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Find the best Data Science courses from Udemy, Udacity, Coursera, Skillshare, Upgrad, Pluralsight, MIT, Harvard, Stanford, IIT to boost your career in 2021. There are many jobs that pay you a handsome salary, good package if you have Data Science experience. Learn data science courses and view top data science job openings specially for you.

Data Science is the most booming sector and you can have an amazing career in the same field. There are many jobs which will pay you a handsome salary in this field. Even if you are unaware of this field then we got some of the best data science courses for free.

Just sit back, and relax because here we will not just mention the names of some top data science courses but also define each of them, instruct you on how to learn data science, and also update you on top data science job openings.

Data Science became known mainly when the world moved into the era of Big Data, so did the need for storage. Until 2010, it was the biggest threat and source of concern for the business industries. The main emphasis was on developing a platform and data storage solutions. Now that Hadoop and other frameworks have successfully solved the storage issue, the emphasis has shifted to data processing. The secret sauce here is Data Science.

Data Science will make all of the theories that you see in Hollywood sci-fi movies a reality. **Artificial Intelligence's future is Data Science**. As a result, it's important to understand what Data Science is and how it can help.

When practicing data science online, it's critical to not only gain a general understanding of what you're doing but also to get enough experience applying data science to real-world problems. Here are some things which you need to keep in mind before learning data science:

**Learn to love data**- Data science is a vast and nebulous profession, making it difficult to master. It's extremely difficult. People who lack motivation, leave halfway through it. You need to develop the interest and the passion to start learning.**Learn data science by doing**- This means that the best way to learn data science is to work on projects. By working on projects, you gain skills that are immediately applicable and useful. You get to know the ground reality because data scientists have to start a project from start to finish, and most of the work is fundamental.**Learn to communicate insights**- Data analysis is only valuable if you can communicate it you can convince others to act on what you found. You have to understand the topic and theory and learn to organize it then you should be able to communicate it well. Communication of complex concepts is what you have to be good at.**Learn from your peers**- In data science, teamwork can be very important. Data scientists often work as a part of a team to solve specific problems and learning from your colleagues enhances your personal and professional growth.

There are various sites and content on the web for data science courses, many are misleading while some are confusing. But DON'T WORRY, we are here with all the information and necessary links from the top data science courses from top international universities and the best free courses from various online platforms.

Harvard University's Online Data Science Certificate Program is available through edX, a leading e-learning website. It gives you a head start in data science roles by teaching you core data science skills like R programming, Machine Learning, and others through real-world case studies.

This is a well-known and intense self-paced curriculum that lasts 2 to 4 months. It consists of nine graduate-level courses taught by Harvard Professor of Biostatistics Rafael Irizarry and delivered entirely online at a fraction of the cost of conventional college, making it extremely open, affordable, and versatile.

R Basics, Visualization is covered. As a result, the courses start with the basics and advance to a capstone project in which you apply the skills and expertise you've learned during the series to a real-world problem. You can learn how to function independently on a data analysis project by the end of the program. Students earn a Professional Certificate of Data Science upon graduation, which they may display to prospective employers.

**Key Highlights**

- Learn open-source tools used in data science like Jupyter Notebooks, Zepplin, RStudio, and IBM Watson.
- Learn the basics of Python, Pandas, and NumPy.
- Build databases, collect and analyze data from them using Python.
- Use Python libraries to generate data visualizations.
- Well designed content and all the topics are covered elaborately.
- Graded Assignments with Peer Feedback.
- Assignments and projects that provide practical skills with applicability to real jobs that employers value – random album generator, predict housing prices, best classifier model, battle of neighborhoods.
- No prior programming or computer science knowledge is required.

The MIT Institute for Data, Systems, and Society (IDSS) has created a stand-alone Data Science and Statistics Certification program that is provided by edX. Mastering the basics of data science, statistics, and machine learning is the aim of this Micromasters data science program.

