So here I’m sharing with you a complete data science path that you can follow to become a great data scientist. Below is the collection of all the courses that you need to learn Data Science from various platforms. Undoubtedly all of them are absolutely free (Without certification) and from the top Universities of the World. If your intention is to complete your Data Science Degree without getting a certificate and for free then this is just for you. This Course is provided by OSSU.

The best part about this that I’m about to tell you is:

**Self Learing:**First of all you can do it on your own time. There are no bounds that you will have to attend this class in this time or something like that.**Free:**Number two it is going to be completely free. That means you are not spending anything.**Learn from the Best:**And thirdly You are learning from the best professors from the best universities around the world. So these are the three important points to keep in mind.

**The Course:**

So the Data Science Degree Course has 10 stages that you have to do in a certain order which is mentioned at the end. These stages can be covered through one or more than one course. There is a total of 29 courses that you have to complete. All these courses are individual as well and can be purchased if you need their Certificates. Otherwise, you can simply audit the course and start learning it for free.

**Introduction to Data Science**

#### Course 1 What is Data Science

**About:**

**Modules:** **1️⃣ **Defining Data Science and What Data Scientists Do [8 videos (Total 27 min), 4 readings, 2 quizzes]**2️⃣** Data Science Topics [8 videos (Total 39 min), 2 readings, 2 quizzes]**3️⃣** Data Science in Business [7 videos (Total 32 min), 4 readings, 4 quizzes]

**Duration:** Approx. 8 hours to complete.

**Offered By:** **IBM** (visit: www.ibm.com) at Coursera.

### Introduction to Computer Science

*Students who already know the basics of programming (any language) can skip this first course*.

#### Course 2 Python for Everybody

**About:** This course is especially for beginners to get started with Python. Here you will learn how to install and write your first program with the basics of the python programming language. Along with that, you will be taught to use variables to store, retrieve and calculate information. Also utilizing core programming tools like functions and loops is in this course.

**Modules:** This Course actually has a set of 5 Courses which are as follows:**1️⃣ **Programming For Everybody (Getting Started with Python)**2️⃣** Python Data Structures**3️⃣** Using Python to Access Web Data**4️⃣** Using Databases with Python**5️⃣** Capstone: Retrieving, Processing, and Visualizing Data with Python

**Duration:** Approximately at the suggested pace of 3 hours per week it will take 8 months to complete.

**Offered By:** University of Michigan (Visit) at Coursera.

#### Course 3 Introduction to Computer Science and Programming Using Python

**About:** This course is basically the first one of a two-course sequence: *Introduction to Computer Science and Programming Using Python*, and *Introduction to Computational Thinking and Data Science.* These two are designed for absolute beginners to learn to program, think computationally, and write real-world problems tackling programs. Here you will learn Python 3.5 with feature lectures, and exercises. even if you have previously learned Python 2.7 you will be able to easily and comfortably transit to Python 3.5.

**Modules:** Following are the topics covered in this course:**1️⃣**A Notion of computation**2️⃣**The Python programming language**3️⃣**Some simple algorithms**4️⃣**Testing and debugging**5️⃣**An informal introduction to algorithmic complexity**6️⃣**Data structures

**Duration:** If given 14 to 16 hours per week this course will take approximately 9 weeks to get completed.

**Offered By:** Massachusetts Institute of Technology (MIT) at edX.

#### Course 4 Introduction to Computational Thinking and Data Science

**About:** From this course, you will learn how to use computation to accomplish a variety of goals and problem-solving. Some prior programming experience in Python and a fundamental understanding of computational complexity are required to start this course. You’ll learn plotting with pylab package, stochastic programming and statistical thinking, and Monte Carlo simulations.

**Module:** Following concepts will be taught in this Course:**1️⃣**Advanced programming in Python 3**2️⃣**Knapsack problem, Graphs and graph optimization**3️⃣**Dynamic programming**4️⃣**Plotting with the pylab package**5️⃣**Random walks**6️⃣**Probability, Distributions

**Duration:** Approximately 9 weeks, considering 14-16 hours per week.

**Offered By:** Massachusetts Institute of Technology (MIT) at edX.

**Data Structures and Algorithms**

*The Algorithms courses are taught in Java. If students need to learn Java, they should take this course first*

#### Course 5 Java Programming

**About:** Along with algorithms and OOP through Java Programming language you will learn Introduction to programming and advanced programming courses. This course also includes plenty of programming exercises and comprehensive materials. You can even start the course without separate registration.

