I don’t spend a penny to teach myself data science methods.

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Editor’s note: Making a challenging plan for the New Year and implementing it is the best New Year gift for yourself. So let’s learn data science by ourselves. Data science is closely related to every industry you can think of, and in terms of employment and future development trends, it has unmatched advantages in other professional fields. Data science can be mastered by self-study, so how do you make full use of various online and offline resources, master data science for free, and enhance your competitiveness? This article by Madison Hunter, The Step-by-Step Curriculum I’m Using to Teach Myself Data Science in 2021, teaches you to teach yourself data science.

Key points: Free self-study of data science, providing more possibilities and better choices for your employment and future development!

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The end of the year is the best time to make plans for the upcoming New Year. For some people, the New Year’s plan includes going to the gym, learning a new language or reading 50 books.

For me, my new year plan is to learn data science. I like challenges, so I am ready to learn data science without spending a penny.

Thanks to the generosity and ingenuity of many data scientists today, there are many free learning resources on the Internet that cover every tiny detail of data science. From learning how to code in Python, to learning multivariate calculus, to learning how to develop machine learning algorithms, these make it possible for everyone to become a skilled and competitive data scientist without spending thousands of dollars or years of work Time to get a college degree, diploma or certificate.

There is no better time to challenge yourself and develop new skills than the new year. If the epidemic has taught us anything, it is that improving skills is not a waste of time, and can help you get better employment options.

DataScience is becoming one of the most attractive technical fields because of its low unemployment rate and the safety of work is basically guaranteed. In addition, data science is closely related to every industry you can think of, including medicine, military, business, science, education, government, and technology. In other words, you can learn data science techniques and apply them to any field that you think has great potential or already knows and has some experience. So, is there any reason not to like data science?

Why do I want to learn data science

I received a technical diploma in software engineering when I graduated two years ago. Due to the province-wide economic recession, and it is a place that is more resistant and slow to respond to economic modernization, it is not easy for me to find one. A professional counterpart job, but the work content is relatively junior.

Now I go back to school and study for a degree in Earth Science. I like this degree very much, and I have been thinking about how to make myself more valuable and more competitive after graduation.

So I thought of data science. I believe that adding data science to my knowledge base on the basis of my software engineering professional experience will make me a favored employee of any environmental or geotechnical company. In addition, having knowledge of data science opened the door for me to start my own environmental consulting company after graduation (if I can’t find a job, I will basically start my own business). At the same time, data is a cool thing that can tell us a lot of information about the past, present, and future. This information is especially useful when we observe and understand environmental or geological events.

So why should I not study data science?

Before starting to introduce the details, first give some parameters

First of all, before setting up the course, I need to set some parameters.

  • This “New Year’s Resolution” does not have to be completed within a one-year deadline. The idea that everyone can learn and master data science within a year is actually absurd, especially for those who have other full-time jobs. To ensure that this plan is in place, I will set the completion time according to my actual needs. I don’t have any hasty or quick ideas, and I don’t want to have only a superficial understanding of this field. On the contrary, I want to lay a solid foundation in this field.

  • First learn programming. Because I already have a programming background (C#, SQL, JavaScript, Java, PHP), I want to learn a new programming language first. I think this is a great way to get into data science because it’s better for usLay the foundation for a thorough understanding of how data works. It is recommended that you consider Python as your usual language when doing data science research. If you do not understand this language, you cannot communicate well. Therefore, before setting up my course, I will first put learning programming language first.

  • The course I use must be free. I am a student, so it is impossible for me to spend thousands of dollars on online courses. Fortunately, there are many free courses on the Internet, and there are also many courses in the university for you to listen to for free. For me, there is one exception: As a mandatory requirement for a science degree, I need to take some math courses in university, and these are the only courses I pay for. In addition, free courses are usually concentrated in the more “beginner” stage, mainly based on entry-level content, so as the depth of knowledge learning, I will have to spend more money to buy more advanced courses.

