What Should I Learn in Python for Data Science?

Step 1- Learn Python Basics

You have to start with learning Python Basics. when I say, “Python Basics”…you might be thinking about what exact topics you have to learn.

Right?

So, don’t worry… I am going to list the topics which you have to learn in this step. So that, you will not be confused about topics.

In Python Basics, learn the following topics-

Installing Python & Setting Up Your Environment

• What It Is: This means downloading and installing Python on your computer and setting up a workspace (like Anaconda or Jupyter Notebook) where you can write and run your Python code.
• Why It’s Important: A good workspace helps you easily manage different Python tools and run your code smoothly.

Numbers

• What It Is: Python uses different types of numbers, such as whole numbers (integers), decimal numbers (floats), and more.
• Why It’s Important: Data science often involves calculations, so understanding how Python works with numbers is very important.

Strings

• What It Is: A string is a piece of text, like a word or a sentence. In Python, you use strings to work with text data.
• Why It’s Important: Many datasets contain text information, like names or addresses. Knowing how to handle text data is crucial for cleaning and analyzing data.

Lists

• What It Is: Lists are a way to store multiple items in a single place. They are like a collection of things, such as a list of numbers or names.
• Why It’s Important: Lists help you organize data and perform actions on multiple pieces of data at once, which is common in data science tasks.

Basic Commands in Python

• What It Is: Basic commands are the most common and essential instructions in Python, like printing output (print()) or checking a variable’s type (type()).
• Why It’s Important: Learning these basic commands is necessary to write any Python code.

If-Else Statements

• What It Is: These are commands that let you make decisions in your code. They allow your program to choose between different actions based on certain conditions.
• Why It’s Important: Data science often involves making choices, like filtering data based on certain criteria.

Loops

• What It Is: Loops are used to repeat a block of code multiple times. There are two main types of loops: for and while.
• Why It’s Important: Loops are very useful for performing the same action on many data points, like rows in a dataset.

Functions

• What It Is: Functions are reusable blocks of code that perform a specific task. You create a function using the def keyword.
• Why It’s Important: Functions help you write cleaner code and save time by reusing code.

Variable Scope

• What It Is: Scope defines where a variable can be accessed in your code. Python has local and global scopes.
• Why It’s Important: Knowing about scope helps avoid errors when your programs get bigger and more complex.

Dictionaries

• What It Is: A dictionary is a collection of items where each item has a key and a value (like a word and its meaning in a dictionary).
• Why It’s Important: Dictionaries are helpful for storing and quickly accessing data in a key-value format, which is common in data science.

Sets

• What It Is: Sets are collections of unique items with no duplicates.
• Why It’s Important: Sets are useful when you want to remove duplicates or compare groups of data.

Classes

• What It Is: Classes are like blueprints for creating objects in Python. They help you organize your code into reusable blocks.
• Why It’s Important: Understanding classes is helpful when you need to build complex programs or create custom data structures.

Methods & Attributes

• What It Is: Methods are actions that objects can do, and attributes are properties of objects.
• Why It’s Important: Knowing methods and attributes is important for working with Python libraries where objects (like tables in pandas) have built-in actions and properties.

Modules & Packages

• What It Is: Modules are Python files with reusable code, and packages are collections of modules.
• Why It’s Important: Data science involves using many external tools and libraries. Knowing how to import and use these is key to getting the most out of Python.

List Comprehension

• What It Is: A simple way to create new lists from existing ones. For example, [x**2 for x in range(10)] creates a list of squares from 0 to 9.
• Why It’s Important: List comprehensions make your code shorter and easier to read, which is helpful when working with lots of data.

Map, Filter, and Lambda

• What It Is:
  • map() applies a function to all items in a list.
  • filter() creates a list of items that match a condition.
  • lambda creates short, one-line functions.
• Why It’s Important: These tools are great for quickly modifying or filtering data in a dataset.

Decorators

• What It Is: Decorators are functions that change the behavior of other functions.
• Why It’s Important: They help add new features to your code without changing the original code structure, like adding logging or checking for errors.

File Handling

• What It Is: File handling involves reading from and writing to files, such as open(), read(), and write().
• Why It’s Important: Many projects require importing data from files or saving results. Knowing how to handle files is crucial for these tasks.

The list is long…but these are very easy to grasp topics. You can learn these topics within a week if you plan your learning accurately.

Now, I have discussed the topics…it’s time to discuss the resources to learn Python Basics.

Resources to Learn Python Basics

• Python for Everybody – Coursera
• Introduction to Python Programming– Udacity (Free Course)
•  The Python Tutorial- (PYTHON.ORG)
• Python Tutorial- MLTUT
• Python Crash Course– Book
• Head First Python: A Brain-Friendly Guide– Book
• Introduction To Python Programming– Udemy