Entry Requirement: 

• Willingness to invest a minimum of 15 hours per week 
• Programming Basics in QBasic/C/C++ or similar is preferable
• No particular education requirement
• The selection of the candidate is subject to a comprehensive test to analyze their knowledge level

Course Syllabus

Python

1: Introduction to Python

• Introduction to programming and Python
• Setting up Python environment (IDEs, text editors, online interpreters)
• Writing and running your first Python program
• Variables, data types, and basic operations
• Comments and code structure

2: Control Structures

• Conditional statements (if, else, elif)
• Comparison operators and boolean logic
• Loops (for and while)
• Break and continue statements
• Practical exercises and mini-projects

3: Functions and Modules

• Defining and calling functions
• Parameters and return values
• Scope and lifetime of variables
• Introduction to built-in functions and modules
• Creating and importing custom modules

4: Data Structures

• Lists, tuples, and sets
• Accessing and manipulating elements
• List comprehensions
• Dictionaries and key-value pairs
• Practical exercises involving data structures

5: Exception Handling in Python

• Understanding exceptions in Python
• Importance of exception handling
• Types of exceptions and their meaning
• try-except blocks to handle exceptions
• Catching specific exceptions
• Handling multiple exceptions
• Using the try-except-else-finally blocks
• Raising Exceptions

6: File Handling

• Reading from and writing to files
• Working with text and binary files
• Exception handling (try, except, finally)
• CSV and JSON file formats
• Practical file processing examples

7: Object-Oriented Programming (OOP)

• Introduction to OOP concepts
• Classes and objects
• Attributes and methods
• Encapsulation, inheritance, and polymorphism
• Creating and using classes in Python

8: Debugging and Testing

• Debugging techniques and tools
• Common errors and how to fix them
• Writing and running unit tests
• Using assertions for testing
• Best practices for writing maintainable code

Data Analysis 

1.Introduction to Data Analysis with Python

• Overview of data analysis and its importance
• Introduction to key libraries: NumPy, pandas, and matplotlib
• Loading and exploring datasets using pandas
• Basic data exploration and summary statistics

2. Numpy 

•  Introduction to NumPy and Data Science
• Array Manipulation and Indexing
• Universal Functions (ufuncs)
• Working with Multi-dimensional Data
• Linear Algebra with NumPy

3. Pandas 

• Overview of pandas and its role in data manipulation and analysis
• Installation and setup of pandas
• Introduction to Series and DataFrame data structures
• Loading data into pandas from various sources (CSV, Excel, SQL, etc.)
• Data Wrangling with pandas
• Exploratory Data Analysis (EDA) with pandas
• Data Transformation and Feature Engineering
• Data Aggregation and Pivot Tables

4. Data Cleaning and Preprocessing

• Handling missing data
• Dealing with duplicate records
• Data type conversions
• Removing outliers and anomalies
• Data normalization and scaling

5. Data Visualization

• Creating basic plots using Matplotlib
• Customizing plots for better understanding
• Introduction to Seaborn for advanced visualization
• Plotly for interactive Visualizations
• Creating bar plots, histograms, scatter plots, and more
• Visualizing relationships and correlations in data

6. Exploratory Data Analysis (EDA)

• Hypothesis generation and testing
• Grouping and aggregating data
• Pivot tables and cross-tabulations
• Analyzing categorical and numerical data
• EDA case studies

7.  Introduction to Statistical Analysis

• Descriptive statistics and probability distributions
• Sampling and confidence intervals
• Hypothesis testing (t-tests, chi-square, ANOVA)
• Correlation and regression analysis
• Applying statistical concepts in Python

8. Final Capstone Project

• Students select a real-world dataset for analysis
• Applying data cleaning, visualization, and statistical analysis techniques
• Formulating insights and drawing conclusions from the data
• Presenting the project and findings

Course Learning Outcomes

The students will be able to:

• Learn data manipulation, cleaning, and preprocessing techniques using Python tools.
• Master exploratory data analysis and create informative visualizations.
• Understand statistical concepts, hypothesis testing, and basic machine learning.
• Develop skills to present insights and results to both technical and non-technical audiences.
• Apply Python for data analysis, solve practical problems, and consider ethical considerations.

Career Opportunities

• Data Analyst
• Business Analyst
• Financial Analyst
• Healthcare Data Analyst
• Social Media Analyst
• Data Journalist
• Data Visualization Specialist
• Junior Data Scientist and so on.