Digital Marketing Mastery

Lessons in this course

Lessons 1: Introduction to Data Science

What is Data Science?
Applications of Data Science in Various Industries
Overview of the Data Science Workflow
Essential Tools and Technologies (Python, Jupyter Notebooks, Excel)

Lessons 2: Data Collection and Preparation

Understanding Data Sources
Data Cleaning: Identifying and Handling Missing or Inaccurate Data
Data Formatting and Transformation Techniques
Introduction to Pandas for Data Manipulation

Lessons 3: Exploratory Data Analysis (EDA)

The Importance of EDA in Data Science
Descriptive Statistics: Mean, Median, Mode, and Standard Deviation
Using Pandas for Summarizing and Filtering Data
Data Visualization with Matplotlib and Seaborn

Lessons 4: Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised vs. Unsupervised Learning
Introduction to Algorithms (Linear Regression, K-Means Clustering)
Building Your First Machine Learning Model

Lessons 5: Data Visualization and Communication

The Art of Storytelling with Data
Creating Effective Charts, Graphs, and Dashboards
Tools and Libraries for Visualization (Matplotlib, Seaborn, and Plotly)

Lessons 6: Capstone Project

End-to-End Project: Solve a Real-World Data Problem
Dataset Exploration, Cleaning, and Analysis
Presenting Your Findings: Visualizations and Summary

Lessons 1: Introduction to Data Science

What is Data Science?
Applications of Data Science in Various Industries
Overview of the Data Science Workflow
Essential Tools and Technologies (Python, Jupyter Notebooks, Excel)

Lessons 2: Data Collection and Preparation

Understanding Data Sources
Data Cleaning: Identifying and Handling Missing or Inaccurate Data
Data Formatting and Transformation Techniques
Introduction to Pandas for Data Manipulation

Lessons 3: Exploratory Data Analysis (EDA)

The Importance of EDA in Data Science
Descriptive Statistics: Mean, Median, Mode, and Standard Deviation
Using Pandas for Summarizing and Filtering Data
Data Visualization with Matplotlib and Seaborn

Lessons 4: Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised vs. Unsupervised Learning
Introduction to Algorithms (Linear Regression, K-Means Clustering)
Building Your First Machine Learning Model

Lessons 5: Data Visualization and Communication

The Art of Storytelling with Data
Creating Effective Charts, Graphs, and Dashboards
Tools and Libraries for Visualization (Matplotlib, Seaborn, and Plotly)

Lessons 6: Capstone Project

End-to-End Project: Solve a Real-World Data Problem
Dataset Exploration, Cleaning, and Analysis
Presenting Your Findings: Visualizations and Summary

Lessons 1: Introduction to Data Science

What is Data Science?
Applications of Data Science in Various Industries
Overview of the Data Science Workflow
Essential Tools and Technologies (Python, Jupyter Notebooks, Excel)

Lessons 2: Data Collection and Preparation

Understanding Data Sources
Data Cleaning: Identifying and Handling Missing or Inaccurate Data
Data Formatting and Transformation Techniques
Introduction to Pandas for Data Manipulation

Lessons 3: Exploratory Data Analysis (EDA)

The Importance of EDA in Data Science
Descriptive Statistics: Mean, Median, Mode, and Standard Deviation
Using Pandas for Summarizing and Filtering Data
Data Visualization with Matplotlib and Seaborn

Lessons 4: Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised vs. Unsupervised Learning
Introduction to Algorithms (Linear Regression, K-Means Clustering)
Building Your First Machine Learning Model

Lessons 5: Data Visualization and Communication

The Art of Storytelling with Data
Creating Effective Charts, Graphs, and Dashboards
Tools and Libraries for Visualization (Matplotlib, Seaborn, and Plotly)

Lessons 6: Capstone Project

End-to-End Project: Solve a Real-World Data Problem
Dataset Exploration, Cleaning, and Analysis
Presenting Your Findings: Visualizations and Summary

Course overview

Unlock the power of data with this beginner-friendly course designed to introduce you to the fundamentals of data science. Whether you're an aspiring data scientist, a professional looking to enhance your skills, or simply curious about the field, this course will equip you with the tools and knowledge to begin your journey.