**Modules:** This course is split into 2 parts which are further segregated into 7 parts each:**1️⃣** Java Programming I**2️⃣** Java Programming II

**Duration:** finishing a single part can take approximately 5-20 hours, and it is recommended to reserve at least 10 hours per part.

**Offered By:** University of Helsinki’s free massive open online course (MOOC)

#### Course 6 Algorithms, Part I

**About:** All the essential information that a serious programmer should know about algorithms, data structures are covered in this course. Moreover, this course emphasizes applications and scientific performance analysis of java implementation. Its first half covers elementary data structures, searching algorithms, and sorting whereas the second half emphasizes the graph and string-processing algorithms.

**Modules:** Following are the modules per week which are further segregated:**1️⃣**Course Introduction

Union-Find

Analysis of Algorithms**2️⃣**Stacks and Queues

Elementary Sorts**3️⃣**Mergesort

Quick Sort**4️⃣**Priority Queues

Elementary Symbol Tables**5️⃣**Balanced Search Trees

Geometric Applications of BSTs**6️⃣**Hash TAbles

Symbol Table Applications

**Duration:** Approximately 54 hours to get completed.

**Offered By:** Princeton University (Visit) at Coursera

#### Course 7 Algorithms, Part II

**About:** This is part two of the above Course.

**Modules:** **1️⃣**Introduction

Undirected Graphs

Directed Graphs**2️⃣**Minimum Spanning Trees

Shortest Paths**3️⃣**Maximum Flow and Minimum Cut

Radix Sorts**4️⃣**Tries

Substring Search**5️⃣**Regular Expressions

Data Compression**6️⃣**Reductions

Linear Programming (Optional)

**Duration:** This course takes around 63 hours to complete.

**Offered By:** Princeton University (Visit) at Coursera

**Databases**

#### Course 8 Database Management Essentials

**About:** This course provides you the foundation for your career in Database Development, warehousing, business intelligence, and for the entire Data Warehousing for Business Intelligence specialization. Here you’ll learn to create relational databases, write SQL statements to extract information to satisfy business reporting requests, create entity relationships diagrams (ERDs) to design databases, and analyze table designs for excessive redundancy. Along with developing these skills, you’ll use Oracle, MySQL, or PostgreSQL to execute SQL statements. This course is like a foundation for specialization learners. Skills such as Database (DB) Design, Entity-Relationship (E-R) Model, Database (DBMS), and SQL you will get through this course.

**Modules:****1️⃣**Course Introduction

Introduction to Databases and DBMSs**2️⃣**Relational Data Model and the CREATE TABLE Statement**3️⃣**Basic Query Formulation with SQL

Extended Query Formulation with SQL**4️⃣**Notation for Entity Relationship Diagrams

ERD Rules and Problem Solving**5️⃣**Developing Business Data Models

Data Modeling Problems and Completion of an ERD**6️⃣**Schema Conversion**7️⃣**Normalization Concepts and Practice

**Duration:** Approx 36 hours to finish.

**Offered By:** The University of Colorado (Visit) at Coursera.

#### Course 9 Data Warehouse Concepts, Design, and Data Integration

**About:** This is course is the second part of the* Data Warehousing for Business Intelligence Specialization*. So the above-mentioned i.e. its first course should be completed before you enter into this one.

**Modules:** Its Weekly distributed schedules are as follows:**1️⃣**Data Warehouse Concepts and Architectures**2️⃣**Multidimensional Data Representation and Manipulation**3️⃣**Data Warehouse Design Practices and Methodologies**4️⃣**Data Integration Concepts, Processes, and Techniques**5️⃣**Architectures, Features, and Details of Data Integration Tools

**Duration:** This course will take approx 22 hours to complete.

**Offered By:** The University of Colorado (Visit) at Coursera.

#### Course 10 Relational Database Support for Data Warehouses

**About:** This is the third part of the *Data Warehousing for Business Intelligence Specialization* Course.

**Modules:** **1️⃣**DBMS Extension and example Data Warehouse**2️⃣**SQL Subtotal Operators**3️⃣**SQL Analytic Functions**4️⃣**Materialised View Processing and Design**5️⃣**Physical Design and Governance

**Duration:** It will take approx 22 hours to complete.

**Offered By:** The University of Colorado (Visit) at Coursera.

#### Course 11 Business Intelligence Concepts, Tools, and Applications

**About:** So in the fourth course of “Data Warehouse for Business Intelligence Specialization” you will gain the knowledge and skills for using Data Warehouses for BI (Business Intelligence) purposes and for working as a Business Intelligence Developer.

