Hands-on Data Analysis with Pandas (TTPS4878)

Description

Duration

3 Days

18 CPD hours

This course is intended for

This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

Overview

Working in a hands-on learning environment, guided by our expert team, attendees will learn to:
Understand how data analysts and scientists gather and analyze data
Perform data analysis and data wrangling using Python
Combine, group, and aggregate data from multiple sources
Create data visualizations with pandas, matplotlib, and seaborn
Apply machine learning (ML) algorithms to identify patterns and make predictions
Use Python data science libraries to analyze real-world datasets
Use pandas to solve common data representation and analysis problems
Build Python scripts, modules, and packages for reusable analysis code
Perform efficient data analysis and manipulation tasks using pandas
Apply pandas to different real-world domains with the help of step-by-step demonstrations
Get accustomed to using pandas as an effective data exploration tool.

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

Introduction to Data Analysis

Working with Pandas DataFrames

Data Wrangling with Pandas

Aggregating Pandas DataFrames

Visualizing Data with Pandas and Matplotlib

Plotting with Seaborn and Customization Techniques

Financial Analysis - Bitcoin and the Stock Market

Rule-Based Anomaly Detection

Getting Started with Machine Learning in Python

Making Better Predictions - Optimizing Models

Machine Learning Anomaly Detection

The Road Ahead