Hands-on Data Analysis with Pandas

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.

Retail Price: $2,195.00

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Course Days: 3


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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



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