Aim

This course is designed to introduce participants to Pandas, a powerful Python library for data manipulation and analysis. Participants will learn how to use Pandas for data cleaning, preprocessing, and transforming datasets for machine learning applications. The course covers practical data manipulation techniques and prepares participants for real-world AI and machine learning tasks.

Program Objectives

Learn the core functionality of Pandas, including data structures like Series and DataFrames.
Understand how to clean, transform, and explore datasets using Pandas methods.
Master data preprocessing for machine learning applications.
Apply Pandas to real-world tasks like time-series analysis, feature engineering, and data visualization.
Gain hands-on experience in using Pandas to prepare data for machine learning models.

Program Structure

Module 1: Introduction to Pandas

Overview of Pandas and its core features.
Setting up Python environment and installing Pandas.
Introduction to Pandas data structures: Series and DataFrames.

Module 2: Data Preprocessing with Pandas

Handling missing data and outliers.
Data cleaning techniques: Removing duplicates, handling NaN values, and replacing data.
Feature selection and engineering using Pandas functions.

Module 3: Data Exploration and Analysis

Exploratory Data Analysis (EDA): Understanding the dataset through summary statistics and visualizations.
Grouping data and performing aggregation using Pandas groupby() function.
Pivot tables and cross-tabulations for multi-dimensional data analysis.

Module 4: Time Series Analysis with Pandas

Working with time-series data: Datetime objects and time-indexing.
Resampling and frequency conversion for time-series data.
Handling time zones and shifting data for analysis.

Module 5: Merging, Joining, and Concatenating Data

Using merge() to combine datasets.
Joining data using join() method for relational data.
Concatenating data using concat() and handling duplicates.

Module 6: Advanced Data Manipulation with Pandas

Using melt() and pivot() for reshaping data.
Handling hierarchical indexing with Pandas.
Working with categorical data and handling large datasets.

Module 7: Preparing Data for Machine Learning

Feature engineering for machine learning models.
Encoding categorical data for modeling.
Scaling and transforming data for machine learning models.

Module 8: Data Visualization with Pandas

Creating visualizations with Pandas plotting functionality.
Integrating Pandas with Matplotlib and Seaborn for advanced visualizations.
Generating histograms, scatter plots, and box plots to visualize data distributions.

Final Project

Develop a data cleaning and analysis pipeline using Pandas for a real-world dataset.
Perform feature engineering and prepare the dataset for machine learning applications.
Example projects: Clean and analyze a customer dataset, sales prediction model, or time-series forecasting task.

Participant Eligibility

Students and professionals with a basic understanding of Python programming and data analysis.
Anyone interested in learning Pandas for machine learning and AI applications.
Data analysts, data scientists, and AI enthusiasts looking to enhance their skills in data manipulation and preprocessing.

Program Outcomes

Proficiency in using Pandas for data manipulation, cleaning, and preprocessing.
Hands-on experience working with real-world datasets for machine learning and AI.
Ability to perform advanced data analysis using Pandas functions.
Practical experience preparing data for machine learning models using Pandas.

Program Deliverables

Access to e-LMS: Full access to course materials, datasets, and resources.
Hands-on Project Work: Preprocess and analyze real-world data using Pandas.
Final Project: Build a data analysis pipeline and prepare a dataset for AI/ML.
Certification: Certification awarded after successful completion of the course and final project.
e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
AI Developer

Job Opportunities

Data Science Firms: Building machine learning pipelines and working with large datasets using Pandas.
Tech Startups: Implementing data cleaning, analysis, and modeling for business applications.
Research Institutions: Performing data analysis for academic and industrial research using Pandas.
Consulting Firms: Helping clients optimize data workflows and preprocessing for AI/ML solutions.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

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Artificial Intelligence(ai)

Pandas – Use in AI Course

Course Overview

Aim

Program Objectives