**Modules:** The modules of this course are organized covering the BI concepts, BI systems, tools, applications, usage of Data WArehouse, etc. for business performance management and descriptive analysis which are as follows:**1️⃣**Decision Making and Decision Support System**2️⃣**Business Intelligence Concepts and Platform Capabilities**3️⃣**data Visualization and Dashboard Design**4️⃣**Business Performance Management System**5️⃣**BI Maturity, Strategy, and Summative Projects

**Duration:** Approx 22 hours.

**Offered By:** The University of Colorado (Visit) at Coursera.

#### Course 12 Design and Build a Data Warehouse for Business Intelligence Implementation

**About:** This course features a real world-class case study that integrates your learning across all courses in the specialization. So in response to this, you will design and create a small data warehouse, write SQL statements (to support analytical and summary query requirements) and use the MicroStrategy BI platform (for creating dashboards and visualizations).

**Modules:** Following are the modules under this course:**1️⃣**Course Overview**2️⃣**Data Warehouse Design**3️⃣**Data Integration**4️⃣**Analytical Queries and Summary Data Management**5️⃣**Data Visualization and Dashboard Design Requirements

Wrap Up and Project Submission

**Duration:** This course takes around 13 hours to get complete.

**Offered By:** The University of Colorado (Visit) at Coursera.

#### Course 13 MongoDB for Developers Learning Path

**About:** From fundamentals to getting started building a MongoDB-based app in Python, .NET, Java, or Javascript, you’ll learn everything in this course. You’ll also learn to optimize the performance of the deployment of your MongoDB. In other words, everything you need know about data modeling for MongoDB is here in this course.

**Modules:** In order to become an expert MongoDB Developer and to deploy and administer modern applications at scale you have to follow this learning path:**1️⃣**MongoDB Basics**2️⃣**Basic Cluster Administration**3️⃣**Aggregation Framework**4️⃣**MongoDB for Developers**5️⃣**MongoDB Performance**6️⃣**MongoDB Data Modelling

**Duration:** Approx 49 hours.

**Offered By:** MongoDB University

**Single Variable Calculus**

#### Course 14 Calculus 1A: Differentiation

**About:** This course will teach you mathematical notation, physical meaning, geometric interpretation, etc. about derivatives. Also, you will learn to differentiate any function you can think up and to be able to sketch the graph of many functions. Furthermore, you will learn linear and quadratic approximations of functions, simplification of computations, and maximize & minimize functions (to optimize properties like cost, efficiency, energy, and power).

**Modules:** This course contains the following chapters:**1️⃣**Limits

1. Limit Laws

2. Continuity

3. Intermediate Value Theorem**2️⃣**Differentiation

1. Introducing the Derivative

2. Rules for Differentiation of all known functions

3. Approximations**3️⃣**Application of Differentiation

1. Curve Sketching

2. Optimization

3. Related Rates

**Duration:** Estimated 13 weeks as 6-10 hours per week.

**Offered By:** MIT(Massachusetts Institute of Technology ) at edX

#### Course 15 Calculus 1B: Integration

**About:** Not only the geometric interpretation, its application, and relation with the derivative but you’ll also encounter functions that can’t be integrated without a computer. So you’ll develop a big bag of tricks to attack the functions that you can integrate by hand. Also, you’ll learn to find the center of mass, the stress on a beam during construction, the power exerted by a motor, distance traveled by rocket, etc. using integration.

**Modules:****1️⃣**Limits

1. Limit Laws

2. Continuity

3. Intermediate Value Theorem**2️⃣**Differentiation

1. Introducing the Derivative

2. Rules for Differentiation of all known functions

3. Approximations**3️⃣**Application of Differentiation

1. Curve Sketching

2. Optimization

3. Related Rates

**Duration:** Estimated 15 weeks (6-10 hours per week).

**Offered By:** MIT at edX

#### Course 16 Calculus 1C: Coordinate Systems & Infinite Series

**About:** In this course, you will learn to compute arc length, methods for parameterizing curves, doing calculus in polar coordinates, approximate functions with Taylor Polynomials, and Determine convergence properties of infinite series.

