The full text has 4864 words, and the expected learning time is 14 minutes
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In the 21st century, traditional education has transformed into a choice, not a necessary stage in life.
With the prosperity of the Internet and the rise of large-scale online open courses (mooc MOOC ), people can choose to learn data science online to avoid the debt burden of students.
statistics show that online teaching allows students to learn 5 times more materials every hour of training. The benefits of online learning are endless, including reduced costs and flexible timing and environment.
Democratization of data science
It is now 2020, and data science is more democratic than before. This means that any individual can conduct data science research with little expertise, as long as they have the right tools and a large amount of data. As data permeates every corner of the industry, it is a trend to have the skills of data scientists, and thus a workforce that speaks the language of data has been created.
With this in mind, with online courses, it is possible for a complete beginner to start studying data science. All that is needed is a well-structured learning course, correct learning methods, perseverance motivation and passion, and auxiliary training programs.
How to learn data science online?
Best MOOC + Correct Learning Method + Passion + Project
So in this post I will introduce the best MOOCs, they are free and very valuable for those who want to become data scientists.
Data Science Wayne Chart
Drew Conway
Multidisciplinary intersection of data science can be visualized by Drew Conway's disliked Venn Chart . Through this chart, we can infer that the fields of data science include hacking skills, machine learning and multivariate analysis.
I have ruled out domain expertise because it depends on the company you are in, and online courses don't get hard skills like communication skills, which you need to talk to real life people to do (although this can be cringe-in).
The following 20 courses will be divided into 3 parts:
. Data Science
- Python
- R language
- structured query language
- Basics of Machine Learning and Artificial Intelligence
- Deep Learning
- Natural Language Processing
- Natural Language Processing
- Computer Vision
Instead of fielding different courses, or spending hours filtering the disturbance information on the web, I edited this list of courses that I found useful in machine learning, artificial intelligence, data science and programming learning. Let’s take a look at this list below
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MOOC
0. Learn how to learn
This course can teach you one of the most important skills in your life, which is to learn how to learn. It will teach you some tips and methods to make sure you remember what you have learned and help you apply them in real life. Because having the right learning method is an important prerequisite for learning anything, that's why it is listed as serial number 0, because it lays the foundation for every course below.
Data Science
1.CS109 Data Science - Harvard
CS109 is a course that introduces five key aspects of the survey:
· Data entanglement, cleaning and sampling to obtain the appropriate data set
· Data management can quickly and reliably access big data
· Exploitable data analysis that generates hypotheses and intuitions
· Prediction based on statistical methods such as regression and classification
· Communication results through visualization, stories and interpretable abstracts.
In addition, it is taught with Python!
2. Learn from Data - Caltech
For all data enthusiasts, it is crucial to have a deep understanding of how machines learn from data and how to improve processing. This is a course introducing machine learning, including basic theories, algorithms, and applications.
What will you learn:
· What is learning?
· Can machines learn?
· How to do it?
· How to do it well?
3. Introduction to Big Data - UC San Diego
is now the era of big data, and all data science enthusiasts have an obligation to understand what big data is and why it is important.
What you will learn:
· Terms and core concepts behind big data problems, applications and systems.
· How useful is big data in a personal business or career.
· Introduction to one of the most commonly used frameworks Hadoop
4. Data Science – Johns Hopkins University (JHU)
In short, this course teaches you how to ask the right questions, manipulate data sets, and create visualizations to communicate results.
What you will learn:
· Use R language to clean, analyze and visualize data.
· From data collection to publication, browse the entire data science pipeline.
· Manage data science projects using GitHub.
· Perform regression analysis, least squares, and inference using regression model.
Finally, you will have a vertex project in which you will build a real product by applying real world data and learn something. This work will then portray your newly acquired data science strength.
math
5. Mathematics for Machine Learning - Imperial College London
This course is a math major in machine learning. It covers all the math knowledge you need and helps update all concepts and theories you may have forgotten in school. Most importantly, this course teaches you the application of computer science and gives you a more intuitive understanding of the relationship between matrix and regression and machine learning and data science.
This major is divided into three main courses:
. Linear Algebra
At the end of this major, you will gain the necessary mathematical knowledge to continue your journey and take more advanced courses in machine learning.
6. Linear Algebra - MIT
is taught by the unique Gilbert Strong . Mr. Strong is the best linear algebra lecturer (personally think). So if you're looking for a good linear algebra course, that's it.
This course covers matrix theory and linear algebra, emphasizing topics that are useful in other disciplines.
7. Multivariate Calculus - MIT
Multivariate Calculus is another important concept in data science. Calculus is necessary from simple linear regression to support vector machines and neural networks.
This course covers the differential, integral and vector calculus of multivariate functions.
8. Probability and Statistics - Stanford University
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Probability and Statistics are the basis for all miracles in data science. Without a p-value distribution and binomial distribution and all jargon, it would be impossible to predict with data.
What will you learn:
. Exploring data analysis
Unfortunately, this course has ended, so the following is a review course! Or if you want similar courses from Carnegie Mellon University, click here.
Hacking Skills
9. Google Python Course
Free Course designed by Google for beginners. This course consists of notes, videos and a lot of code exercises to help you get started writing code in Python.I found it useful and recommended it to anyone who wishes to start learning Python.
10. Applied Data Science and Python - University of Michigan
5 professional courses at the University of Michigan introduce data science to learners through the Python programming language. This course is easy to use and intuitive Jupyter Notebooks.
The five courses are:
. Introduction to Data Science
11. R language statistics - Duke University
This specialization helps you master the analysis and visualization in R language, which is one of the top programming languages in the field of data science.
What will you learn:
· Create a repeatable data analysis report
· Uniformity of inference statistics
· Perform frequency inference statistics and Bayesian models to understand natural phenomena and make data-based decisions
· Correctly and effectively convey statistical results without relying on statistical terms, criticize data-based requirements and evaluate data-based decisions
· Use R language package to analyze and visualize data.
12. Structured query language in data science - University of Davis, California,
Structured query language (SQL) is an important tool for data scientists to retrieve and process data, and is a recognized language for interacting with database systems. This course is tailored for beginners who want to add SQL to the LinkedIn (Social Platform for Workplace) skills section and start using it to mine data. Most importantly, they will learn to ask the right questions and come up with good answers to provide valuable insights to your organization.
What will you learn:
· Create a table and be able to move data into the table
· Common operators and how to combine data
· Concepts such as case statements, data governance and profiling
· Discuss topics about data, and use real-world programming homework to practice
· Explain the structure, meaning and relationships in source data, and put SQL As a professional data to shape data for target analysis
Machine Learning and Artificial Intelligence
13. Machine Learning Crash Class – Google
This crash course is a self-study guide for aspiring machine learning practitioners, featuring video lectures, real-world case studies and practical exercises. This is one of the courses under the "Learn with Google" Artificial Intelligence Initiative that encourages everyone to learn AI.
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14. Artificial Intelligence Elements - University of Helsinki
"Artificial Intelligence Elements" is a series of free online courses developed by Reaktor and the University of Helsinki. It aims to encourage everyone to learn what AI is, what AI can and can’t do, and how to start creating AI avenues. These courses combine theoretical and practical exercises and can be completed at your own pace.
15. Machine Learning - Ng
Ng 's machine learning is one of the most popular online courses on the Internet, and it contains all aspects. From the most basic to neural networks and support vector machines, and finally add an application project. The benefit of this course is that Ng is an incredible teacher. The bad aspect is taught using MATLAB (I prefer Python).
16. Practical deep learning course for programmers - Fast.ai
If you want to learn about deep learning for free, Fast.ai is an online course. Everyone on the internet recommends it, and it is undoubtedly a valuable resource for those who want to learn deep learning. This course uses jupytorch's notebook for learning and uses it as a main tool for writing deep learning code.
17. Deep Learning - Stanford University
Deep Learning is one of the most popular skills in artificial intelligence.In this course, you will learn the basics of deep learning, learn how to build neural networks, and learn how to lead successful machine learning projects. You will learn convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
18. CS224N Natural Language Processing and Deep Learning - Stanford University
Natural Language Processing (NLP) is one of the important technologies in the information age and an important part of data science. NLP applications are everywhere - in the fields of web search, email, language translation, chatbots, etc. In this course, students will receive a comprehensive introduction to cutting-edge research on deep learning in natural language processing.
What you will learn:
· Design, implement and understand your neural network model.
· PyTorch!
19. CS231n: convolutional neural network for visual recognition —Stanford University
Computer vision has become ubiquitous in our society. Its application areas include search, facial recognition, drones, and the most eye-catching one is Tesla cars. This course explores the details of deep learning architectures in depth, focusing on learning end-to-end models of these tasks, especially image classification.
What you will learn:
· Implement, train and debug their neural networks
· Learn more about cutting-edge research in computer vision.
The final task involves training a convolutional neural network with millions of parameters and applying it to the largest image classification dataset (ImageNet).
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Honorary nomination
· Khan Academy
· Kaggle course
· Blue Brownian nature of linear algebra, calculus and neural networks
· Towards the learning part of data science
Action plan
Online learning data science is sometimes difficult because you don't have a structured course to tell you what to do. But rather than looking at it like this, you’re aware that you have the freedom to build a learning path that suits you and can give yourself the best. One benefit is that you can learn when your brain is at its highest efficiency and rest when it is less efficient. In addition, you can decide what to learn based on your interests and passion.
Recommendation
When studying online, some tips are to keep taking simple notes, write some experiences at the end of the day, or record what you have learned on your blog. Similarly, it is important to use Feynman technology to explain what you have learned to friends and family, especially for complex topics like data science.
Also, when learning machine learning algorithms and neural networks, it is crucial to learn it while writing code so you can see what you are learning and have a better understanding of the topic at hand. It's also great to be part of an online community like Reddit, Discord, and so you can ask questions and get good answers from experts.
Summary:
. Take notes/blog
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HD12. Use Feynman Skills
HD13. Encoding and Concepts (creating a neural network from scratch)
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Last, quote Arthur w · Chicklin and Stephen · c · Elman's sentence
"Just just sit in class and listen to the teacher's lectures, remember pre-packaged homework, and spit out the answers, students can't learn much. They must talk about what they are learning, write down reflections on it, connect it with past experiences, and apply it to their daily lives. They must make what they have learned a part of themselves."
Thanks for reading, I hope this article can provide you with a lot of ideas.
Please leave any other free online courses for data science you suggest in the comments!
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