Data Analysis Using Python

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Level

All Levels
Total Enrolled

5
Duration

5 hours 10 minutes
Last Updated

December 16, 2024
Certificate

Certificate of completion

Level

All Levels
Total Enrolled

5
Duration

5 hours 10 minutes
Last Updated

December 16, 2024
Certificate

Certificate of completion

Course content:

Module 1: Introduction to Data Analysis

What is Data Analysis

04:05
The Importance of Data in Decision-Making

01:50
Distinguishing Between Quantitative and Qualitative Data.

02:01
Data Collection Methods

01:25
Data Sources

01:13
The Data Analysis Process From Collection to Insight

00:51
Data Cleaning and Preprocessing

01:03
The Role of a Data Analyst

03:40
Common Tools Used in Data Analysis

01:26
Data Analysis Life Cycle

02:12
Visualization in Data Analysis

01:04
Data Ethics

01:17
Introduction to Data Models

02:37
Challenges in Data Analysis:

00:42
Real-World Applications of Data Analysis

00:35
Data Analysis Quiz

Module 2: Introduction to Python Programming

Introduction to Python Programming

01:17
IDE

01:21
Anaconda

00:40
Install anaconda

04:25
Jupyter notebook

02:38
Numbers

03:47
Print Function

02:05
Variables

08:19
Strings

08:55
Strings Methods

11:23
Data Structures

00:31
lists

07:23
Dictionaries

02:57
Tuples

01:02
Sets

01:22
Booleans

01:03
Comparisons Operators

02:17
Logical Operators

01:36
Conditional statement If, Else, Elif Statements

03:45
Loops for loop and while loop

06:29
Functions

03:11
Lambda

01:33
Map

03:21
Python Programming quize

Module 3: Statistics for Data Analysis

Statistics

00:41
Structured Data

01:19
Types of Structured Data

02:04
A Typical data frame format

00:30
Quartiles

08:03
Estimates of Location

03:44
Estimates of Variability

08:54
Outliers

02:13
Correlation

03:57
Data and Sampling Distributions

02:35
Skewness

02:14
Normal Distribution

01:05
Statistics quiz

Module 4 : Data Analysis Using Python

Introduction to pandas

00:49
Resources Files

00:09
Data Frame

02:29
Create DataFrame

01:41
Working with columns

04:45
Working with Rows

03:58
Subsets

01:25
Working with files

01:22
Method1

04:23
Method2

06:45
Method3

00:51
Summarize data

00:00
View the first few rows of a DataFrame

01:47
View the last few rows of a DataFrame

00:46
Get summary of the DataFrame

00:57
Generate summary statistics for numerical columns

00:50
Get the number of rows and columns in a DataFrame

00:46
View the column names of the DataFrame

00:17
Access the index of the DataFrame

00:29
Check the data types of each column

00:24
Check for missing values in the DataFrame

01:12
Remove rows with missing values

01:21
Rename columns or rows in the DataFrame

00:56
Sort the DataFrame

03:10
Sample method

01:09
Group data by one or more columns to perform aggregation

05:57
Merge two DataFrames

03:35
Create a pivot table for summarizing data

02:02
file format

03:55
CSV file

03:23
Excel file

01:48
Json file

02:25
Parquet file

01:21
XML file

00:39
Data Quality

00:00
Handling Missing Data

08:07
Removing Duplicates

02:12
Data Consistency

01:34
Data Transformation and Normalization

03:51
Validating Data Types

02:07
Fixing Data Entry Errors

02:32
Consistency in Categorical Data

01:13
Standardizing Data Formats

02:20
Data Type Conversion

01:00
Data Validation and Verification

01:09
Handling Categorical Variables

02:00
Removing Outliers

07:42
Next

00:49
Project 1 : Customer Transaction Analysis in Banking

22:48
Project 2 : Payroll Analysis

16:01
Project 3 : Customer Purchase Analysis

23:13
Pandas quiz

Data Analysis Fundamentals: How to collect, clean, and analyze data, differentiate between data types, and use tools like Python for decision-making and insights. You will also understand the role of data analysts and the data analysis lifecycle.
Python Programming: Basic Python programming concepts, including variables, data structures (lists, dictionaries, etc.), control flow, loops, and functions, which are essential for analyzing data.
Statistics: Key statistical concepts, including measures of central tendency, variability, distributions, and outliers, to help analyze and interpret data.
pandas Library: How to use pandas to manipulate and clean data, work with DataFrames, handle missing data, and apply data transformations, which are crucial for effective data analysis.

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Instructor:

Eng Mohammed

Big Data Engineer and Data Consultant @ ISD Company

Data Analysis Using Python

Course content:

Module 1: Introduction to Data Analysis

What is Data Analysis

The Importance of Data in Decision-Making

Distinguishing Between Quantitative and Qualitative Data.

Data Collection Methods

Data Sources

The Data Analysis Process From Collection to Insight

Data Cleaning and Preprocessing

The Role of a Data Analyst

Common Tools Used in Data Analysis

Data Analysis Life Cycle

Visualization in Data Analysis

Data Ethics

Introduction to Data Models

Challenges in Data Analysis:

Real-World Applications of Data Analysis

Data Analysis Quiz

Module 2: Introduction to Python Programming

Introduction to Python Programming

IDE

Anaconda

Install anaconda

Jupyter notebook

Numbers

Print Function

Variables

Strings

Strings Methods

Data Structures

lists

Dictionaries

Tuples

Sets

Booleans

Comparisons Operators

Logical Operators

Conditional statement If, Else, Elif Statements

Loops for loop and while loop

Functions

Lambda

Map

Python Programming quize

Module 3: Statistics for Data Analysis

Statistics

Structured Data

Types of Structured Data

A Typical data frame format

Quartiles

Estimates of Location

Estimates of Variability

Outliers

Correlation

Data and Sampling Distributions

Skewness

Normal Distribution

Statistics quiz

Module 4 : Data Analysis Using Python

Introduction to pandas

Resources Files

Data Frame

Create DataFrame

Working with columns

Working with Rows

Subsets

Working with files

Method1

Method2

Method3

Summarize data

View the first few rows of a DataFrame

View the last few rows of a DataFrame

Get summary of the DataFrame

Generate summary statistics for numerical columns

Get the number of rows and columns in a DataFrame

View the column names of the DataFrame

Access the index of the DataFrame

Check the data types of each column

Check for missing values in the DataFrame