**Modules:** The chapter in this course are below:**1️⃣**Changing Perspectives

1. Parametric Equations

2. Polar Coordinates**2️⃣**Series and Polynomials**3️⃣**Approximations

1. Series and Convergence

2. Taylor Series and Power Series

**Duration:** Estimated 13 weeks ( 6 to 10 hours per week)

**Offered By:** MITx

**Linear Algebra**

#### Course 17 The essence of Linear Algebra

**About:** The Essence of Linear Algebra is actually a youtube course by the 3Blue1Brown channel. It’s a Playlist on the complete concept of Linear Algebra.

**Modules:** The contents of the Course Playlist are as follows:

- Vectiors
- Linear Combinations , Span, and Basic Vectors
- Linear Tranformations and Matrices
- Matrix Multiplication as Composition
- 3 Dimensional Linear Tranformations
- The Determinant
- Inverse Matrices Column Space and Null Space
- Non Square Matrices and Trasformations between Dimensions
- Dot Products and Duality
- Cross Products
- Cross Products in the Light of Linear Tranformations
- Cramer’s Rule, Explained Geometrically
- Change of Basis
- Eigenvectors and Eigenvalues
- A Quick Trick For Computing Eigen Values
- Abstract Vector Spaces

**Duration:** The playlist is of 3 hours approximately.

**Offered By:** 3Blue1Brown at youtube

#### Course 18 Linear Algebra

**About:** Designed for independent study this course has been organized as per the MIT course on Linear Algebra and split into three major units:

- Ax = b and the four Subspaces
- Least Squares, Determinants and Eigenvalues
- Positive Definite Matrices and Application.

These units are further divided into a sequence of sessions that covers the topics mentioned below.

**Modules:** Through this course, you’ll have a good understanding of the following topics:

- Systems of linear equations
- Row reduction and echelon forms
- Matrix operations, including inverses
- Block matrices
- Linear dependence and independence
- Subspaces and bases and dimensions
- Orthogonal bases and orthogonal projections
- Gram-Schmidt process
- Linear models and least-squares problems
- Determinants and their properties
- Cramer’s Rule
- Eigenvalues and eigenvectors
- Diagonalization of a matrix
- Symmetric matrices
- Positive definite matrices
- Similar matrices
- Linear transformations
- Singular Value Decomposition

**Duration:** Unpredictable.

**Offered By:** MIT Open Courseware

**Multivariable Calculus**

#### Course 19 Multivariable Calculus

**About:** This Course is basically the next part of Single Variable Calculus which covers differential, integral, and vector calculus for functions of more than one variable.

**Modules:****1️⃣**Vector and Matrices

2️⃣Partial Derivatives

3️⃣Double Integrals and Line Integrals in the Plane

4️⃣Triple Integrals and Surface Integrals in 3 Space**5️⃣**Final Exam

**Duration:** Unpredictable

**Offered By:** MIT Open Courseware

**Statistics & Probability**

#### Course 20 Introduction to Probability

**About: **This course is available on both Youtube and edX that you can choose through the link attached with the title of this course. Through this course, you’ll get the tools needed to understand data, science, philosophy, engineering, economics, and finance. Also, you’ll learn to solve challenging technical problems and apply those solutions in everyday life along with examples ranging from various fields.

**Modules:** Following are the units as per the edX course:

- Introduction, course Orientation, and FAQ
- Probability, Counting, and Story Proofs
- Conditional Probability and Bayes’ Rule
- Discrete Random Varianles
- Averages, Law of Large Numbers, and Central Limit Theorem
- Joint Distributiond and Conditional Expectation
- Markov Chains

**Duration:** Estimated 10 weeks (5-10 hours per week)

**Offered By:** Harvard University, Department of Statistics.

#### Course 21 Intro to Descriptive Statistics

**About:** Here you will learn to analyze, interpret, predict outcomes from data through basic concepts used to describe data. Also, you will learn several statistical study methods, create and interpret histograms, bar charts, frequency plots, center for distributions (mean, median, and mode), quantify data using range and standard deviation, interquartile range, using the Z-score and Z-table, using normal and standardized distributions, and many more. Lastly, you will apply the concepts of probability and Normalization to sample data sets.

