Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Principal Component Analysis (PCA) in beginner-friendly terms:

What is PCA?
PCA stands for Principal Component Analysis. It is a powerful technique used to analyze and simplify complex datasets.

How does it work?
PCA works by reducing the number of variables (or dimensions) in a dataset, while keeping as much information as possible. It transforms the data to a new coordinate system such that the greatest variance comes to lie on the first axis (called the first principal component), the second greatest variance on the second axis, and so on.

In simpler terms, PCA looks at all the variables (or features) in your data and picks out the important ones – the ones that best explain the differences between data points. It throws away less important variables to simplify the dataset.

Why use PCA?
There are a few key reasons to use PCA:

  1. Dimensionality Reduction: PCA helps reduce the number of variables in a dataset, which is useful for visualization and preventing overfitting.

  2. Simplification: It transforms complex datasets into a format that is easier to understand and interpret.

  3. Pattern Recognition: PCA helps identify patterns and groupings in datasets that were previously obscured.

  4. Data Compression: It can compress large datasets while retaining the important information and patterns.

How is it applied?
PCA is commonly used as a pre-processing step before applying machine learning algorithms. It is especially useful for visualizing high-dimensional data in 2D or 3D. PCA has applications in fields like image processing, recommendation systems, genomic analysis and more.

Overall, PCA provides a simple yet powerful way to analyze complex datasets and extract the most representative information from large amounts of data.

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