Data Analysis with Python

Module 1: Introduction to Data Analysis (1 Week)

- Delve into the role of data analysis in modern decision-making.

- Explore various data types (structured, unstructured, quantitative, qualitative).

- Understand the data analysis process: data acquisition, cleaning, transformation, analysis, and visualization.

Module 2: Data Manipulation with Spreadsheets (2 Weeks)

- Leverage essential formulas and functions in Microsoft Excel for data cleaning, organization, and calculations.

- Master pivot tables and data analysis tools within Excel.

Module 3: Statistics for Data Analysis (3 Weeks)

- Grasp key statistical concepts like descriptive statistics (measures of central tendency and dispersion).

- Explore probability distributions and their applications in data analysis.

- Learn about hypothesis testing and drawing statistical inferences.

Module 4: Introduction to Programming for Data Analysis (4 Weeks)

- Learn the fundamentals of Python programming, a popular language for data analysis.

- Gain hands-on experience with data structures (lists, dictionaries) and basic programming constructs (loops, conditional statements).

Module 5: Data Wrangling with Python Libraries (2 Weeks)

- Explore popular Python libraries for data manipulation (pandas, NumPy).

- Learn techniques for data import, cleaning, transformation, and wrangling with Python.

Module 6: Handling Missing Values and Outliers with Python (1 Week)

- Understand different types of missing values and strategies for imputation using Python libraries.

- Explore methods for identifying and handling outliers in data with Python.

Module 7: Introduction to Relational Databases with MySQL (Optional) (1 Week)

- Understand the basics of relational databases and their role in data storage and management.

- Learn the fundamentals of querying data using the MySQL language (SQL).

Module 8: Introduction to Data Visualization (1 Week)

- Understand the importance of data visualization for effective communication of insights.

- Explore various data visualization techniques (bar charts, line charts, scatter plots, histograms, etc.).

- Learn design principles for creating clear and impactful data visualizations.

Module 9: Data Visualization with Python Libraries (1 Week)

- Gain hands-on experience with popular Python libraries for data visualization (Matplotlib, Seaborn).

- Create various data visualizations with Python and customize them for clarity and aesthetics.

Module 10: Data Storytelling with MS Power BI (2 Weeks)

- Explore Microsoft Power BI, a powerful tool for creating interactive data visualizations and reports.

- Learn how to connect to various data sources, build visualizations, and create compelling dashboards in Power BI.

Module 11: Exploratory Data Analysis (EDA) with Python (2 Weeks)

- Perform exploratory data analysis (EDA) to understand data characteristics and relationships using Python libraries.

- Identify patterns and trends in data through EDA techniques.

Module 12: Introduction to Machine Learning (2 Weeks)

- Understand the core concepts of machine learning and its applications in data analysis.

- Explore different types of machine learning algorithms (supervised, unsupervised learning).

- Learn the basic workflow of building and evaluating machine learning models with Python.

Module 13: Putting it All Together - Data Analysis Project (4 Weeks)

- Apply the acquired skills to a real-world data analysis project.