Data Science Online Courses & Certifications

Data Science is one of the top 10 most sought-after jobs in India for 2024, with an average yearly salary of INR 10,50,000. Every month, thousands of students and professionals from various backgrounds, including both tech and non-tech, worldwide are enrolling in data science courses to advance their careers.

 

Data Science involves using tools and techniques to extract insights and knowledge from data. This includes collecting, cleaning, analyzing, and visualizing data to identify patterns and make predictions.

 

In simple terms, Data Science is about finding answers to questions by examining a lot of information. For example, a data scientist might use data to figure out what people are most likely to buy at a store or how to make a website more popular. Think of a data science professional as a detective, analyzing data to solve problems rather than crimes.

Read more on: What is Data Science? A Complete Career Guide for Beginners in 2024

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1. 10 Reasons Why to Learn Data Science Online is Better

2. 10 Things That You Must Check Before Purchsing Online Data Science Course

 

Why Learn Data Science?

There are many reasons to learn data science, including

1. High demand for data science skills: Data science is one of the fastest-growing occupations in the tech industry. Plus the demand for skilled data scientists is continuously growing.

2. Opportunities for career advancement: Data science skills open up a wide range of career opportunities, with different roles in data analysis, machine learning, business intelligence, and many more.

3. High earning potential: Data scientists often command high salaries, making it a high-paid lucrative career choice. 

4. Ability to solve real-world problems: Data science can be used to solve a wide range of real-world problems in different sectors, from predicting the outcomes to optimizing production processes and improving healthcare outcomes.

5. Personal and professional growth: Learning data science can help you develop new skills and broaden your horizons, leading to personal and professional growth.

Learning data science can provide you a range of benefits, including high demand for your skills, opportunities for career advancement, and the ability to solve real-world problems. So whether you are looking to start a new career or advance in your current field, data science can be a valuable skill to have.

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How to Start your Career in Data Science?

There are a few steps you can follow to start a career in data science:

1. Build a strong foundation in math and statistics: Data science involves working with large datasets and using statistical techniques to extract insights from them. As such, a strong foundation in math and statistics is essential. Consider taking courses or self-studying topics such as calculus, linear algebra, and statistical modeling.

2. Learn a programming language: Data science involves working with data and algorithms, so it is important to have some programming skills. Consider learning a language such as Python or R, as these are commonly used in data science.

3. Gain experience with data analysis tools and techniques: There are many tools and techniques used in data science, such as machine learning algorithms, data visualization tools, and databases. Consider taking courses or completing online tutorials to learn how to use these tools and techniques.

4. Build a portfolio of projects: A strong portfolio of projects is a key factor in getting hired as a data scientist. Consider completing data science projects on your own or through a data science bootcamp or program. These projects should showcase your skills and demonstrate your ability to apply data science techniques to solve real-world problems.

5. Stay up-to-date with developments in the field: Data science is a rapidly evolving field, so it is important to stay up-to-date with the latest developments and trends. Consider reading industry blogs, attending conferences or joining online communities or forums to stay current.

6. Network and build relationships: Networking and building relationships in the data science community can be valuable for finding job opportunities and getting advice from experienced professionals. Consider attending data science meetups or conferences, or joining online communities or forums.

Starting a career in data science requires a combination of strong foundational skills, experience with data analysis tools and techniques, and a portfolio of projects to showcase your skills. By building these skills and gaining experience, you can position yourself as a competitive candidate for data science roles.

Read more:

1. Career in Data Science as a Fresher
2. Data Science Courses to Unlock Financial Insights for Analysts

 

Top Online Data Science Course Providers 

Platform	Description	Course Levels
Coursera	Self-paced videos, courses start from scratch and cover intermediate and advanced levels	Beginner, Intermediate, Advanced
edX	Collaboration with universities and organizations, professional certificates and master's degrees available	Fundamentals, Intermediate, Advanced
UpGrad	Collaboration with top universities, ten courses available with a focus on career mentorship and industry projects	Masters, Executive Post-Graduate, Certificate
IIT Madras	Undergraduate degree/diploma program taught by experienced faculty and industry experts	Undergraduate
Udacity	Offers nanodegree programs in Data Science and AI in collaboration with industry leaders	Intermediate, Advanced
Udemy	Offers a wide range of data science courses with a focus on practical application	Beginner, Intermediate, Advanced
Simplilearn	Offers a variety of data science and big data courses with a focus on certification and career advancement	Beginner, Intermediate, Advanced
Great Learning	Offers a variety of data science and big data courses, including a PG program with a focus on industry-relevant skills	Beginner, Intermediate, Advanced
Edureka	Offers a variety of data science and big data courses with a focus on live, instructor-led sessions	Beginner, Intermediate, Advanced
DataCamp	Offers interactive coding challenges and video tutorials with a focus on hands-on learning	Beginner, Intermediate, Advanced