It is one of the best data science programs available, and it consists of four rigorous online courses followed by a credential exam that is practically proctored. Probability, Data Analysis in Social Science, Fundamentals of Statistics, Machine Learning with Python, and Capstone Exam in Data Science and Statistics are among the graduate–level courses offered. This program's Probability course is the same as the introduction to probability course taught on the MIT campus for the past 50 years. All of the courses are taught by MIT faculty and follow a high-quality, hands-on approach to learning.

To participate in the program, you should be familiar with single and multivariable calculus, linear algebra, mathematical reasoning, and Python programming. Each course in the MIT Data Science Certificate program lasts 13 to 16 weeks, and students are required to devote 12-14 hours per week to each. Learners receive an individual Verified Certificate for each course they complete, as well as a MicroMasters Program Credential if they pass the capstone test at the end of the program.

**Key Highlights**

- Learn how to apply data mining and machine learning algorithms to real-world data sets.
- Hands-on Python projects include an in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning.
- Learn how to use probabilistic modeling and statistical inference to evaluate big data and make data-driven predictions, as well as how to apply effective methodologies for extracting useful knowledge to assist decision-making.
- Develop and implement machine learning algorithms to make sense of unstructured data and extract useful insights.
- Work on supervised and unsupervised learning approaches, such as clustering methodologies and deep neural networks.
- Learners who complete this MIT MicroMasters credential will apply to the MIT IDSS's Doctoral Program in Social and Engineering Systems (SES) and have their coursework accepted for credit.
- After completing this qualification, learners may apply for jobs such as Data Scientist, Data Analyst, Systems Analyst, Business Intelligence Analyst, Data Engineer, and so on.

This Data Science Specialization is a 10-course introduction to the principles and tools you'll need in the data science pipeline, taught on the Coursera platform by renowned Johns Hopkins University professors. It aims to improve learners' ability to ask the right questions, manipulate data sets, draw inferences, and construct visualizations to present their findings.

This certification program consists of ten modules, with a Capstone project at the end. These courses include: Version Control, Markdown, GitHub, R Programming, Exploratory Data Analysis techniques for summarizing data, Reproducible Research, Statistical Inference, Regression Models.

The Capstone Project will be based on real-world issues and will be carried out in collaboration with government, business, or academic partners. It will enable students to demonstrate their data science abilities to prospective employers. Beginner-level experience in Python and some familiarity with Regression are listed as requirements for this course.

- R is a programming language that can be used to clean, analyze, and visualize data.
- From data collection to printing, navigate the entire data science pipeline.
- To handle data science projects, use GitHub.
- Use regression models to perform regression analysis, least squares, and inference.
- To evaluate and manage large-scale data sets, balance both the theory and practice of applied mathematics.
- Create models that can be automated to solve real-world problems using formal techniques and abstraction methodologies.

This IBM Data Science Certification Program, which is provided via the Coursera website, is designed to help students and professionals prepare for careers in data science. With extensive hands-on and practical learning, you can master data science and machine learning principles.

This is one of the best Data Science Programs, with nine courses covering the fundamentals of data science, open-source tools, and libraries, data science methodology, Python programming, working knowledge of databases and SQL, data analysis and visualization with Python, basics of machine learning, and an applied data science capstone project.

Each of the nine courses consists of three to six modules, each of which takes between two and four hours to complete. It could take up to 2-3 months for a complete novice to complete the program. After completing this data science training, you'll receive a certificate and an IBM open badge (in reality, 9 IBM badges for each of the program's 9 courses) that demonstrate data science performance.

**Key Highlights**

- Learn open-source tools used in data science like Jupyter Notebooks, Zepplin, RStudio, and IBM Watson.
- Learn the basics of Python, Pandas, and NumPy.
- Build databases, collect and analyze data from them using Python.
- Use Python libraries to generate data visualizations.
- Well designed content and all the topics are covered elaborately.
- Graded Assignments with Peer Feedback.
- Assignments and projects that provide practical skills with applicability to real jobs that employers value – random album generator, predict housing prices, best classifier model, battle of neighborhoods.
- No prior programming or computer science knowledge is required.

