Data Analysis with Python

Course Content

Introduction to Data Analysis and Python Basics

What is Data Analysis?

00:00
Importance of Python in the Data Field

00:00
Career paths in the data field

00:00
Setting up Python Environment (Google Colab)

00:00
Python Basics: Data types, variables, loops, conditionals, functions

00:00
Introduction to IDEs and essential Python packages

00:00
Write Python scripts for basic arithmetic and logical operations
Create a program to calculate basic statistics (mean, median, mode)

Python Data Structures and Libraries

Lists, tuples, sets, and dictionaries

00:00
Iterating over and modifying collections

00:00
Working with Regexp, datetime

00:00
Introduction to Numpy

00:00
Arrays, indexing, and slicing

00:00
Array operations: addition, multiplication, and dot products

00:00
Introduction to Pandas

00:00
Introduction to Series and DataFrames

00:00
Reading and writing CSV, Excel, and JSON files

00:00
Web scraping

00:00
Load and explore a sample CSV dataset
Use NumPy to perform basic matrix operations

Data Cleaning and Preprocessing

Exploratory Data Analysis (EDA)

Descriptive Statistics

00:00
Summary statistics: mean, median, mode, variance, etc

00:00
Distribution analysis: histograms and box plots

00:00
Correlation and Relationships

00:00
Correlation matrices

00:00
Scatter plots and pair plots

00:00
Introduction to Visualization Libraries

00:00
Using Matplotlib and Seaborn for EDA

00:00
Analyze a dataset (e.g., weather data) to calculate descriptive statistics

Advanced Data Manipulation

Advanced Data Visualization and Reporting

Advanced Visualizations with Seaborn

00:00
Advanced plot types: violin plots, facet grids, and heatmaps

00:00
Interactive Visualizations with Plotly

00:00
Creating interactive line charts, bar charts, and scatter plots

00:00
Reporting and Dashboards

00:00
Introduction to Dash for creating interactive dashboards

00:00
Visualize survey data using advanced plots
Create an interactive dashboard for a business KPI dataset
Prepare a simple data story using Python visualizations

Introduction to Advanced Analytical Techniques

Capstone Projects

Student Ratings & Reviews

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What Will You Learn?

Course Content

Introduction to Data Analysis and Python Basics

What is Data Analysis?

Importance of Python in the Data Field

Career paths in the data field

Setting up Python Environment (Google Colab)

Python Basics: Data types, variables, loops, conditionals, functions

Introduction to IDEs and essential Python packages

Write Python scripts for basic arithmetic and logical operations

Create a program to calculate basic statistics (mean, median, mode)

Python Data Structures and Libraries

Lists, tuples, sets, and dictionaries

Iterating over and modifying collections

Working with Regexp, datetime

Introduction to Numpy

Arrays, indexing, and slicing

Array operations: addition, multiplication, and dot products

Introduction to Pandas

Introduction to Series and DataFrames

Reading and writing CSV, Excel, and JSON files

Web scraping

Load and explore a sample CSV dataset

Use NumPy to perform basic matrix operations

Data Cleaning and Preprocessing

What is Data Cleaning?

Identifying and handling missing values

Removing duplicates and outliers

Data Pre-Processing

Introduction to Data Pipelines

Clean a dataset with missing and inconsistent values (e.g., sales data)

Encode categorical variables in a sample dataset

Exploratory Data Analysis (EDA)

Descriptive Statistics

Summary statistics: mean, median, mode, variance, etc

Distribution analysis: histograms and box plots

Correlation and Relationships

Correlation matrices

Scatter plots and pair plots

Introduction to Visualization Libraries

Using Matplotlib and Seaborn for EDA

Analyze a dataset (e.g., weather data) to calculate descriptive statistics

Advanced Data Manipulation

Grouping and Aggregation

Aggregating data: sum, mean, custom functions

Working with Time-Series Data

Parsing dates and timestamps

Resampling and rolling windows

Merging and Joining Data

Combining multiple datasets

Aggregate sales data by region and time period

Advanced Data Visualization and Reporting

Advanced Visualizations with Seaborn

Advanced plot types: violin plots, facet grids, and heatmaps

Interactive Visualizations with Plotly

Creating interactive line charts, bar charts, and scatter plots

Reporting and Dashboards

Introduction to Dash for creating interactive dashboards

Visualize survey data using advanced plots

Create an interactive dashboard for a business KPI dataset

Prepare a simple data story using Python visualizations

Introduction to Advanced Analytical Techniques

Time Series Analysis

Decomposing time-series data into trend, seasonality, and noise

Working with time-series data in Pandas

Forecasting basics using moving averages and exponential smoothing

Introduction to handling large datasets using Python

Techniques to optimize Pandas operations for large-scale data

Capstone Projects

Student Ratings & Reviews

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