Course content
1. Introduction to Data Analysis: An overview of the field of data analysis, its importance in decision-making, and the role of a data analyst.
2. Data Collection and Cleaning: Techniques for gathering and organizing data from various sources, data cleaning and preprocessing methods to ensure data quality.
3. Statistical Analysis: Introduction to statistical concepts and methods used in data analysis, including descriptive statistics, hypothesis testing, correlation, and regression analysis.
4. Data Visualization: Principles and techniques for creating effective visual representations of data using tools like Excel, Tableau, or Python libraries like Matplotlib and Seaborn.
5. Data Analysis Techniques: Application of various data analysis techniques such as exploratory data analysis, data mining, clustering, and classification.
6. Database Management: Introduction to database concepts, SQL (Structured Query Language) for data manipulation and retrieval from databases.
7. Programming for Data Analysis: Introduction to programming languages commonly used in data analysis, such as Python or R, and their application for data manipulation, analysis, and visualization.
8. Machine Learning: An introduction to machine learning algorithms and techniques used for predictive modeling and pattern recognition.
9. Data Ethics and Privacy: Understanding ethical considerations in data analysis, data privacy regulations, and ensuring data security.
10. Practical Projects and Case Studies: Hands-on projects and real-world case studies to apply data analysis techniques and solve practical problems.
Assessment
The assessment is done via submission of assignment. There are no written exams.