
Learn to extract insights from data using statistical and analytical techniques, and visualize findings using popular tools.
In this course, you will learn Introduction to Data Analysis, Data Preparation, Statistical Analysis, Data Visualization, Data Mining and Machine Learning, Working with Databases, Advance Data Analysis Techniques, Case Studies and Projects, Data Storytelling and Communication
Course Description: Learn to extract insights from data using statistical and analytical techniques, and visualize findings using popular tools.
Course Outline:
Module 1: Introduction to Data Analysis
1.1. Definition and importance of data analysis
1.2. Data types and sources
1.3. Data analysis process
Module 2: Data Preparation
2.1. Data cleaning and preprocessing
2.2. Data transformation and normalization
2.3. Data visualization for exploration
Module 3: Statistical Analysis
3.1. Descriptive statistics (mean, median, mode)
3.2. Inferential statistics (hypothesis testing, confidence intervals)
3.3. Regression analysis (simple and multiple)
Module 4: Data Visualization
4.1. Principles of effective visualization
4.2. Popular visualization tools (Tableau, Power BI, D3.js)
4.3. Visualization best practices
Module 5: Data Mining and Machine Learning
5.1. Introduction to data mining and machine learning
5.2. Supervised and unsupervised learning
5.3. Model evaluation and selection
Module 6: Working with Databases
6.1. Database fundamentals (SQL, NoSQL)
6.2. Data querying and manipulation
6.3. Data warehousing and ETL
Module 7: Advanced Data Analysis Techniques
7.1. Time series analysis
7.2. Forecasting and predictive modeling
7.3. Text analysis and sentiment analysis
Module 8: Case Studies and Projects
8.1. Real-world data analysis scenarios
8.2. Group project: Analyze and visualize a dataset
8.3. Presenting findings and insights
Module 9: Data Storytelling and Communication
9.1. Effective communication of insights
9.2. Data storytelling principles
9.3. Creating interactive dashboards
Module 10: Advanced Tools and Technologies
10.1. R and Python programming for data analysis
10.2. Cloud-based data platforms (AWS, Google Cloud)
10.3. Emerging trends in data analysis
Course Projects:
1. Analyze a dataset using statistical and visualization techniques
2. Create a data visualization dashboard
3. Develop a predictive model using machine learning
Course Resources:
1. Textbooks: "Data Analysis with Python" and "Visualize This"
3. Software: Excel, Tableau, Power BI, R, Python
Course Duration: 12 weeks (3 hours/week)
Target Audience: Business professionals, data analysts, and enthusiasts.
Prerequisites: Basic computer skills, familiarity with spreadsheets.
Certification: Data Analysis Certification upon completion.
Views: 8