Master In Data Analysis-Numpy, Pandas, Visualuze & Streamlit

python, data structures, data analysis, Numpy, Pandas, matplotlib, Streamlit, plotly, dashboard, realtime problems

What you'll learn

Handle numerical data efficiently with NumPy

Clean, organize, and analyze datasets using Pandas

Create stunning visualizations with Matplotlib & Seaborn

Build interactive dashboards with Streamlit

Understand core statistical concepts used in data science

Apply techniques to real-world datasets and examples

Requirements

Basic Python

basics of statistics

Description

In today’s world, data is the new oil, and the ability to analyze and interpret it is one of the most in-demand skills across industries. Businesses, governments, and researchers depend on data to make smarter decisions, uncover patterns, and solve real-world problems. Yet, raw data is often messy and meaningless without the right tools.This course equips you with the essential Python libraries for data analysis—NumPy, Pandas, and Matplotlib, seaborn and Plotly to clean, process, and visualize data with confidence. NumPy powers numerical operations, Pandas simplifies handling complex datasets, and Matplotlib helps you create compelling visualizations to tell stories with data. Together, they form the foundation of any data analyst or data scientist’s toolkit.What makes this course even more powerful is the addition of Streamlit, a modern tool that allows you to transform your analysis into interactive, shareable dashboards. Instead of static reports, you’ll learn how to build dynamic apps that bring your insights to life.Whether you’re a student exploring data careers, a beginner in programming, or a professional looking to upgrade your skills, this course gives you the practical knowledge and real-world projects needed to stand out in today’s data-driven job market.Data science success starts with mastering the tools that help you explore, transform, and visualize data. This course bridges theory with practice, taking you from the foundations of data analysis all the way to building your own interactive dashboards and preparing datasets for machine learning.

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Course content and roadmap

Lecture 3 Prerequisites

Section 2: Software Installations

Lecture 4 Python Installation

Lecture 5 Jupyter Notebook Installation (Anaconda)

Lecture 6 How to use Jupyter Notebook -Part1

Lecture 7 How to use Jupyter Notebook -Part2

Lecture 8 Install PyCharm community for Streamlit application development

Section 3: NumPy Arrays

Lecture 9 Why we need arrays, dataframes and tensors

Lecture 10 NumPy Introduction

Lecture 11 Arrays Properties

Lecture 12 array creation

Lecture 13 List Vs Arrays

Lecture 14 Vectors vs Matrics Vs Tensors

Lecture 15 reshape and resizing arrays

Lecture 16 Array Initializations - zeros, ones, and full methods

Lecture 17 arange method

Section 4: Pandas - Part1- Dataframes

Lecture 18 Pandas - Introduction

Lecture 19 Pandas Documentation

Lecture 20 Series data Vs DataFrame

Lecture 21 Create dataframes with data

Lecture 22 Export of Dataframes

Lecture 23 labelling the columns and indexes in dataframes

Section 5: Pandas -Part2 -Data loading from external sources

Lecture 24 Create dataframes from csv files

Lecture 25 dataframes - head, tail method

Lecture 26 Dataframes - columns , index, information

Lecture 27 row indexing and slicing

Lecture 28 Create dataframes from excel files

Lecture 29 Create dataframes from data servers (Optional)

Section 6: Pandas - Part3

Lecture 30 Column indexing and Column Slicing

Lecture 31 Duplicated rows/columns

Lecture 32 Dataframe filtering - single condition

Lecture 33 Filtering with filter method by columns names

Lecture 34 Dataframe filtering - multiple conditions

Section 7: Pandas - Part4- Operations

Lecture 35 Datatype conversions - float to int

Lecture 36 Working with Datetime column

Lecture 37 Dataframe working with categorical data - Unique and Value counts

Lecture 38 Value counts - index and values

Lecture 39 Insert extra column/s into dataframe

Lecture 40 Insert extra row/s into dataframe

Lecture 41 Remove (drop) unnecessary column/columns

Lecture 42 Export dataframes to excel and csv

Section 8: Pandas -Part5- Advance Operations

Lecture 43 Section - Intro

Lecture 44 Overview of Encoding Techniques

Lecture 45 Encoding data - map function

Lecture 46 Encoding Data - Label Encoding (Algorithmic based approach)

Lecture 47 pd.get_dummies (One Hot Encoding)

Lecture 48 Label Encoding Vs OneHotEncoding

Lecture 49 Data Bucketing - Binning process

Section 9: Pandas -Part6 - Advance Operations

Lecture 50 Section -Intro

Lecture 51 Data sorting - single level sorting

Lecture 52 Data sorting - Multilevel sorting

Lecture 53 Groupby - Signle level Grouping

Lecture 54 Multi-Level Grouping

Lecture 55 Pivot dataframes

Lecture 56 Dataframe - joins , merge, concate

Lecture 57 Dataframe - concatinations

Lecture 58 center join, left join and right join

Section 10: Pandas -Part7- Missing values and Outliers (IQR method)

Lecture 59 Section Intro

Lecture 60 Missing values - Theory

Lecture 61 impact of the missing values and options - Theory

Lecture 62 Treating of missing values - Imputation Techniques - Theory

Lecture 63 Treating of missing values - Coding part

Lecture 64 Outliers or anomalies

Lecture 65 Quantification of Outliers through IQR (Inter Quartile Method) - Theory

Lecture 66 Quantification of Outliers through IQR (Inter Quartile Method) - Coding

Lecture 67 Treating or imputing outliers

Section 11: Matplotlib- Data Visualizations

Lecture 68 Section - Intro

Lecture 69 install matplotlib, seaborn and Plotly libraries

Lecture 70 Plotting, labels, label sizes, plot the data (x.y)

Lecture 71 multiple plots in same graph

Lecture 72 plt.show() - functionality and saving plots to external memory

Lecture 73 bar garphs

Lecture 74 pie charts

Lecture 75 pie charts from dataframes

Lecture 76 scatter plots

Lecture 77 box plots

Lecture 78 histograms

Lecture 79 Seaborn - Distribution Plots

Lecture 80 Seaborn - Pairplots

Lecture 81 Seaborn - Heatmap

Lecture 82 Plotly - 3D Graphs

Lecture 83 Plotly - interactive visualizations - Scatter Plots

Lecture 84 Plotly - interactive visualizations - Tree maps

Lecture 85 Plotly - interactive visualizations - sunburst charts

Section 12: Project - Building interactive Dashboard from the scratch - Python Coding

Lecture 86 Section - Intro

Lecture 87 Streamlit - Exploration

Lecture 88 Project Design & requirements

Lecture 89 Project setup and environment setup

Lecture 90 first app running

Lecture 91 app_development.py

Beginners who want to start a career in data science or machine learning,Analysts looking to upskill in Python-based data handling and visualization,Anyone eager to create insightful reports and dashboards without overwhelming complexity,Who are interested to build webapps with data analytics,Students and professionals who want practical, applied knowledge with real-world datasets