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Data Analysis
Data Analysis
Curriculum
10 Sections
40 Lessons
10 Weeks
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Module 1: Introduction to Data Analysis
3
1.0
Importance of Data Analysis
1.1
The Data Analysis Process
1.2
Role of Data Analysts
Module 2: Data Collection and Preparation
4
2.0
Data Sources and Acquisition
2.1
Data Importing and Loading
2.2
Data Cleaning and Preprocessing
2.3
Data Structuring and Transformation
Module 3: Exploratory Data Analysis (EDA)
5
3.0
Descriptive Statistics
3.1
Data Visualization
3.2
Data Exploration Techniques
3.3
Identifying Patterns and Trends
3.4
Detecting Anomalies
Module 4: Data Visualization
4
4.0
Principles of Data Visualization
4.1
Choosing the Right Chart or Graph
4.2
Data Visualization Tools and Libraries
4.3
Designing Effective Visualizations
Module 5: Statistical Analysis
5
5.0
Basic Statistical Concepts
5.1
Probability Distributions
5.2
Hypothesis Testing
5.3
Correlation and Regression Analysis
5.4
Inferential Statistics
Module 6: Introduction to Machine Learning
4
6.0
Machine Learning Fundamentals
6.1
Types of Machine Learning (Supervised, Unsupervised, etc.)
6.2
Machine Learning Algorithms Overview
6.3
Applying Machine Learning to Data Analysis
Module 7: Practical Applications
3
7.0
Real-World Data Analysis Projects
7.1
Case Studies from Various Domains (e.g., finance, healthcare, marketing)
7.2
Hands-On Data Analysis Exercises
Module 8: Data Ethics and Privacy
4
8.0
Ethical Considerations in Data Analysis
8.1
Privacy and Data Protection
8.2
Mitigating Bias in Data Analysis
8.3
Responsible Data Analysis Practices
Module 9: Tools and Software
4
9.0
Introduction to Data Analysis Tools (e.g., Python, R)
9.1
Data Visualization Libraries (e.g., Matplotlib, Seaborn)
9.2
Statistical Analysis Software
9.3
Practical Coding and Data Manipulation
Module 10: Final Project and Certification
4
10.0
Capstone Data Analysis Project
10.1
Presentation of Findings
10.2
Course Review and Evaluation
10.3
Certification of Completion
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