Byte-Sized ML Series: NumPy and Pandas

Description:

This 90-minute session introduces the essential Python libraries for data manipulation—NumPy and Pandas. Learners will gain hands-on experience working with numerical arrays and tabular data structures, key skills required before diving deeper into machine learning workflows. With a practical, example-driven approach, this session demystifies the core operations, empowering learners to clean, explore, and prepare data effectively.

Duration: 90 mins

Course Code: BDT488

Learning Objectives:

After this course, you will be able to:

• Understand the purpose and role of NumPy and Pandas in a data science workflow
• Use NumPy to create and manipulate arrays efficiently
• Perform key operations on Pandas Series and DataFrames
• Load, inspect, transform structured datasets
• Prepare data for machine learning models using foundational techniques

• Must have some python programming experience.

• Aspiring data scientists, analysts, and ML enthusiasts with basic python experience and are looking to build a strong foundation in structured data manipulation using Python's core data libraries.

Course Outline:

1. NumPy Essentials
2. 1. • What is NumPy? Why use it?
      • Creating arrays (1D, 2D, 3D)
      • Array indexing and slicing
      • Vectorized operations & broadcasting
      • Hands-on: Using NumPy and numeric computations (mean, sum, etc.)
   2. Pandas Basics
      • Introduction to Pandas Series and DataFrames
      • Reading CSV files and inspecting data
      • Getting statistical information from the data
      • Data selection and filtering, changing data types
      • Hands-on: Load and explore small dataset
   3. Data Transformations & Preparation
      • Removing duplicates
      • Renaming columns
      • Soring, grouping and aggregating
      • Basic string operations
      • Hands-on: Perform transformations on data

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools