[PYTHON] [Even in the video] Data science at the famous university level that you can learn for free [Yes, if it is coursera. ]
I have a timetable in the center of the article, but the course link is connected to the image.
0. Target audience
・ Interested in data science, statistics, machine learning, and AI ・ I didn't major in college ・ It is difficult to spend a lot of money on learning ・ I feel that self-study is a difficult area ・ Understand English at the beginner level of junior high school students
** Those who are not suitable for reading this article **
・ I have no intention of learning English ・ I'm not interested in the field of data science and just want to get an overview. (In this case, you should spend money to ask for an introductory course or a tutor)
1.First of all
1-1. Let's study by ourselves
In May 2020, I wrote 5 masterpieces of data science that can be learned for free as an article. This is the sequel.
By this time, the virus will have subsided by August. I made an unfounded prediction, There is still a self-restraint mood.
I'm sure there are new graduates who joined the company with the aim of becoming a data scientist. I would like to tell the hiring side as soon as possible and make it full-fledged. Maybe some of you are looking down on it and feel that you haven't had it.
1-2. Let's have a creative vacation
Famous [Newton](https://ja.wikipedia.org/wiki/%E3%82%A2%E3%82%A4%E3%82%B6%E3%83%83%E3%82%AF%E3 % 83% BB% E3% 83% 8B% E3% 83% A5% E3% 83% BC% E3% 83% 88% E3% 83% B3) is also said to have made a big discovery during the period of refraining from going out due to the plague epidemic. I am.
Now that you have time, let's improve yourself with the same level of education as university. If work is unstable, I think the data science field is still an area where you can change your career.
1-3. What is coursera?
cousera is an online educational organization founded by Andrew Ng and Daphne Koller of Stanford University.
** Basically free if you don't ask for a degree ** ** Almost all English ** (See below, but don't be scared)
Andrew has a lot of media exposure and is famous in the machine learning area. (You released Ngboost a while ago) He is also the teacher of the famous course of machine learning in coursera.
At coursera, well-known universities publish videos and programming assignments to promote online degrees and education.
1-4. But English?
・ Japanese subtitles translated by volunteers are attached to famous courses. ・ Automatic translation subtitles are displayed ・ The English script is displayed even without subtitles, so you can translate it and listen. ・ Technical talk can be roughly understood in some terms ・ When you listen, you will naturally get hearing power ~~ (I can't hear Russian and Spanish accents, so I give up and translate) ~~
Only after studying English! Please try it for the first time. If you really want to prepare, get in the habit of watching your favorite overseas drama on Netf 〇 ix in English only.
1-5. Which data science system should I receive?
Coursera has learning contents in various fields. In this article, we have summarized the contents related to data science in order of level, like a university timetable. I chose it so that it wouldn't be a dream to reach a level where I can speak and use properly in the field of data science in just over half a year **.
I myself was not involved in any field of mathematics or computer science when I was a student, but coursera has helped me.
I chose a course that I can take for free. Even if you are worried about the cost, please register.
There is a registration button on the upper right, so let's register the information and log in.
1-6. Can't register for free?
step1 ・ Find the course you want to take and press free registration
step2 ・ It costs money or you are required to register your credit card.
step3 ・ Press the course tab to jump to each course
step4 ・ When you reach a specific course, you will see buttons such as "Audit" and "Audit". ・ Although issues are locked, you can check basic video content.
It would be great if Japanese universities could observe classes at this level before enrolling ... Since cousera is a non-profit organization, if you improve your skills and earn more income, register for "coursera plus" and support everyone!
2. Main story: I tried to build a curriculum
I made recommendations while considering the order of learning. There may be other excellent courses, but I have missed them, so if you have any recommendations, please comment. Also, I haven't completed about half of the courses shown here, so even if the explanation is wrong, it's Yurushite.
Some courses, such as 1 week x 10 courses, last about 30 hours, while others Some courses are heavy, such as 5 weeks x 12 courses, so please adjust yourself.
2-1. Coursera without permission 1st semester of Faculty of Data Science
In the first semester, I tried to organize a curriculum with the theme of "relationship between machine learning and mathematics".
――We will create a model using R and python while learning the necessary mathematical knowledge. ――I feel that it is extremely effective to learn the theory and see how the numbers actually change. ――When and how the numbers change --Does the package result be the same as if you implemented it yourself from theory? --Apply to actual data with the created model ――Visualize for understanding anyway --Be able to do the calculations you learned ――We will proceed with learning with the above in mind.
| Subject | Mathematical basics | R &Bayesian statistics,ML | python &statistics,ML |
|---|---|---|---|
| 1st period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/0fce6767-1738-f771-06c8-fd3c788de018.png "width=80%> | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/b4da27fc-b8e6-c4ce-972f-6d805f9292ae.png "width=80%> |
| Description | Data Science Math Skills | Statistics with R | Statistics with Python |
| 2nd period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/1d47ff7a-02a1-b122-894c-235b8725a852.png "width=80%> | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/99e8c2bf-55f3-0993-c3d6-15d10e5f18b2.png "width=80%> |
| Description | Mathematics for Machine Learning Specialization | Bayesian Statistics: From Concept to Data Analysis | IBM AI Engineering |
| 3rd period | -blank- |  |
-blank- |
| Description | -none- | Bayesian Statistics: Techniques and Models | -none- |
2-2. Coursera without permission Middle term of Faculty of Data Science
In the middle term, I tried to organize a curriculum with the theme of "wide machine learning knowledge and time series".
――Using the theory and programming skills you learned in the previous term --Search for a package similar to the lesson content --Try to implement it yourself --Consider converting something implemented in another language to R or python
- Time series --Understand stochastic processes, Gaussian processes, etc. and learn approaches to time series data --Learn time-series-specific statistics such as ACF and PACF --Understanding the idea of testing and modeling unique to time series --To be able to handle time series data based on the concept of statistics, probabilities, and vectors -** If you want to be a kaggler, don't miss the advanced Machine Learning ** kaggle course
| Subject | ML,DL | Stochastic process,Time series |
|---|---|---|
| 1st period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/8cf90a9c-9c07-db60-51a0-60d2cc78d826.png "width=80%> |
| Description | Machine learning | Stochastic processes |
| 2nd period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/b0ee4d0a-795c-d08f-a4ac-91156e4fb56a.png "width=80%> |
| Description | Deep Learning Specialization | Practical Time Series Analysis |
| 3rd period |  |
-blank- |
| Description | Advanced Machine Learning | -none- |
2-3. Coursera without permission, Faculty of Data Science, 2nd year
In the second semester, I tried to organize a curriculum with the theme of "more advanced mathematical knowledge, strengthening of thinking ability and expertise in the real world".
――We have collected the fields necessary to master advanced machine learning. --Analysis --Proof method --Optimized mathematics --Combination mathematics --Graph theory --MCMC, belief propagation ――Economic finance is a problem related to money that is relevant to everyone in the real world ――There is no loss in having knowledge about money as a culture --Think about money with AI --Learn using reinforcement learning and recursive deep learning that did not appear until the middle term
| Subject | Mathematical basics | Bayesian statistics,Graph theory | Finance,Economy,ML |
|---|---|---|---|
| 1st period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/4774804e-9373-d9b5-fc65-1577d76d5055.png "width=80%> | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/b74acf8e-bf59-9f69-cd88-d99d73139dc2.png "width=80%> |
| Description | Mathematics for Data Science | Bayesian Statistics: Mixture Models | Reinforcement Learning for Trading Strategies |
| 2nd period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/8ca45239-2f49-8783-18dd-36d287c82ad2.png "width=80%> | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/dee79e86-9346-623b-17b1-8bf69c7f0945.png "width=80%> |
| Description | Introduction to Discrete Mathematics for Computer Science | Probabilistic Graphical Models | Finance & Quantitative Modeling for Analysts |
2-4. Free elective courses in the Faculty of Data Science with coursera
From here, let's take classes according to your specialty
--Finance: If you want to learn more about finance, economy, and time series --Life science: clinical knowledge such as public health, classification, risk assessment, survival analysis, genetics --Thermodynamics: When you get a pinch on words such as fluid mechanics and quantum --IT technology: When it comes to development, DevOps, cloud, distributed computing, infrastructure --Specialized mathematics: Algebra equations, groups, fields, full search algorithms
| Subject | Economy,Finance,investment | Thermodynamics,quantum | IT technology |
|---|---|---|---|
| 1st period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/80ed386f-22d7-c345-353c-a50ccbca6f01.png "width=80%> | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/9b093fa3-d768-88cf-24de-f83da213dce3.png "width=80%> |
| Description | Investment Management with Python and Machine Learning | Statistical Thermodynamics | Cloud Computing |
| 2nd period |  |
-blank- | <imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/411ddf2a-1e30-3853-2901-a6f9f2d3afe6.png "width=80%> |
| Description | Fundamentals of Quantitative Modeling | -none- | Getting Started with Google Kubernetes Engine |
| 3rd period |  |
-blank- | -blank- |
| Description | Machine Learning and Reinforcement Learning in Finance | -none- | -none- |
| Subject | Professional mathematics | Medicine,Life science |
|---|---|---|
| 1st period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/a4ab5bb0-92c6-d352-6b27-f8e0d9bac6d6.png "width=80%> |
| Description | AI for Medicine | Introduction to Galois Theory |
| 2nd period |  |
<imgsrc="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/275572/e3e669cf-5f35-ccc4-eedb-de23e69a762d.png "width=80%> |
| Description | Statistical Analysis with R for Public Health | game theory |
| 3rd period |  |
-blank- |
| Description | Big Data, Genes, and Medicine | -none- |
3. Finally
Two birds with one stone if you can understand English. Rather than reading a book yourself, you may find it easier to understand what you are talking about in the video and what you see on the board. Even if you search for youtube, it is difficult to find such systematically organized contents. It will be easier to understand if you search for the part you wondered using youtube or the net.
When you take the above course, we would appreciate it if you could share the output of what you learned. We are also waiting for recommended courses.
Recommended Posts