Popular Data Science Online Courses 

1. IBM Data Science Professional

There are ten courses in this professional certificate. The course starts with an overview of what data science is like today. After that, it will cover some popular data science tools -how to use them and what are their features. It will briefly cover Python and SQL, followed by the classes of Machine Learning with the Capstone Projects.

Recommended For: Beginner

Instructor: Rav Ahuja, Alex Aklson + 9 others

Mode of Teaching: Online

Prerequisite: High School Math, familiarity working with Computers, Communication and Presentation Skills

Fees:

1. EMI available
2. Financial Aid available

Enroll Now: IBM Data Science Professional

2. Executive Post Graduate Program in Data Science-upGrad

Course is offered in collaboration with IIIT Bangalore. It provides five specializations (Deep Learning, NLP, Business Analytics, Business Intelligence/Data Analytics, and Data Engineering). It includes Programming in Python, EDA, Statistics, and Advanced SQL. 

Recommended: Freshers, Engineers, Software Professional

Mode of Teaching: Online

Eligibility: Bachelor Degree with minimum 50% or equivalent

Course Duration: 12 Months

Fees: 2,99,000/- 

Enroll Now: Executive Post Graduate Program in Data Science-upGrad

3. Diploma in Data Science-IIT Madras

There are eight courses, including hands-on training through labs and projects. The diploma includes mathematics, statistics, machine learning and business analytics courses. You can take the admission by passing the Diploma Qualifier Exam. 

Recommended: Student/ Working Professional/Job-Seekers

Instructor: IIT Madras Faculty

Mode of Teaching: Online

Eligibility: Anyone with at least two years of any UG

Course Duration: 8 Months

Fees: INR 63,000/- 

Enroll Now: Diploma in Data Science-IIT Madras

Best Data Science Certifications in 2024

Certification	Provider	Online Provider	Audience
Enroll Now	John Hopkins University	Coursera	Professionals wanting to learn machine learning techniques.
Enroll Now	HarvardX	edX	Professionals looking to gain a deeper understanding of the theoretical foundations of machine learning.
Enroll Now	IBM	Coursera	Professionals wanting to learn IBM's data science and machine learning tools.
Enroll Now	IBM	edX	Professionals wanting to learn Microsoft's data science and machine learning tools.
Enroll Now	Columbia Universirty	Udemy	Professionals wanting to learn data science and machine learning techniques.
Enroll Now	Google Cloud	Coursera	Professionals wanting to learn data science and machine learning techniques on Google Cloud Platform.

Tips on How to choose the Best Online Data Science Course?

There are several factors to consider when choosing an online data science course:

Criteria	Ask Questions
Quality of the course material	What is the level of coverage and the relevance of the course material? Are there any hands-on learning opportunities?
Instructor expertise	What are the qualifications and experience of the instructors?
Course format and length	Does the format of the course suit your learning style? Is the course length manageable?
Support and resources	Are there any resources and support systems provided by the course?
Cost	Is the cost of the course within your budget? Are there any financial assistance or scholarships available?
Reputation	What are the reviews and recommendations for the course?
Programming Language	What programming language is the course based on? Python and R are the most commonly used languages for data science.
Project-based learning	Does the course offer project-based learning to apply the concepts learned in real-world scenarios?
Industry Recognition	Is the course recognized or endorsed by any industry leaders or organizations?
Flexibility	Are there any flexible options such as self-paced learning or flexible deadlines?
Job assistance	Does the course provide job assistance or career support?

Ultimately, the best online data science course for you will depend on your goals and learning style, as well as the resources and time you have available. By considering these factors and doing your research, you can find the right course that helps you achieve your objectives.

What are the Prerequisites for learning Data Science Online?

There are a few key prerequisites to learning data science:

1. A strong foundation in math and statistics: Data science involves working with large datasets and using statistical techniques to extract insights from them. As such, a strong foundation in math and statistics is essential. This may include courses in topics such as calculus, linear algebra, and statistical modeling.

