This Python with Pandas and NumPy training is designed to help participants master data manipulation, numerical computing, and analytical workflows using Python’s most powerful libraries. The course focuses on working with structured data using Pandas and performing high-performance numerical operations with NumPy. Participants will gain hands-on experience to clean, transform, analyze, and visualize datasets efficiently for data analysis, business intelligence, and data science use cases.

• Understand core Python programming concepts for data analysis and scientific computing.
• Work with NumPy arrays for efficient numerical computations and mathematical operations.
• Perform data cleaning, transformation, filtering, and aggregation using Pandas.
• Analyze structured datasets using DataFrames, statistical functions, and data manipulation techniques.
• Handle real-world data from CSV, Excel, and JSON sources for business analysis.
• Build data-driven reports, visualizations, and analytical workflows using Python libraries.

 • Data analysts and business analysts
 • Data scientists and aspiring data professionals
 • Python developers working with data
 • Business intelligence professionals
 • Students and professionals entering data analytics

 • Basic knowledge of Python programming
 • Basic understanding of data concepts is helpful
 • Familiarity with spreadsheets or CSV data formats

 • Performing data analysis using Pandas
 • Manipulating and transforming datasets
 • Working with NumPy arrays and vectorized operations
 • Handling missing data and data cleaning
 • Merging, joining, and reshaping datasets
 • Performing statistical and numerical analysis
 • Building efficient data analysis workflows

By the end of this training, participants will be able to confidently use Pandas and NumPy to analyze, clean, and transform data for real-world business and data science applications. Learners will gain practical skills to build efficient analytical workflows and prepare datasets for advanced analytics, machine learning, and reporting.

Introduction to NumPy for Numerical Computing
 • NumPy arrays and data types
 • Creating and reshaping arrays
 • Indexing, slicing, and broadcasting
 • Vectorized operations and performance benefits

Foundations of Pandas
 • Pandas Series and DataFrames
 • Loading data from CSV, Excel, and databases
 • Inspecting and exploring datasets
 • Basic data selection and filtering

Data Cleaning & Preprocessing
 • Handling missing values
 • Data type conversions
 • Removing duplicates
 • Basic data validation

Exploratory Data Analysis with Pandas
 • Descriptive statistics
 • Sorting and ranking data
 • GroupBy operations
 • Basic data aggregation

Advanced Pandas Operations
 • Merging and joining DataFrames
 • Concatenating datasets
 • Reshaping with pivot tables and melt
 • Working with hierarchical indexes

Time Series Data Analysis
 • Working with date and time data
 • Resampling and rolling windows
 • Time-based indexing
 • Time series feature extraction

Numerical Analysis with NumPy
 • Mathematical and statistical functions
 • Random number generation
 • Linear algebra basics
 • Performance optimization with NumPy

Data Visualization Integration
 • Integrating Pandas with Matplotlib
 • Creating basic charts and plots
 • Visualizing trends and distributions
 • Best practices for data visualization

Building End-to-End Data Analysis Workflows
 • Designing repeatable analysis pipelines
 • Combining NumPy and Pandas effectively
 • Managing large datasets efficiently
 • Best practices for production-ready analysis

• Python Fundamentals for Data Analysis Assessment
• NumPy Array Operations Assessment
• Pandas Data Manipulation Assessment
• Data Cleaning & Transformation Assessment
• Data Analysis & Reporting Mini Project Assessment
 

Participants will be evaluated through hands-on labs, real-world data analysis exercises, instructor-led code reviews, and a final practical assessment focused on applying Pandas and NumPy for business and analytical use cases.

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Python with Pandas and NumPy. This digital, verifiable certification validates practical data analysis and numerical computing skills and can be shared on LinkedIn and included in professional profiles.