Hands-on Data Analysis with Pandas

At Course Completion

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.

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.

Audience Profile

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.

Prerequisites

Students should have skills at least equivalent to the following course(s) or should have attended as a pre-requisite:

· TTDS6000 Understanding Data Science | A Technical Overview – 1 day (helpful but not required)

· TTPS4800 Introduction to Python Programming Basics (3 days)

Outline

1. Introduction to Data Analysis

· Fundamentals of data analysis

· Statistical foundations

· Setting up a virtual environment

2. 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

3. Data Wrangling with Pandas

· What is data wrangling?

· Collecting temperature data

· Cleaning up the data

· Restructuring the data

· Handling duplicate, missing, or invalid data

4. Aggregating Pandas DataFrames

· Database-style operations on DataFrames

· DataFrame operations

· Aggregations with pandas and numpy

· Time series

5. Visualizing Data with Pandas and Matplotlib

· An introduction to matplotlib

· Plotting with pandas

· The pandas.plotting subpackage

6. Plotting with Seaborn and Customization Techniques

· Utilizing seaborn for advanced plotting

· Formatting

· Customizing visualizations

7. 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

8. Rule-Based Anomaly Detection

· Simulating login attempts

· Exploratory data analysis

· Rule-based anomaly detection

9. Getting Started with Machine Learning in Python

· Learning the lingo

· Exploratory data analysis

· Preprocessing data

· Clustering

· Regression

· Classification

10. Making Better Predictions - Optimizing Models

· Hyperparameter tuning with grid search

· Feature engineering

· Ensemble methods

· Inspecting classification prediction confidence

· Addressing class imbalance

· Regularization

11. Machine Learning Anomaly Detection

· Exploring the data

· Unsupervised methods

· Supervised methods

· Online learning

12. The Road Ahead

· Data resources

· Practicing working with data

· Python practice