2. Programming skills: Data science involves working with data and algorithms, so it is important to have some programming skills. This may include languages such as Python or R, which are commonly used in data science.

3. Familiarity with data analysis tools and techniques: Data science involves using a variety of tools and techniques to analyze data and extract insights. Familiarity with tools such as machine learning algorithms, data visualization tools, and databases can be helpful when learning data science.

There are no strict prerequisites to learning data science. Still having a strong foundation in math and statistics, programming skills, and familiarity with data analysis tools and techniques can be helpful.

What will you learn from a Data Science course?

Specifics of the topics covered in a data science course will depend on the specific goals and focus of the course. However, most of the data science courses will cover a range of topics related to collecting, manipulating, analyzing, and interpreting data to extract insights and inform business decisions.

Exhaustive Data Science Course Syllabus in 2024:

A Data Science syllabus generally covers a wide range of topics including:

1. 1. Programming languages such as Python, R, and SQL.
   2. Statistical methods and probability theory for data analysis.
   3. Data visualization and data exploring techniques.
   4. Data Wrangling and Cleaning Techniques
   5. Structured and Non Structured Database - SQL and NoSQL Database
   6. Machine learning algorithms such as linear regression, decision trees, k-means clustering, etc.
   7. Deep learning techniques such as neural networks, convolutional neural networks, recurrent neural networks, etc.
   8. Natural Language Processing (NLP) and Text mining.
   9. Database management and data warehousing.
   10. Big Data platforms such as Hadoop, Spark, and Kafka.
   11. Cloud Computing Platform such as AWS, Azure, GCP

Additionally, a Data Science syllabus may also include:

• • Project work and case studies to provide hands-on experience with real-world data science problems.
  • Guest lectures and workshops by industry experts to give students an understanding of the latest trends and developments in the field.
  • Opportunities for internships and co-op programs to give students the opportunity to apply their knowledge and skills in a professional setting.

Here is the exhaustive syllabus for a Data Science Course.

1. Introduction to Data Science:

Importance of data in decision making	Types of data and their characteristics
Definition and history of data science	Applications of data science
Key concepts and terminology	Data science process and workflows
Data science teams and roles	Tools and technologies used in Data Science
Overview of data visualization and its importance	Overview of data science project lifecycle

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2. Programming Fundamentals:

Introduction to programming concepts and paradigms	Basic data types in Python
Operators in Python	Loops in Python
Control flow statements	Functions and modules
Basic input/output operations	Basic error handling
Writing Python scripts	Running Python scripts from command-line
Working with command-line arguments	Reading and writing files
Basic regular expressions	Basic testing and documentation
List in Python	Tuples in Python
Dictionary in Python	Sets in Python
String in Python	Numpy Array
Pandas Dataframe	Matplotlib
Seaborn	Scikit-learn
Working with CSV and Excel files	Web scraping with Python

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3. Data Exploration and Visualization:

Overview of data exploration	Importing and exporting data
Basic data manipulation and cleaning	Basic data statistics and descriptive statistics
Outlier detection and handling	Missing data imputation
Data normalization and scaling	Data transformation
Overview of data visualization and its importance	Basic plotting techniques (line, bar, scatter, etc.)
Advanced plotting techniques (heatmap, boxplot, etc.)	Interactive visualization
Visualizing distributions and relations	Visualizing time series data
Visualizing categorical data	Visualizing geographic data
Matplotlib	Seaborn
Plotly	ggplot
Bokeh	D3.js
Overview of data storytelling	Identifying the story in the data
Communicating the data story effectively	Best practices for data storytelling

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4. Data Wrangling:

Overview of data acquisition	Data sources and types
Web scraping and APIs	Data formats and protocols
Data quality and completeness	Data versioning and provenance
Overview of data cleansing	Data validation and integrity checks
Data formatting and standardization	Data deduplication and removal of duplicates
Data normalization and scaling	Handling missing and null values
Handling outliers	Overview of data transformation
Data reshaping and pivoting	Data merging and joining
Data aggregation and summarization	Data normalization and scaling
Data reduction and dimensionality reduction techniques	Identifying patterns and trends
Data profiling	Identifying anomalies in the data

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5. Database management systems (DBMS)

Overview of database management systems	Types of databases
Database models and architecture	Data modeling and database design
Normalization and database design principles	Database management and administration
Introduction to relational databases	SQL basics and data manipulation
SQL query optimization	Advanced SQL queries and functions
Database transactions and concurrency	Data integrity and constraints
Introduction to NoSQL databases	Types of NoSQL databases
Data modeling and design for NoSQL databases	NoSQL data management and administration
MongoDB	Casandra

