ANU UG/Degree 2nd Sem Python For Data Science Material

 ANU UG/Degree 2nd Sem Python For Data Science Material: Acharya Nagarjuna University Python for data science material for B.Sc Artificial Intelligence. 

Python's Dominance in Data Science:

  • Readability and Simplicity: Python's clear syntax and intuitive structure make it easy to learn and use, even for those without extensive coding experience. This allows data scientists to focus on problem-solving rather than language complexities.
  • Extensive Libraries and Ecosystem: Python boasts a rich collection of libraries specifically tailored for data science tasks, covering data manipulation, analysis, visualization, machine learning, and more. These include:
    • NumPy: Foundational library for numerical computing, providing powerful N-dimensional array objects.
    • Pandas: High-performance data structures and analysis tools for working with tabular data.
    • Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
    • SciPy: Collection of algorithms and mathematical functions for scientific computing.
    • Scikit-learn: Versatile machine learning library with a wide range of algorithms for classification, regression, clustering, and more.
  • General-Purpose Versatility: Python's capabilities extend beyond data science, making it useful for web development, automation, scripting, and other tasks. This empowers data scientists to handle diverse projects and create end-to-end solutions.
  • Active Community and Support: Python enjoys a large and active community of developers and data scientists, fostering extensive online resources, tutorials, documentation, and support forums.

Key Steps for Learning Python for Data Science:

  1. Master Python Fundamentals:
    • Variables, data types, operators, control flow (if statements, loops)
    • Functions, modules, object-oriented programming concepts
  2. Grasp Core Data Science Libraries:
    • NumPy: Array manipulation, linear algebra, random number generation
    • Pandas: Data loading, cleaning, wrangling, transformation
    • Matplotlib: Creating various visualizations (plots, charts, graphs)
  3. Explore Machine Learning Libraries:
    • Scikit-learn: Implementing machine learning algorithms
    • TensorFlow or PyTorch: For deep learning (optional, depending on interests)
  4. Practice Through Hands-on Projects:
    • Engage in real-world datasets and problem-solving to solidify skills and understanding.

UNIT-1: Basics of Python

  1. Features of python
  2. literal constants-numbers
  3. variables
  4. identifiers
  5. data types
  6. input operation
  8. operators
  9. operations on strings
  10. other data types
  11. type conversion
  12. Selection or conditional branching statements
  13. loops or iterative statements
  14. break, continue, pass, else statement with loops
  15. nested loops

UNIT-2: Functions and Modules


  1. Definition and call
  2. return statements
  3. anonymous function- LAMBDA
  4. recursive functions. 


  1. Using existing modules
  2. making own modules
  3. packages in python
  4. Names of standard library modules

UNIT-3: Data Structures


  1. Accessing lists
  2. updating lists
  3. nested lists
  4. basic list operations
  5. list methods
  6. loops in lists.


  1. Creation, 
  2. Accessing
  3. updating, 
  4. deletion in tuples and basic tuple operations.

Sets-creation, set operations.


  1. creation
  2. accessing
  3. adding and modifying items
  4. deleting items

UNIT-4: Object Oriented Programming concepts

  1. Oops concept- Introduction,
  2. Classes and Objects
  3. Class method Inheritance
  4. Introduction Inheriting classes in python
  5. Types of Inheritance
  6. Error and Exception Handling

UNIT-5: Data Analysis

Data preparation using pandas and series:

  1. pandas data frame basics
  2. Creating your own data
  3. Series
  4. Data frames
  5. Making changes to series and data frames


  1. Matplotlib Introduction
  2. Univariate plots-Histograms

Text Books:

1. Python Programming Using Problem Solving Approach –Reema Thareja , Oxford University Press, 

2. Pandas for Everyone (Python data Analysis)-Daniel Y.Chen, Pearson Addison Wesley Data and Analytics series


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