Python is possibly the most popular programming language of the 21st century. These are some of the reasons why Python is gaining popularity at such a high rate:
- Python readability and syntax, o the words and characters used to communicate with the computer, it's simple and intuitive. It's just like the English language!
- Python supports multiple paradigms, but most would describe Python as an object-oriented programming language (OOP).
- It is a language without costs and open source.
- Python has hundreds of different libraries and frameworks, what a great addition to your development procedure. They save a lot of manual time and can easily replace the whole answer.
Libraries and frames
Then, I list some of the popular libraries that are often used in numerous data science and machine learning projects:
Ideal for data management and analysis. Pandas provide data manipulation control.
NumPy is a free library for numerical computing. Provides high-level mathematical functions along with data manipulations.
This library is related to scientific and technical computing. SciPy can be used for data optimization and modification, algebra, special functions, etc.
Scikit-learn is a free software machine learning library for the Python programming language. It has quite a few classification algorithms, regression and grouping including support vector machines, random forests, gradient increase, k-meansetc.
Malplotlib is a complete library for creating static visualizations, animated and interactive in Python.
Seaborn is a Python data visualization library based on matplotlib. Provides a high-level interface for drawing attractive and informative statistical charts.
Other fields where Python is used
Apart from data science, Python has many more applications. Being such a flexible and easy-to-use language, has built up a huge fanbase for himself. Some of the fields where Python is used are:
- Game development
- Web development
- Image processing and computer vision
- PNL (natural language processing)
- Medicine and Pharmacology
- Astrophysics and Astronomy
- Physical particles
- Neuroscience
- GUI development (graphical user interface) … and much more
Then, as you can see, if you somehow fail as a data scientist, you can easily change your career and take advantage of your skills as a Python programmer or developer.
The central philosophy of the language is summarized in the document. the Image source, which you can see in any Python IDE by running the line:
import this
Since it is the most popular programming language out there, there is no shortage of online resources where you can learn Python, but i firmly believe in quality over quantity. In this blog, I have tried including some of the free resources that I found very useful in my early days of learning Python.
1. Sololearn
Sololearn teaches you Python in a very playful and interactive way. The course is divided into many bite-size modules, each with quizzes at the end to examine their learning. These modules are further subdivided into topics to help you focus better.. Sololearn is also enabled on Playstore and Appstore which helps you learn on the go.
2. DataPeaker
Vidhya Analytics, at the same time blogging on data science and machine learning, offers several free courses that will teach you Python and data science in general, from a beginner level to a fairly advanced level. Instructor uses Jupyter Notebook to hands-on concepts. You can easily download the notebooks for personal practice and later use. Certificates are also provided at the end of the courses.
3. Kaggle
At the same time having a large data science community, Kaggle it also offers several free certification courses. Even though these courses may not have many in-depth teachings, offer good practice for the Kaggle interface and kernel, which will be useful to you in the long run, since he is a data science enthusiast, will spend most of his time at Kaggle. .
4. Krish Naik
When it comes to the availability of free resources, Youtube is one of the best sites to search. The tutorials offered by Krish Naik they are truly impressive. His teaching methods are on par with the courses that are available on reputable websites like Coursera, Edx, etc. The playlist also contains numerous guided projects to help you understand how Python is used in real-life projects and data..
5. HackerRank
Even though this is a web portal to practice coding, instead of learning to code, I got more skills to write Python programs, from HackerRank than any other course or study material found online. Coding is like math. You must do it, instead of just studying it in theory. Writing a program per day is a healthy habit, that I myself follow and recommend everyone to follow it. HackerRank offers practice problems from beginners to fairly advanced levels. The user interface is excellent and the questions are very well written, with examples, so there is no ambiguity regarding the questions.
Even after all this, if you cannot distinguish what the problem requires, you can check the Discussions (edit) tab, where a large part of the community gets involved and talks to help others with any type of problem they have (Professional advice: if you just can't solve the question, there is always a guy on the discussion forum who posts all his code hahaha). I highly recommend studying the theory of the resources mentioned above and practicing problems related to the topics you just studied., next to each other, to build a solid Python coding foundation.
When you're just starting out on your data science journey, it will be overwhelming to see so much material available online. My advice to you is to choose a course or program that you think will be good for you and start doing it..
Be consistent and keep practicing. Some concepts may seem difficult, but avoid worrying, look for them on other websites, practice more problems related to that topic and in no time you'll be proficient in Python. The websites and courses mentioned above are the ones that I feel helped me a lot while starting out., and I hope they do the same to you. Health!!
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