Program AI 1 – Data Analysis and Data Science - Gain proficiency in Python for data analysis with practical applications

ADVANCEDai

4.8(42 reviews)

ADVANCEDai

Program AI 1 – Data Analysis and Data Science

Name: Program AI 1 – Data Analysis and Data Science
Rating: 4.8 (42 reviews)

Gain proficiency in Python for data analysis with practical applications

4.8(42 reviews)

Duration

24 lessons

Class Size

Max 8 students

Learning Mode

hybrid

6 modules•24 topics•24 lessons•24 total hours

About This Course

Learn data analysis techniques using Python, focusing on libraries like NumPy, Matplotlib, and Pandas to derive insights from real-world datasets.

Key Learning Outcomes

Data Analysis
Apply data analysis techniques to real-world problems
Python Libraries
Use Python libraries for data analysis

Course Modules

Course Curriculum

A comprehensive learning path structured in modules

6 modules

24 total topics

24 lessons

1
Python Fundamentals for Data Analysis

Learning Python basics and programming concepts essential for data science

4 lessons

Collapse module

1.1Python & Google Colab Introduction

Setting up Google Colab and writing first Python code for data analysis

1.2Variables, Data Types & Operators

Working with strings, numbers, and basic mathematical operations

1.3Lists & Conditional Logic

Creating data collections and implementing decision-making logic

1.4Loops & Iteration

Automating repetitive tasks with for and while loops

2
Advanced Python Structures

Mastering dictionaries, functions, and packages for efficient data handling

4 lessons

Expand module

2.1Dictionary Data Structures

Organizing data with key-value pairs and nested structures

2.2Functions & Reusable Code

Writing modular code with parameters and return values

2.3Python Packages & Libraries

Extending functionality with standard and external libraries

2.4Ice Cream Shop Simulation Project

Building a complete Python program integrating all learned concepts

3
Statistical Analysis with NumPy

Analyzing numerical data and creating visualizations with Python libraries

4 lessons

Expand module

3.1NumPy Arrays & Statistics

Performing efficient calculations on numerical data with NumPy

3.2Data Visualization with Matplotlib

Creating line, bar, and scatter plots to represent data graphically

3.3Exam Results Analysis Project

Applying statistical methods to analyze educational data and identify patterns

3.4Mid-Program Assessment

Testing understanding of Python fundamentals and data analysis basics

4
Data Processing with Pandas

Managing and transforming tabular data with the pandas library

4 lessons

Expand module

4.1Pandas DataFrames

Creating and manipulating tabular data structures with pandas

4.2CSV Data Import & Cleaning

Loading external data and handling missing or inconsistent values

4.3Data Transformation Techniques

Filtering, sorting, and applying functions to transform datasets

4.4Advanced Data Operations

Grouping, joining, and aggregating data for comprehensive analysis

5
Data Analysis Projects

Applying analysis techniques to create actionable insights from real datasets

4 lessons

Expand module

5.1Data Interpretation Methods

Translating numerical findings into meaningful business insights

5.2Google Play Store Analysis I

Analyzing app store data to identify trends and patterns in the code phase

5.3Google Play Store Analysis II

Creating professional visualization reports from the analysis findings

5.4Data Analysis Assessment

Demonstrating proficiency in comprehensive data analysis techniques

6
Capstone Project

Developing a comprehensive data project from planning to presentation

4 lessons

Expand module

6.1Project Planning & Dataset Selection

Defining project goals and choosing appropriate datasets

6.2Data Collection & Initial Analysis

Gathering, cleaning, and performing exploratory analysis

6.3Visualization & Insight Generation

Creating compelling visualizations and extracting meaningful insights

6.4Final Presentation

Presenting findings with professional data storytelling techniques

Key Benefits

Data analysis expertise

Students will gain proficiency in data analysis using Python

Visualization skills

Students will learn to visualize data using Python libraries

Real-world experience

Students will work with actual datasets and problems

Course Details

Information about this course

Duration

24 lessons

Session Length

1 hour

Learning Mode

HYBRID

Age Group

13 years old and above

Course Details

Information about this course

Duration

24 lessons over 6 months

Session Length

1 hour per session

Course Content

6 modules, 24 topics, 24 lessons

Total Hours

24 hours of instruction

Meeting Frequency

Weekly mentor check-ins via Zoom

Class Size

1:8

Age Group

13 years old and above

Program AI 1 – Data Analysis and Data Science

About This Course

Key Learning Outcomes

Course Modules

Course Curriculum

1Python Fundamentals for Data Analysis

1.1Python & Google Colab Introduction

1.2Variables, Data Types & Operators

1.3Lists & Conditional Logic

1.4Loops & Iteration

2Advanced Python Structures

2.1Dictionary Data Structures

2.2Functions & Reusable Code

2.3Python Packages & Libraries

2.4Ice Cream Shop Simulation Project

3Statistical Analysis with NumPy

3.1NumPy Arrays & Statistics

3.2Data Visualization with Matplotlib

3.3Exam Results Analysis Project

3.4Mid-Program Assessment

4Data Processing with Pandas

4.1Pandas DataFrames

4.2CSV Data Import & Cleaning

4.3Data Transformation Techniques

4.4Advanced Data Operations

5Data Analysis Projects

5.1Data Interpretation Methods

5.2Google Play Store Analysis I

5.3Google Play Store Analysis II

5.4Data Analysis Assessment

6Capstone Project

6.1Project Planning & Dataset Selection

6.2Data Collection & Initial Analysis

6.3Visualization & Insight Generation

6.4Final Presentation

Key Benefits

Data analysis expertise

Visualization skills

Real-world experience

Course Details

Course Details

Course Curriculum

1Python Fundamentals for Data Analysis

1.1Python & Google Colab Introduction

1.2Variables, Data Types & Operators

1.3Lists & Conditional Logic

1.4Loops & Iteration

2Advanced Python Structures

2.1Dictionary Data Structures

2.2Functions & Reusable Code

2.3Python Packages & Libraries

2.4Ice Cream Shop Simulation Project

3Statistical Analysis with NumPy

3.1NumPy Arrays & Statistics

3.2Data Visualization with Matplotlib

3.3Exam Results Analysis Project

3.4Mid-Program Assessment

4Data Processing with Pandas

4.1Pandas DataFrames

4.2CSV Data Import & Cleaning

4.3Data Transformation Techniques

4.4Advanced Data Operations

5Data Analysis Projects

5.1Data Interpretation Methods

5.2Google Play Store Analysis I

5.3Google Play Store Analysis II

5.4Data Analysis Assessment

6Capstone Project

6.1Project Planning & Dataset Selection

6.2Data Collection & Initial Analysis

6.3Visualization & Insight Generation

6.4Final Presentation

1
Python Fundamentals for Data Analysis

2
Advanced Python Structures

3
Statistical Analysis with NumPy

4
Data Processing with Pandas

5
Data Analysis Projects

6
Capstone Project

1
Python Fundamentals for Data Analysis

2
Advanced Python Structures

3
Statistical Analysis with NumPy

4
Data Processing with Pandas

5
Data Analysis Projects

6
Capstone Project