Pilon.
- Think Stats by Allen B Downey.
- Understanding Machine Learning– From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David.
- Advanced R by Hadley Wickham.
Career Scope in Data Science
Studying DS brings along a plethora of career opportunities in varied sectors. Not just limited to being a Data Scientist, you can go opt for various other job profiles under this vast domain.
Data Analyst
A Data Analyst is responsible for turning the datasets into a usable structure, such as presentations, reports, graphs, etc. They are the ones who gather, refine, perform and analyse the statistical data to support and influence the objectives of a business. Being an entry-level position in the organizational chart of the business, a data analyst should have deep knowledge in Python, R, C, C++, HTML, SQL, Machine Learning, Excel, Probability and Statistics. They work closely with different departments and experts in the business and identify and extract key business risks and performance, in compliance with the data and convert it into a simple and legible format.
Business Analyst
Though a Business Analyst is technically less skilled than its other counterparts in Data Science, yet they have a strong knowledge of all the commercial procedures and has a solid business intelligence. Serving as a nexus between IT and Business Administration, a business analyst is responsible for processing basic data through different data visualization tools and data modelling. They mostly focus on producing the data in the form of graphs, charts, reports, etc., which can be easily read and eventually work towards the interest of the business. If you are planning to work as a business analyst, you will need a strong educational background in Computer Science, Statistics, Mathematics, Business Administration, Economics, Finance or other related fields.
Data Engineer
Skilled in coding languages such as Python, SQL, R, Java, Ruby, MATLAB, Hive, Pig, SAS, etc., Data Engineers design, construct and manage the information or bigger chunks of data. This is one of the most extraordinary careers in DS, as a Data Engineer focuses on the hardware systems that facilitate the data activities of a business. They are responsible for developing an architecture that further helps in processing and analysing the data in such a manner that is best suited for a business organization. After acquiring an advanced degree and significant years of experience in the field of DS, one can secure a senior position under this career profile as well.
Apart from these three main and most demanded career outlooks in DS, here are some other common job profiles you can consider:
- Marketing Analyst
- Data Architect
- Data and Analytics Manager
- Statistician
- Machine Learning Engineer
- Database Administrator
- Data Mining Specialist
Top Recruiters
With a huge amount of production of data on a daily basis, there is a special demand for them in businesses and multinational companies. Here are the top international recruiters of DS professionals.
- Amazon
- Microsoft
- Deloitte
- KPMG
- Verizon
- Apple
- Walmart
- Accenture
- JP Morgan Chase
Data Science Jobs & Average Salary
FAQs
A Data Scientist is supposed to examine which questions need answering and where to find the related data.
Data Science is a technical field, hence it is recommended to learn Data Science through structural learning programs i.e. a bachelor’s or master’s degree, a diploma or a certificate course.
IIT Kharagpur offers courses such as an MTech in Data Science.
If you’ve studied science preferably with maths, then you’re eligible to pursue a Data Science Course. You must have obtained 50% in the same.
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I read your blog and I like very much the roles and responsibilities of the data scientist part. There is no doubt that data science is the top trending career option and especially for those who have python skills. Thanks for sharing best career guidance.
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Thank you. Keep following our blogs!
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I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science
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6 comments
I read your blog and I like very much the roles and responsibilities of the data scientist part. There is no doubt that data science is the top trending career option and especially for those who have python skills. Thanks for sharing best career guidance.
Thank you. Keep following our blogs!
I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science
Glad to know that our article was helpful for you. If you are interested in knowing more about our study abroad related services call Leverage Edu anytime at 1800 572 000 for a free consultancy session.
thank you for sharing the information about a career in data science. people get clarity about why to choose data science as a career after reading this. keep going.
Glad to know that our article was helpful for you. If you are interested in knowing more about our study abroad related services call Leverage Edu anytime at 1800 572 000 for a free consultancy session.