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Data Analysis with Python (Short Course)

courseCourse Title

Data Analysis with Python

courseCourse Duration

2 months


Rupak Koirala


courseCredit Hours


courseRegistration Form

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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


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.

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