Hands-on Data Analysis with Pandas (TTPS4878)

3 Days

18 CPD hours

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.

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

• Fundamentals of data analysis
• Statistical foundations
• Setting up a virtual environment

Working with Pandas DataFrames

• Pandas data structures
• Bringing data into a pandas DataFrame
• Inspecting a DataFrame object
• Grabbing subsets of the data
• Adding and removing data

Data Wrangling with Pandas

• What is data wrangling?
• Collecting temperature data
• Cleaning up the data
• Restructuring the data
• Handling duplicate, missing, or invalid data

Aggregating Pandas DataFrames

• Database-style operations on DataFrames
• DataFrame operations
• Aggregations with pandas and numpy
• Time series

Visualizing Data with Pandas and Matplotlib

• An introduction to matplotlib
• Plotting with pandas
• The pandas.plotting subpackage

Plotting with Seaborn and Customization Techniques

• Utilizing seaborn for advanced plotting
• Formatting
• Customizing visualizations

Financial Analysis - Bitcoin and the Stock Market

• Building a Python package
• Data extraction with pandas
• Exploratory data analysis
• Technical analysis of financial instruments
• Modeling performance

Rule-Based Anomaly Detection

• Simulating login attempts
• Exploratory data analysis
• Rule-based anomaly detection

Getting Started with Machine Learning in Python

• Learning the lingo
• Exploratory data analysis
• Preprocessing data
• Clustering
• Regression
• Classification

Making Better Predictions - Optimizing Models

• Hyperparameter tuning with grid search
• Feature engineering
• Ensemble methods
• Inspecting classification prediction confidence
• Addressing class imbalance
• Regularization

Machine Learning Anomaly Detection

• Exploring the data
• Unsupervised methods
• Supervised methods
• Online learning

The Road Ahead

• Data resources
• Practicing working with data
• Python practice