This Coursera Data Science program was created by four University of Michigan professors. It aims to teach learners with a basic understanding of programming how to manipulate and interpret data effectively.

It consists of five courses that cover data science processes, techniques, and skills in Python programming. Learners should have a simple working knowledge of Python or at least some programming experience.

Statistical analysis, machine learning, knowledge visualization, text analysis, and social network analysis are all included in this curriculum.It teaches popular python toolkits such as Pandas, Matplotlib, Scikit-learn, NLTK, and NetworkX to gain insight into data.

Specifically, the 5 courses are – Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python. Learners need to complete all five courses to earn the specialization certificate.

**Key Highlights**

- Examine a social network's accessibility.
- Conduct a mathematical inference review.
- Using the Matplotlib library, learn the fundamentals of visualization with an emphasis on reporting and charting.
- Determine whether a data visualization is good or poor, and include examples. Develop best practices for making simple maps and visualizations.
- Machine learning can be used to improve data analysis.
- Learn data mining techniques including clustering and classification.
- Learn how to clean, manipulate, and run simple inferential statistical analysis on tabular data.
- Learn about network generation models and how to solve the link prediction problem.

Skills in Deep Learning and Machine Learning are in high demand. This Deep Learning Specialization course by deeplearning.ai is your best bet if you want to master them and develop a career in AI. Andrew Ng (CEO/Founder Landing AI; Co-Founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain) has created this program with two professors from Standard University. This is one of the most sought-after deep learning programs.

This data science specialization program consists of five courses that teach the foundations of Deep Learning, how to create neural networks, and how to lead effective machine learning projects. It's a beginner-to-intermediate level approach to learning neural networks — efficient non-linearity learning algorithms. Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more will be covered. Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization, Structuring Machine Learning Projects, and Convolutional Neural Networks and Sequence Models are the five courses.

The course curriculum has been meticulously planned, with well-timed videos and a steady pace. To take the course, you must have a clear understanding of mathematics and machine learning, as well as some programming experience. Since the course is presented in Python, having some prior experience with the language is a plus.

**Key Highlights**

- Understanding how neural networks operate, as well as How and Why We Make Deep Neural Networks?
- To be able to create, train, and apply completely connected deep neural networks, you must first learn how to build, train, and apply them.
- TensorFlow and several optimization algorithms are covered.
- Case studies in healthcare, autonomous driving, sign language reading, music generation, and natural language processing are all being worked on.
- Heroes and top leaders of Deep Learning are interviewed and share their personal stories.

Andrew Ng, a world-renowned AI specialist, created this Machine Learning Certification Course, which covers the most powerful machine learning techniques and how to apply them in the real world. You can learn not only the theory of machine learning and statistical pattern recognition but also how to apply these techniques to new problems quickly and effectively. This course is widely regarded as one of the best online data science courses.

Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, help vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; broad margins); reinforcement learning and adaptive control are some of the topics discussed in this course.

Learners should have a clear understanding of computer science concepts, as well as a basic understanding of linear algebra and probability theory. The data science machine learning course is highly involving with multiple videos in each lecture, followed by quizzes and assignments. It is approximated that one would need 11 weeks to take the course spending around 5-7 hours a week.

**Key Highlights**

- Work with massive datasets in a variety of fields and formats.
- Learn about parametric and non-parametric algorithms, clustering (the k-Means algorithm), dimensionality reduction, and anomaly detection, among other things.
- Programming tasks that will assist you in understanding how to put the learning algorithms into effect.
- Read about the best practices of machine learning and AI engineering in Silicon Valley.
- Many a case studies and applications to learn how to apply machine learning algorithms to smart robots (perception, control), text comprehension (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other fields.

