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:
- NumPy Essentials
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- 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.)
- 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
- 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
