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- Course Length:
- 3 Days Instructor Led
The Analytics Workshop is designed for individuals who are familiar with Python and want to start their journey into the world of Data Analytics. By using hands-on, lab-based programming exercises (using Python, Pandas, Jupyter Notebooks and other Python modules), this workshop provides a practical hands-on starting point by focusing on ONLY the necessary statistical concepts needed without getting into the mathematical details. Once the foundational concepts have been covered the workshop takes a use case based approach to get, clean, analyze, visualize and predict from multiple data sets. The students develop programs to go through the analytics process by using Linear Regression, Time Series Analysis and Logistic Regression. The end goal is to get them ready to take the next step of their journey into Machine Learning.
■ Students need a laptop with Python, Notepad ++
■ Students need privileges to install Python modules
This workshop is intended for anyone with Python skills and the desire to build knowledge and skills related to leveraging data tools to start their journey into Data Analytics.
After completing this course, the student will be able to:
• Describe the landscape of Data Analytics
• Describe the role of Pandas in Data Analytics
• Use Pandas to load and prepare data for analysis
• Use Pandas for Linear Regression and prediction
• Use Pandas for Time Series Analysis
• Use Pandas for Logistic Regression and prediction
1. Landscape of Analytics and ML
1.1 What is the Data trying to tell me?
1.2 Why Analytics? Why Now?
1.3 What is Machine Learning?
1.4 What knowledge and skills?
1.5 Get started with Pandas
1.6 Process flow for Analytics and ML
Exercise: Pandas for Data Analytics
2. Statistical Analysis without the Math
2.1 Data Exploration
2.2 Hypothesis Testing
2.4 Data Visualization
2.5 Predictive and Prescriptive Techniques
Exercise: Explore Data using Pandas
Exercise: Explore Data using PowerBI
3. Linear Regression using Pandas
3.1 Linear Regression without the Math
3.2 Why and When of Linear Regression
3.3 Get, Clean, Analyze, Visualize, Predict
Exercise: Use Case: Build LinearR Model 1
Exercise: Use Case: Build LinearR Model 2
4. Time Series Analysis using Pandas
4.1 Time Series Analysis without the Math
4.2 Why and When of Time Series Analysis
4.3 Get, Clean, Analyze, Visualize, Predict
4.4 Types of Time Series
4.5 Types of Models
Exercise: Use Case: Build Time Series Model 1
Exercise: Use Case: Build Time Series Model 2
5. Logistic Regression using Pandas
5.1 Logistic Regression without the Math
5.2 Why and When of Logistic Regression
5.3 Get, Clean, Analyze, Visualize, Predict
Exercise: Use Case: Build LogisticR Model 1
Exercise: Use Case: Build LogisticR Model 2