  • Plans can change over time. In the process of learning data science, it is almost certain that I will change the plan according to the actual situation at any time. Whether to increase or decrease courses, the plan needs to be flexible. It is very possible that in the process of self-study data science, I will have a deeper understanding of how to use the best method to learn this course, and at the same time my chosen course will also change.

I plan to use resources to create my own data science course

Thanks to the fast-growing data science community on Medium, I was able to search for the best information resources on this platform, which will help me develop my own learning courses. From these articles, I simulated my own learning path to ensure that I can lay a solid foundation and learn the most comprehensive knowledge.

Total Course

The total course will be divided into four learning topics. I will study in the following order: programming, mathematics and statistics, data analysis and visualization, and machine learning.

Programming

Python (a computer high-level programming language)

Learn Python–a complete introductory course

Python that everyone can learn–a full set of university Python language courses

Python for mastering data science-introductory course (learning Python language, Pandas data analysis, arrays, Matplotlib plotting database)

Scientific computing with Python certification

Python for mastering data science

Python data knotStructure

SQL (Structured Query Language)

SQL Tutorial-A Complete Database Introduction Course

SQL–Introduction to Computer Science–CS50 of Harvard University (2018)

SQL for data analysis

SQL for Data Science

JavaScript (a programming language)

JavaScript algorithm and data structure authentication|

Math and Statistics

Limited Mathematics (a course of the science degree I am studying)

Introduction to Statistics (a course of science degree)

Introduction to Calculus (a course of science degree)

Multivariate Calculus

Linear Algebra

Data analysis and visualization

Learning Data Science Tutorial-A Complete Introductory Course | freeCodeCamp

Python data analysis-a complete introductory course (including arrays, Pandas data analysis, Matplotlib plotting database, Python data visualization tool Seaborn)

Data structure required for advanced courses–a complete tutorial taught by a Google engineer

Data visualization certification

Data analysis with Python certification

Python data analysis

Python data visualization

Data Science: Visualization

Machine learning

TensorFlow 2.0 (a deep learning system and tool) complete tutorial – Python neural network introductory tutorial

Practical deep learning tutorials for programmers-from fast.ai (a deep learning platform) and Jeremy Howard (data scientist) founded the fast.ai technology sharing platform, providing free information about deep learning technology Series of video tutorials)

Intensive learning course-a complete machine learning tutorial

Machine learning with Python certification

Machine learning with Python certification: a practical overview

Data science projects for deep learning

One thing I learned from my software engineering diploma is that it’s best to applyAnd practice to learn to code. Therefore, when I finish studying in this professional field, I will also pass the Capstone project (after the course is over, the college or the teacher team will collect the results of the student project), Kaggle competition (provide a machine learning competition for developers and data scientists , A platform for hosting databases, writing and sharing codes) and hackathons (a competition that encourages contestants to develop programs or software products at a fixed time) to deepen the learning effect.

Like writing with a pen, learning to program and processing data is like exercising a certain muscle. The more you practice, the easier it will be. In an ideal state, the rule of thumb is to spend a few hours on weekends on personal projects. Therefore, if I can develop the habit of spending a few hours on a data science project every weekend, then I will be able to better grasp some important concepts in this professional field while completing the course.

Final thoughts

This course of study is not perfect no matter what, and as I continue to learn and make progress, it may change. However, it is more certain that this course will give me a basic understanding of data science, and I will be able to learn more efficiently and better on this basis in the future.

As I said before, the New Year is the best time to start a new journey. However, I like to use a relatively difficult and complicated way to complete a thing (I like challenging tasks), so I am not satisfied with the task of going to the gym or reading more books. I tend to choose things that are more difficult to operate, such as self-study data science. Fortunately, my stamina is very good, and I am good at a programming learning experience that tests stamina, and I can learn some difficult areas relatively easily by myself (thanks to those online universities). Fortunately, I am entering a community where everyone is welcomed and encouraged. This community will play an important role in my journey towards data science.

The only thing to do now is to dive in.

Translator: Vivi