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6. Probability and Statistics:

Basic probability concepts and notation	Random variables and probability distributions
Discrete and continuous probability distributions	Joint and conditional probability
Bayes' theorem	Random sampling and sampling distributions
Central limit theorem	Measures of central tendency: mean, median, mode
Measures of dispersion: range, variance, standard deviation	Measures of skewness and kurtosis
Basic probability plots and histograms	Basic box plots and scatter plots
Inferential Statistics	Point estimation
Interval estimation	Hypothesis testing
p-values and Type I and Type II errors	t-tests and ANOVA
Chi-squared tests	Non-parametric tests
Regression Analysis	Simple linear regression
Multiple linear regression	Model evaluation and selection
Polynomial regression	Logistic regression
Regularization techniques	Time Series Analysis
Time series decomposition	Time series forecasting
ARMA	ARIMA
Exponential smoothing	FBProphet
Time series anomaly detection	Time series clustering

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7. Machine Learning:

Overview of Machine Learning	Types of Machine Learning
Feature engineering	Feature Selection
Model evaluation	Model Selection
Linear Regression	Logistic Regression
Decision Tree	Random Forest
K- Nearest Neighbor (K-NN)	Support Vector Machine (SVM)
Gradient Boosting	Perceptrons
K Means Clustering	Hierarchical Clustring
Density Based Clustring	Dimentionality Reduction
Association rule mining	Overfitting and Underfitting
Reinforcement Learning	Markov Decision Process (MDP)
Q-learning	SARSA
Deep Q-network (DQN)	Policy gradient methods

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8. Deep Learning

Neural networks and their architecture	Activation functions
Gradient descent and backpropagation	Overfitting and regularization
Convolutional Neural Networks (CNNs)	CNN architecture and layers
Convolution, pooling and padding	Transfer learning and fine-tuning
Object detection	Image Segmentation
Recurrent Neural Networks (RNNs):	RNN architecture and layers
LSTM and GRU	Sequence-to-sequence models
Text generation and language modeling	Sentiment analysis
Generative Models	Autoencoder and Variational Autoencoder (VAE)
Generative Adversarial Networks (GANs)	Deep Convolutional GAN (DCGAN)
Style transfer	Superresolution
Transfer learning concepts	Pre-trained models and feature extraction
Fine-tuning pre-trained models	Tensorflow and Keras
Pytorch	Caffe

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9. Natural Language Processing:

Text pre-processing	Part-of-Speech (POS) tagging
Named Entity Recognition (NER)	Syntax analysis
Semantic analysis	Text classification
Information extraction	Machine translation
Speech processing	Language modeling
Sentiment analysis	Text generation
Dialogue systems	Text-to-speech and speech-to-text
Text summarization	BERT
GPT 2, GPT 3	Transformer
Transformer-XL	Aspect-Based Sentiment Analysis
Contextual Chatbots	Dialogue Generation

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10. Business Intelligence and Reporting:

Business intelligence process and architecture	Business intelligence use cases and scenarios
Business intelligence tools and technologies	Business intelligence strategy and planning
Overview of data warehousing and data marts	Data warehousing architecture and design
Data warehousing best practices and performance tuning	Data warehousing in the cloud
Data Visualization and Dashboarding	Interactive data visualization and dashboarding
Data visualization best practices and design principles	Data visualization for decision making and business analysis

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11. Big Data in Data Science:

Introduction to Big Data	Big Data Architecture and Design
Data warehousing and data lake architecture	Distributed systems and distributed computing
Data partitioning and data distribution	Data replication and data consistency
HDFS and Hadoop Distributed File System	MapReduce and YARN
Stream processing	Apache Kafka
Apache Storm	SQL on Hadoop
Hive	Impala
SparkSQL	Apache Flink

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12. Cloud Computing in Data Science:

Introduction to Cloud Computing	Cloud computing services and providers
Cloud computing models (IaaS, PaaS, SaaS)	Cloud computing use cases and scenarios
Cloud Computing Architecture and Design	Cloud computing security and compliance
Cloud computing deployment models (public, private, hybrid)	Cloud computing management and orchestration
Cloud storage services	AWS S3
Azure Blob storage	Google Cloud Storage
RDS	Cosmos
Bigtable	Cloud data warehousing
Redshift	BigQuery
Cloud data lakes and data management	AWS Glue
Azure Data Factory	Google Cloud Dataflow
Cloud computing services for compute	AWS EC2
Azure Virtual Machines	GCE
Cloud computing services for networking	AWS VPC
Azure Virtual Network	GCP VPC
Cloud computing services for serverless	AWS Lambda
Azure Functions	GCP Cloud Function
Cloud computing services for machine learning	AWS Sagemaker
Azure Machine Learning	Cloud ML Engine
Cloud computing services for analytics	EMR
HDInsight	Dataproc
Cloud Security and Compliance	Cloud Data encryption

 

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Top Colleges Providing Data Science Course in India

Here's the list of top college in India providing Data Science Course.