**Modules:** Following lessons will be taught in this course:

**1️⃣**Intro to Research Methods**2️⃣**Visualizing Data**3️⃣**Central Tendency**4️⃣**Variability**5️⃣**Standardizing**6️⃣**Normal Distribution**7️⃣**Sampling Distribution

**Duration:** Approx 2 months

**Offered By:** Udacity

#### Course 22 Intro to Inferential Statistics

**About:** This course allows you to draw conclusions from data that might not be immediately obvious, and enhances your ability to develop hypotheses. You’ll also know to use common tests such as t-tests, ANOVA Tests, and regression to validate your claims.

**Modules:** The course has the following lessons:

**1️⃣**Estimation**2️⃣**Hypothesis Testing**3️⃣**t-tests**4️⃣**ANOVA**5️⃣**Correlation**6️⃣**Regression**7️⃣**Chi-squared Tests

**Duration:** Approx 2 months

**Offered By:** Udacity

**Data Science Tools & Methods**

#### Course 23 Tools for Data Science

**About:** Through this course, you’ll learn to use some most popular data science tools along with their features. You’ll also be taught about Jupyter Notebooks, JyupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. Not only the usage but features, limitations, executable programming language, and testing of each tool will be taught individually.

**Modules:** It has been divided into 4 weeks. The topics are as follows:

**1️⃣**Data Scientist’s Toolkit [17 videos (Total 84 min)]**2️⃣**Open Source Tools [12 videos (Total 54 min)]**3️⃣**IBM Tools for Data Science [15 videos (Total 72 min)]**4️⃣**Final Assignment: Create and Share your Jupyter Notebook. [1 reading]

**Duration:** Approximately 19 hours to complete.

**Offered By:** IBM at Coursera

#### Course 24 Data Science Methodology

**About:** Undoubtedly, this course aims to share a methodology that can be used within Data Science to ensure the relevance and proper manipulation of data used in problem-solving. So, this course offers you a solid understanding of the questions being asked and techniques to apply the data correctly to the problem at hand.

**Modules:** The course has the following modules:

**1️⃣**From Problem to Approach and From Requirements to Collection.**2️⃣**From Understanding to Preparation and From Modeling to Evaluation.**3️⃣**From Deployment to Feedback.

**Duration:** Approx 8 hours.

**Offered By:** IBM at Coursera.

#### Course 25 Data Science: Wrangling

**About:** It’s very rare that data is easily accessible in a Data Science Project. Most of the time the data is in a file, a database, extraction of web pages, tweets, PDFs, etc. Firstly we have to import the data into R and tidy it using tidyverse package. So the step of converting data from its raw form to the tidy form is called Data Wrangling. Obviously, it’s a critical step that enables you to make critical insights as a Data Scientist. So, here you’ll learn the Data Wrangling processes like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining.

**Modules:** You’ll learn the following things through this course:

**1️⃣**Importing Data into R from Different File Formats.**2️⃣**Web Scrapping**3️⃣**How to tidy Data using Tidyverse to better facilitate analysis**4️⃣**String processing with regular expressions (regex)**5️⃣**Wrangling data using dplyr**6️⃣**How to work with dates and times as file formats**7️⃣**Text mining

**Duration:** 8 weeks (1-2 hours per week).

**Offered By:** Harvard University at edX.

**Machine Learning/Data Mining**

#### Course 26 Machine Learning

**About:** In this course, you’ll learn the most effective ML techniques, and gain practice implementing and getting them work.

**Modules:** The course contains the following chapters:

- Introduction [5 videos (Total 42 min), 9 readings, 1 quiz]
- Linear Regression with One Variable [7 videos (Total 70 min), 8 readings, 1 quiz]
- Algebra Linear Review [6 videos (Total 61 min), 7 readings, 1 quiz]
- Linear Regression with Multiple Variables [8 videos (Total 65 min), 16 readings, 1 quiz]
- Octave/Matlab Tutorial [6 videos (Total 80 min), 2 readings, 2 quizzes]
- Logistic Regression [7 videos (Total 71 min), 8 readings, 1 quiz]
- Regularization [4 videos (Total 39 min), 5 readings, 2 quizzes]
- Neural Networks: Representation [7 videos (Total 63 min), 6 readings, 2 quizzes]
- Neural Networks: Learning [8 videos (Total 78 min), 8 readings, 2 quizzes]
- Advice for Applying Machine learning [7 videos (Total 63 min), 7 readings, 2 quizzes]
- Machine lewarning Sytem Design [5 videos (Total 60 min), 3 readings, 1 quiz]
- Support Vector Machines [6 videos (Total 98 min), 1 reading, 2 quizzes]
- Unsupervised learning [5 videos (Total 39 min), 1 reading, 1 quiz]
- Dimensionality Reduction [7 videos (Total 67 min), 1 reading, 2 quizzes]
- Anomaly Detection [8 videos (Total 91 min), 1 reading, 1 quiz]
- Recommender Systems [6 videos (Total 58 min), 1 reading, 2 quizzes]
- Large Scale Machine Learning [6 videos (Total 64 min), 1 reading, 1 quiz]
- Application Example: Photo OCR [5 videos (Total 57 min), 1 reading, 1 quiz]

