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. 
• Optional: Master the integration of AI analytics to transform and elevate data analysis, encompassing predictive modeling and decision-making enhancement techniques. 
• Optional: Acquire practical skills in applying machine learning and deep learning to automate data wrangling and generate dynamic, predictive data visualizations. 

Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional python, data science, AI / machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals. 

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. Ideal attendees include experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics or eventual machine learning tasks. Attending students are required to have a background in basic Python for data science. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.  

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 

Optional: AI-Driven Enhancements to Data Analytics 

Incorporating AI in Data Analysis Enhancements 

• Exploring AI-driven analytics platforms 
• Integrating predictive analytics into data projects 
• Enhancing decision-making with prescriptive analytics 
• Building a simple AI model to derive insights from data 

Advancing Data Wrangling with Machine Learning 

• Implementing ML algorithms for data cleaning and preprocessing 
• Utilizing natural language processing (NLP) for data categorization and tagging 
• Case study: Automating data wrangling in industry scenarios 
• Optional Lab: Using ML tools for handling complex data transformations 

Deep Learning for Predictive DataVisualizations 

• Fundamentals of neural networks in visualization 
• Creating advanced visuals with deep learning models 
• Interactive data stories using AI-powered tools 
• Optional Lab: Designing a predictive dashboard using AI visual tools 

The Road Ahead 

• Data resources 
• Practicing working with data 
• Python practice