Data Analysis and Visualization in Hindi

Overview

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Dive into a comprehensive Hindi-language course on data analysis and visualization, focusing on powerful Python libraries like NumPy, Pandas, and Matplotlib. Learn to import, clean, manipulate, and understand data for statistical analysis, machine learning, and data presentation. Explore NumPy arrays, reshaping, indexing, slicing, and operations. Master Pandas for reading CSV files, selecting columns and rows, and analyzing real-world datasets like the World Happiness Report. Gain practical skills in data workflow, from importing messy data to preparing it for advanced analytics, all explained using real-life problems and datasets. Suitable for those with basic Python knowledge, this course serves as an up-to-date encyclopedia for data science methods and workflows.

Syllabus

Why Data Analysis and Visualization(Hindi).
Download and Install Anaconda(Hindi).
Jupyter Notebook Tutorial (Hindi).
NumPy Overview (Hindi).
Create NumPy Arrays (Hindi).
Create Arrays Using Numpy Functions(Hindi).
NumPy : Reshaping data(Hindi).
NumPy: -1 in reshape(Hindi).
NumPy : Seed Function (Hindi).
NumPy : Indexing And Slicing of 1-d array(Hindi).
Numpy : 2-d Matrix - Indexing and Slicing (Hindi).
Numpy : View Vs Copy(Hindi).
NumPy : Conditional Selection(Hindi).
Numpy: Operations on array with scaler(Hindi).
NumPy : operations on two or more array(Hindi).
Some more useful numpy functions(Hindi).
NumPy Axis (Hindi).
NumPy : shuffle and unique (Hindi).
NumPy : Horizontal and Vertical Stacking(Hindi).
NumPy Exercise (Hindi).
NumPy : Exercise Solution (Hindi).
Numpy vs python lists [Hindi].
Pandas Overview(Hindi).
Read CSV Files using pandas.
Pandas : Selecting Columns(Hindi).
Pandas : Selecting rows using square brackets.
Pandas : iloc (Hindi).
Pandas : Loc.
Analyse World Happiness Report Pandas Exercise.
[Solution]Analyse World Happiness Report Solution.
Pandas Series Part 1.
Pandas Series [2].
Different Ways to Create Pandas Series.
Pandas : Sort Series.
Series to list.
Pandas Series : nsmalest nlargest idxmin idxmax.
Pandas : Index Object.
Pandas : Filter Data.
Pandas: Filter Data Part 2.
pd.DataFrame.