By taking programming courses for developers, most ambitious data scientists begin learning Python. They are also starting to solve Python programming puzzles at sites such as LeetCode, thinking they need to master programming concepts before they can start to use Python to parse data.
This is a major mistake that Python is used by data scientists to get cleanup, view, and create models; rather than designing software applications. But you need to spend much of your time studying Python libraries and modules to do these activities. I advice you to learn data science masters only after knowing the basic concepts.
Core principles of programming
Effective programming is not about memorizing the syntax but is about learning a new thought of the process.
So, take the time to create a good foundation for the fundamentals of programming. This will help you to view the solutions with a machine instruction in mind.
If programming is new to you
If you’re completely new to programming, we recommend the excellent Python book Automating Boring Tricks, which was published free online under a Creative Commons license.
The book promises “practical programming for complete beginners” and holds every lesson grounded. Read Chapter 6-Management of strings and answer the practical questions along the way.
Essential Data Science Libraries
First, we will concentrate on How to Learn Python for Data Science part. As described earlier, Python has a flexible collection of data science libraries. Libraries are only pre-existing collections of functions and objects you can import to save time in your script.
Numpy and Pandas – A great tool for learning them
Python is slow to get, it has numerically heavy calculations and to handle large quantities of data. So, you might wonder why Python is the most common language in data science programming.
The solution is that it is not difficult to import numeral calculation tasks in the form of a C or Fortran extension to the bottom layer of Python. Numpy and Pandas do exactly that.
Need to know Numpy. This is the most basic module with Python for scientific computing. Numpy offers support for highly optimized multidimensional arrays, which are the most machine learning algorithms.
You will get a clear understanding of programming and a realistic understanding of important libraries at this stage. That actually covers much of the data science that you need to get started with Python.
Some students will be feeling a little overwhelmed at this stage. All right, and that’s perfectly natural.
You may feel less stressed if you choose to take the gradual, traditional bottom-up approach, but it may have taken ten times longer to arrive.
Now the trick is to dive in quickly and to start putting things together. Again, so far our goal is to learn enough to get started.
Now, it’s time to refine your skills through several training sessions and programs.
Learning to visualize data using Matplotlib
Matplotlib is the complete Python package for graphical visualization development. You will learn how to build some of the most popular charts using Matplotlib, such as line charts, bar charts, scatterplots, histograms, and boxplots.
Seaborn is another strong library of layouts based on Matplotlib and closely integrated with Pandas. At this point, I suggest you learn how to build simple charts in Matplotlib quickly and accurately, and not concentrate on Seaborn.
Python lists and strings
A list is a Python data structure which is an ordered sequence of modifiable or alterable elements. Each object or value within a list is called a feature. Since strings are defined as double quotation marked characters, lists are specified with values in square brackets .
Strings can be described simply using single (‘), double (“), or triple (“’) inverted commas. Strings with (‘’’) can span several lines, and are often used in docstrings (Python style documentation). \ is used as an escape character. Please note that Python strings are permanent, meaning that part of the strings can not be modified.
Study basic statistics with Python
Before studying the fundamentals of statistics, most ambitious data scientists jump straight to explore machine learning.
Do not make that mistake because statistics are the backbone of data science. Aspiring data scientists who understand data, on the other hand, simply study theoretical concepts rather than technical concepts.
Through scientific theories, I mean, you need to learn what kind of problems, Statistics can solve. Understand the difficulties that Statistics can solve.