Don’t expect that you will get satisfying results right away in just one try. In addition, doing repetitive things for a

Now here’s a mini-exercise. Can you find the documentation of the latest stable versions of the core Python language and of the two libraries mentioned here, NumPy and pandas? Hint: Search engines are your friend. The answer to this exercise is given at the end of this blog post.

In a more expensive course, you might have an instructor or a teaching assistant whose office hours you can visit when you’re stuck. Your instructor might also give you a professional reference for your first technical job. However, after a certain point, there is only so much hand-holding you will have. At some point, you are the one expected to lead your teammates on the job, and no one is there for you. You’ll need to learn how to rely on other resources, so why not save the money and be more independent from the start?

In my case, Docker was very important in deployment. It helps me deploy multiple modules in containers. Apart from that, Tensorflow Serving is also very useful to easily make the trained model can serve prediction requests.

When your code does not work, another tip is to read the error messages you receive. Part of your error message should be common to a variety of other error messages, and part should be a distinct phrase. Take the part that is distinct and copy/paste it into a search engine. You might want to put it in quotes, to search for the consecutive phrase rather than the non-consecutive words. This will hone your search down to web pages where your specific error, and its fix, is discussed. Sometimes these web pages do not have a fix but have clues you can also search for. Tracing the clues is part of learning how to code.

I believe there is still much to learn. But the most important advice is, don’t be afraid to fail. Because basically research and experimentation are processes of trying and failing then getting up to try again.

The first thing you need to do, probably before you write a line of code, is to learn to read the programming language documentation and the textbooks. You are probably in the best position if you learn Python, which is an especially popular and versatile programming language. Familiarity with the documentation can give you a feel for how to write Python and what the language will and will not do. The core Python language has extensive official documentation and tutorials. So do the specialized Data Science and other Python libraries taught in most Python-based Data Science courses, such as NumPy and pandas.

For learning data science, one option is to order and go through an on-demand Data Science course without an instructor. These courses are far easier on your budget than a conventional instructor-led coding boot camp, let alone an accredited computer science degree. Like other technical career preparation courses, you’ll get plenty of opportunities to practice. Without intensively practicing, there is no way to gain the experience to become a professional.

The program may also not give an explicit error, but may still not be behaving how you want. You need to think about the problem line of code by line of code, to pinpoint the specific commands you will need to change. One of the first programming commands you will learn is how to print out lines of text onto your screen. Add these commands to the program, after each line or so, to verify the lines of code in your program are running the correct number of times.

I believe there is still much to learn. But the most important advice is, don’t be afraid to fail. Because basically research and experimentation are processes of trying and failing then getting up to try again.

A programming library is a reusable set of programming commands, with a defined interface to enable this set of commands to be used in a program. The core Python language is available in all Python programs, and other libraries can be specially added.

In Python, you may also be creating something called a variable. A variable is a value stored in a specific location in memory and given a name. When you refer to the name of the variable, you can access its value. Variables are very important in most programs for storing and sharing information. And as you run the program, you can specify how the variable’s value changes. When you test your program by printing out the line of code, you can include the value of the variable to double-check that this value has changed to what you want.

Good documentation usually contains information such as configuration of hyperparameters, epochs, methods, data, and scores. In addition, providing automated scripts to run programs is helpful for other engineers without having to read the entire code first.

I was one of those people who said “It worked on my machine!” when there was an error deploying our system to the production. And again, I hated myself for that. For this reason, it is very important to know some useful frameworks and tools for deployment. After doing some exploration, you can compare them and choose which one is most suitable based on your needs.

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Write not only for yourself, but also for others. Creating documentation can be helpful for other engineers if they want to keep experimenting or just use your program. It would be nice if the documentation is also easy to read and understand by people outside our expertise.