Here are some of the data science courses which are provided online globally with online certificates and lectures are provided according to your feasibility.

Data Science: R Basics Provided by Harvard University (HarvardX)Enroll NowWhat you will learn

- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots

The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

We'll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll learn how to apply general programming features like ''if-else,'' and ''for loop'' commands, and how to wrangle, analyze and visualize data.

Python for Data Science Provided by The University of California, San Diego (UCSanDiegoX) Enroll NowWhat you will learn

- The basic process of data science
- Python and Jupyter notebooks
- An applied understanding of how to manipulate and analyze uncurated datasets
- Basic statistical analysis and machine learning methods
- How to effectively visualize results

In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?

This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:

- python
- jupyter notebooks
- pandas
- numpy
- matplotlib
- git
- and many other tools.

Introduction to Data Science Provided by Microsoft Enroll Now What you will learn

- How the Microsoft Data Science curriculum works
- How to navigate the curriculum and plan your course schedule
- Basic data exploration and visualization techniques in Microsoft Excel
- Foundational statistics that can be used to analyze data

This is the first stop in the Data Science curriculum from Microsoft. It will help you get started with the program, plan your learning schedule, and connect with fellow students and teaching assistants. Along the way, you'll get an introduction to working with and exploring data using a variety of visualization, analytical, and statistical techniques.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Data Science Research Methods: R Edition Provided by Microsoft Enroll NowWhat you will learn

- After completing this course, you will be familiar with the following concepts and techniques:
- Data analysis and inference
- Data science research design
- Experimental data analysis and modeling

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a good understanding of some problem or idea and build useful models on this understanding. Because of the principle of ''garbage in, garbage out,'' it is vital that the data scientist know how to evaluate the quality of information that comes into data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this course, you will learn the fundamentals of the research process from developing a good question to designing good data collection strategies to putting results in context. Although the data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

Developed as a language with statistical analysis and modeling in mind, R has become an essential tool for doing real-world Data Science. With this edition of Data Science Research Methods, all of the labs are done with R, while the videos are tool-agnostic. If you prefer your Data Science to be done with Python, please see Data Science Research Methods: Python Edition.

Data Science Research Methods: Python Edition Provided by Microsoft Enroll nowWhat you will learn

- After completing this course, you will be familiar with the following concepts and techniques:
- Data analysis and inference
- Data science research design
- Experimental data analysis and modeling

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a good understanding of some problem or idea and build useful models on this understanding. Because of the principle of ''garbage in, garbage out,'' it is vital that a data scientist know how to evaluate the quality of information that comes into data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this course, you will learn the fundamentals of the research process from developing a good question to designing good data collection strategies to putting results in context. Although a data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

Data Science Ethics University Of Michigan Enroll NowWhat you'll learn

- Who owns data
- How we value different aspects of privacy
- How we get informed consent
- What it means to be fair

This course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws.

Everyone, including data scientists, will benefit from this course. No previous knowledge is needed.

Data Science: Probability Provided by Harvard University (HarvardX) Enroll NowWhat you will learn

- Important concepts in probability theory including random variables and independence
- How to perform a Monte Carlo simulation
- The meaning of expected values and standard errors and how to compute them in R
- The importance of the Central Limit Theorem

In this course, part of our Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance

.