This MicroMasters program is a set of graduate-level data science courses developed by the University of California, San Diego professors and delivered online via edX. It's an intensive curriculum that will help you develop the skills you'll need to advance as a data scientist. It aims to gain a thorough understanding of the mathematical and analytical methods that underpin data science, as well as how to apply them to make data-driven business decisions.

In the form of four courses, his UCSD Data Science certification program essentially covers two sides of data learning – the mathematical and the applied. Python for Data Science, Probability and Statistics in Data Science with Python, Machine Learning Fundamentals, and Big Data Analytics with Spark is the courses offered. Learners will be introduced to a series of strong, open-source resources for data analysis and data science. They specifically learn how to use: Python, Jupyter Notebook Environment, Numpy, Matplotlib, Git, Pandas, Scipy, Apache Spark.

At each stage of completing a course, learners earn a verified certificate for the course. After completing all four program courses, they earn the MicroMasters Program Certificate.

**Key Highlights**

- Learn how to load and clean data from the real world.
- Learn how to conduct large-scale data analysis and present your results in a compelling, visual manner using common open-source tools.
- Learn how to use noisy data to make accurate statistical inferences.
- Learn data structures using machine learning.
- Use the methods covered in the lectures to visualize complex data.
- Use Apache Spark to explore data that won't fit in a single computer's memory.
- Learn to build data science tools, explore public datasets, and discuss evidence-based findings.
- Work on realistic assignments and projects to build your portfolio and put what you've learned in class to use.

When you enroll in one of these classes, you should commit to learning as if you were in a college course. One aim of online data science education is to increase mental discomfort. It's quick to fall into the trap of logging in to watch a few videos and pretend to learn, but unless your mind hurts, you're not learning much.

One of the most frustrating aspects of studying data science online is that you never know when you've learned enough. When studying online, you don't have as many good barometers for progress as you would in a traditional school setting, such as passing or failing exams or whole courses. Projects will help with this by demonstrating what you don't know and then acting as a record of information until they're completed.

Overall, the project should be the primary focus, with courses and books serving as supplements.

Data science is one of the highest-paying careers available today. Data science jobs have risen to popularity as the hottest work of the twenty-first century in recent years. We've compiled a list of the ten most recent data science job openings below for this week.

Experts in data science are required in almost every industry, not just technology. In reality, the five largest tech firms in the United States—Google, Amazon, Apple, Microsoft, and Facebook—employed just 0.5 percent of the country's workforce. However, specialized education is usually needed to break into these high-paying, in-demand positions.

Here are some top data science job careers you can break into:

**Data Scientists**- Data scientists may be required to analyze vast volumes of complex raw and processed data to uncover trends that will support a company and aid in strategic business decisions. Data scientists are much more technical than data analysts.**Machine Learning Engineer**- Data funnels and software solutions are created by machine learning engineers. Strong statistics and programming skills, as well as knowledge of software engineering, are usually needed. They are responsible for running tests and experiments to track the efficiency and functionality of machine learning systems in addition to designing and developing them.**Machine Learning Scientist**- New data methods and algorithms, such as supervised, unsupervised, and deep learning techniques, are being investigated for use in adaptive systems. Research Scientist or Research Engineer are popular titles for Machine Learning Scientists.**Applications Architect**- Follow the actions of business applications and how they communicate with one another and with users. Applications Architects are also responsible for designing the architecture of applications, which includes elements such as the user interface and infrastructure.**Data Engineer**- On gathered and stored data, perform batch or real-time processing. Data Engineers are also in charge of creating and maintaining data pipelines that enable Data Scientists to access information by creating a robust and integrated data ecosystem within an enterprise.

There are many other jobs also which could be missed out from the list but one thing is sure that any job in data analysis will pay you a handsome salary with lots of interesting work experience.

Data science is a massive, fascinating, and satisfying area to learn about and work in. To become an effective data scientist that businesses want to employ, you'll need a wide range of skills, a broad range of knowledge, and a passion for data, and it'll take longer than the hyped-up YouTube videos suggest.

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