**Duration:** Approx 61 hours.

**Offered By:** Stanford Online at Coursera

#### Course 27 Intro to Machine Learning

**About:** Here you will learn the end-to-end process of investigating data through the Machine Learning lens. In other words, you’ll learn to extract and identify useful features that best represent your data, a few of the most important ML Algorithms, and to evaluate the performance of your ML Algorithms.

**Modules:** The course contains the following lessons:

**1️⃣**Welcome to Machine Learning**2️⃣**Naive Bayes**3️⃣**Support Vector Machines**4️⃣**Decision Trees**5️⃣**Choose your own Algorithm**6️⃣**Datasets and Questions**7️⃣**Regressions**8️⃣**Outliers**9️⃣**Clustering**🔟**Feature Scaling

**Duration:** Approx 10 weeks.

**Offered By:** Udacity

#### Course 28 Mining Massive Datasets

**About:** This is an advanced level course intended for graduates in computer science. In order to go through this course, one should have had courses in Data Structures, Algorithms, Database Systems, Linear Algebra, Multivariable Calculus, and Statistics. The course is based on Text Mining of Massive Datasets.

**Modules:** You will learn the following topics:

- MapReduce systems and algorithms
- Locality-sensitive hashing
- Algorithms for data streams
- PageRank and Web-link analysis
- Frequent itemset analysis
- Clustering
- Computational advertising
- Recommendation systems
- Social-network graphs
- Dimensionality reduction
- Machine-learning algorithms

**Duration:** Estimated 7 weeks (5-10 hours per week)

**Offered By:** Stanford Online at edX

#### Course 29 Process Mining

**About:** You will come to know the key analysis techniques in process mining, various process discovery algorithms and various other analysis techniques that use event data will be presented. Moreover, the course focuses on easy-to-use software, real-life data sets, practical skills to directly apply theory in a variety of application domains. Furthermore, it has three main types of process mining i.e. 1. Discovery, 2. Conformance, and 3. Enhancement.

**Modules:** Topics of this course are given below:

**1️⃣**Introduction and Data Mining**2️⃣**Process Models and Process Discovery**3️⃣**Different Types of Process Models**4️⃣**Process Discovery Techniques and Conformance Checking**5️⃣**Enrichment of Process Models**6️⃣**Operational Support and Conclusion

**Duration:** Approx 22 hours to complete.

**Offered By:** Eindhoven University of Technology at Coursera

**Order of the Courses:**

It is best recommended to follow the sequence of the courses as mentioned above. But some of the courses can be taken parallel as well. The topics or modules within a course must be covered sequentially. The flowchart below demonstrates the order of the courses:

**Track Your Progress:**

As there are multiple courses involved from various platforms and some of them can also be taken parallel, so tracking your progress might get challenging. Don’t worry this Free Data Science Degree Course by OSSU takes care of that as well. You just need to follow the steps below:

**Step 1:** Go to Trello and create an account there.**Step 2:** After opening your account copy this board there. Click here to see how to copy?**Step 3:** Thirdly you just need to pass the Course Cards from the ` Curriculum column` to the

`and`

**Doing**`column.`

**Done****Footnote**:

- All the courses mentioned above have to be pursued individually and through multiple platforms.
- You can access all the above mentioned courses for absolutely free. In case of the paid courses, click the course in
**audit mode**after free enrollment. - Most of the courses are paid if you need their individual
**sharable certificate**. - One can even get individual course
**certificates for free**through finacial aid if available. Click on the link attached to know more about it. - So, if you want the sharable certificate of any of the above mentioned courses individually you may go through the above points (3 & 4).
- Feel free to comment in case of any queries or questions regarding this article.