College Name	Location	Degree Offered	Duration	Exam Score Accepted	Data Science Course Offered
Indian Institutes of Technology (IITs)	Across India	B.Tech, M.Tech, M.Sc, PhD	4 years, 2 years, 2 years, 3-5 years	JEE Advanced, GATE, JAM, CAT, GRE	M.Tech in Data Science, M.Tech in Artificial Intelligence, M.Tech in Machine Learning, M.Sc in Data Science
Indian Institute of Science (IISc)	Bengaluru	M.Tech, M.Sc, PhD	2 years, 2 years, 3-5 years	GATE, JAM, NET, GRE	M.Tech in Data Science, M.Sc in Data Science
Indian Institute of Management (IIMs)	Across India	MBA, PGDM	2 years, 2 years	CAT, GMAT, XAT	MBA in Business Analytics, PGDM in Business Analytics
Indian Statistical Institute (ISI)	Kolkata, Delhi, Bangalore, Chennai	B.Stat, M.Stat, M.Sc, PhD	3 years, 2 years, 2 years, 3-5 years	ISI Entrance Test, GATE, JAM, NET, GRE	M.Sc in Data Science, M.Tech in Data Science
Birla Institute of Technology and Science (BITS)	Pilani, Goa, Hyderabad	B.E, M.E, M.Sc, MBA	4 years, 2 years, 2 years, 2 years	BITSAT, GATE, CAT, GMAT, XAT	M.E in Data Science, M.Sc in Data Science
International Institute of Information Technology (IIITs)	Hyderabad, Bangalore, Bhubaneswar, Allahabad	B.Tech, M.Tech, M.Sc, PhD	4 years, 2 years, 2 years, 3-5 years	JEE, GATE, CAT, GRE, TOEFL	M.Tech in Artificial Intelligence, M.Tech in Data Science, M.Sc in Data Science
Delhi Technical University (DTU)	Delhi	B.Tech, M.Tech, M.Sc, PhD	4 years, 2 years, 2 years, 3-5 years	JEE, GATE, CAT, GRE, TOEFL	M.Tech in Data Science, M.Sc in Data Science
National Institute of Technology (NITs)	Across India	B.Tech, M.Tech, M.Sc, PhD	4 years, 2 years, 2 years, 3-5 years	JEE, GATE, CAT, GRE, TOEFL	M.Tech in Data Science, M.Sc in Data Science
Vellore Institute of Technology (VIT)	Vellore	B.Tech, M.Tech, M.Sc, PhD	4 years, 2 years, 2 years, 3-5 years	VITEEE, GATE, CAT, GRE, TOEFL	M.Tech in Data Science, M.Sc in Data Science

 

What are Top Data Science Entrance Exams?

Exam Name	Eligibility Criteria	Exam Frequency	Accepted by colleges
GATE	Bachelor's degree in relevant field	Once a year	IITs, NITs, IIITs, GFTIs, and other institutes
CAT	Bachelor's degree	Once a year	IIMs, and other top B-schools
GRE	Bachelor's degree	Multiple times a year	Various colleges and universities in India and abroad
JEE	12th class	Once a year	IITs, NITs, IIITs, and other institutes
TOEFL	Bachelor's degree	Multiple times a year	Various colleges and universities in India and abroad
IIT-JAM	Bachelor's degree	Once a year	IITs and IISc
JEST	Master's degree or equivalent in relevant field	Once a year	Various institutes offering Ph.D and Integrated Ph.D programs in Physics and Theoretical Computer Science
NIMCET	Bachelor's degree	Once a year	NITs
TANCET	Bachelor's degree	Once a year	Anna University, and other colleges in Tamil Nadu
NMAT	Bachelor's degree	Multiple times a year	NMIMS, and other management institutes

Atul Harsha03-25-2022 00:00