Data science is an exciting and booming industry that involves acquiring judgment and knowledge and skills from data. To get a top data science job, the most important thing is to have a solid foundation in key data science expertise, including programming, statistical analysis, data processing methods, and machine learning.

Fortunately, there are many free online learning resources that can help you develop these abilities and lay the foundation for a career in data science. Such resources include e-learning platforms such as Coursera, edX and DataCamp, which offer a wide range of courses in data science and related fields.

Coursera

The various courses in the web-based learning platform Coursera cover data science and subject areas. Such courses often involve multiple disciplines such as machine learning, data statistical analysis and applied statistics, and then are taught by experts and scholars from famous universities.

Here are some examples of data science courses on Coursera:

• Application of data science and Python systematization: this systematization is provided by the University of Michigan and consists of five courses covering the basics of data manipulation, analysis and visualization using Python.
• Machine learning: this course, offered by Stanford School, provides a detailed introduction to machine learning, including linear regression analysis, logical regression, neural networks and clustering algorithms.
• Data Science Science Methodology: this course, provided by IBM, covers the basics of data science, including data preparation, data cleaning, and data exploration.
• Applied Statistics and R systematization: this systematization is offered by the University of Connecticut and includes four courses covering statistical inference, regression modeling, and machine learning using the R computer language.

You can do free assistance and get this kind of verification free of charge. However, taking a course simply for verification may not get a desired job in the data science industry.

Kagel.

Kaggle is a data science competition platform, which provides superior conditions for learning and training data science professional skills. People can improve their professional skills at the level of data statistical analysis, machine learning and other branches of data science by participating in the test of the service platform and a large amount of data.

Here are some examples of completely free courses available on Kaggle:

• Python: this course covers the basics of Python programming, including basic data types, system structure, function formulas and control modules.
• Little Panda: this course covers the basics of applying Little Panda to carry out data operations, including data cleaning, data consolidation and information reconstruction.
• Big data Visualization: this course covers the basics of big data visualization using Matplotlib and Seborn, including trend lines, bar charts and bar charts.
• Introduction to machine learning: this course covers the basics of machine learning, including classification, re-classification and clustering algorithms.
• Beginner machine learning: this course covers a better thematic style of machine learning, including logical regression models, model selection, and hyperparameter adjustment.
• SQL: this course covers the basics of SQL, including data statistics, data filtering, and data aggregation.
• Deep neural networks: this course covers the basics of deep learning, including neural networks, neural networks, and recurrent neural networks.

EDX

EDX is another web-based learning platform that offers courses in data science and related fields. Many of the courses in EdX are taught by experts from top universities, and the website offers both free and fee-based teaching methods.

Some of the free courses on data science offered on EdX include:

• Basics of data Science: this course, provided by Microsoft, covers the basics of data science, including data exploration, data preparation and big data visualization. It also covers the important topic styles in machine learning, such as reversion, scientific classification and clustering algorithms.
• Introduction to Python in data Science: this course is provided by Microsoft and covers the basic knowledge of Python programming, including basic data types, system structure, function formulas and control modules. It also covers important data science libraries in Python, such as Pandas, NumPy, and Matplotlib.
• Introduction to R in data Science: this course, provided by Microsoft, covers the basics of R programming, including basic data types, system structure, function formula movement, and packages. It also covers important data science libraries in R, such as dplyr, ggplot2, and tidyr.

All of these courses are free, which means you can browse each course material and lecture for free. However, if you expect to browse the role of a large number of courses or obtain training certificates, you will have to pay a certain fee. In addition to this course, edX also offers a full range of paid courses and programs on data science, machine learning and related topics.

DataCamp

DataCamp is a web-based learning platform that offers courses in data science, machine learning and other related areas. The site offers a new interactive numbering challenge project that can help you build real-world data science expertise.

The following courses are available free of charge on DataCamp:

• Introduction to Python: this course covers the basic knowledge of Python programming, including basic data types, system structure, function formulas and control modules.
• Introduction to R: this course covers the basics of R programming, including basic data types, system structure, function formula movement and packages.
• Introduction to SQL: this course describes the basics of SQL, including data statistics, data filtering, and data aggregation.
• Apply red panda to carry out data operation: this course covers the basic knowledge of applying red panda to carry out data operation, including data cleaning, data merging and information reconstruction.
• Importing data in Python: this course describes the basics of importing data into Python, including reading files, transferring it to a database system, and using Web API.

All of these courses are free and can be browsed through DataCamp's online learning platform. In addition to this course, DataCamp offers a wide range of paid courses and programs covering big data visualization, machine learning and data engineering.

Udacity

Udacity is a web-based learning platform that offers courses in data science, machine learning and other related areas. The site offers completely free and paid courses, many of which are taught by industry professionals.

Here are some examples of completely free data science courses available on Udacity:

• Python programming learning: this course covers the basic knowledge of Python programming, including basic data types, system structure, function formulas and control modules. It also covers important data science libraries in Python, such as NumPy and Pandas.
• SQL for data statistical analysis: this course covers the basics of SQL, including data statistics, data filtering, and data aggregation. It also illustrates higher-level theme styles in SQL, such as joins and subqueries.
• Introduction to data science: this course covers the basics of data science, including data information disputes, exploratory data analysis, and statistical inference. It also covers the key machine learning techniques, such as reversion, scientific classification and clustering algorithms.

MIT Open course Mobile Software

MIT OpenCourseWare is an online database of MIT course materials. The site offers a wide range of courses in data science and related fields, all of which are provided free of charge.

The following are some of the completely free data science courses offered on MIT Open courses:

1. Electronic information science and Python programming learning: this course covers the basic knowledge of Python programming, including basic data types, system structure, function formulas and control modules. It also covers important data science libraries in Python, such as NumPy, Pandas, and Matplotlib.
2. Introduction to probability and Statistics: this course covers the basics of probability theory and statistical inference, including probability distribution functions, significance tests, and confidence intervals.
3. Machine learning of large data sets: this course covers the basics of machine learning, including linear regression analysis, logical regression, and k-mean clustering algorithms. It also describes expertise in the application of large and medium-sized data, such as map-Reduced and Hadoop.

GitHub

GitHub is a channel for sharing and cooperation in coding, and it can become a valuable network resource for learning and training data science skills. However, GitHub does not offer completely free courses. As a result, people can explore many new open source system data science projects hosted on GitHub to learn more about how data science can be used in specific situations.

Scikit-Learn is a popular Python library for machine learning, which provides a series of optimization algorithms for classification, re-classification and clustering algorithms. It can also be used as a special tool for data preprocessing, model selection and identification. This project is open source code and can be obtained from GitHub.

Jupyter is an open source system Web application for building notebook computers that interact with data sharing. Jupyter laptops provide a form of combining coding, text, and multimedia system details into a separate word, making it easier and easier to explore and communicate the scientific results of data.

These are just a few examples of the many open source system data science projects available on GitHub. By exploring and contributing to such projects, people can gain valuable experience at the technical level of data science tools, as well as create his asset allocation while demonstrating his expertise to potential employers.