> For the complete documentation index, see [llms.txt](https://mpp-data-science.gitbook.io/project/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mpp-data-science.gitbook.io/project/06-data-science-essentials.md). # README DAT203.1x: Data Science Essentials ## About course I completed in late 2016 ## Contents ### Before You Start * Course Introduction * Lab Overview * Setting Up Azure Machine Learning * Installing R or Python ### Module 1: Introduction to Data Science #### Principles of Data Science * What Is Data Science? * Data Analytic Thinking * The Data Science Process * Further Reading **Data Science Technologies** * Introduction to Data Science Technologies * An Overview of Data Science Technology * Azure Machine Learning * Using Code in Azure ML * Jupyter Notebooks * Creating a Machine Learning Model * Further Reading **Lab** * Lab ### Module 2: Probability and Statistics for Data Science #### Probability and Random Variables * Overview of Probability and Random Variables * Introduction to Probability * Discrete Random Variables * Discrete Probability Distributions * Binomial Distribution Examples * Poisson Distributions * Continuous Probability Distributions * Cumulative Distribution Functions * Central Limit Theorem * Further Reading **Introduction to Statistics** * Overview of Statistics * Descriptive Statistics * Summary Statistics * Demo: Viewing Summary Statistics * Z-Scores * Correlation * Demo: Viewing Correlation * Simpson's Paradox * Further Reading **Lab** * Lab This content is graded * Lab Instructions * Lab Verification **Module 3: Simulation and Hypothesis Testing** **Simulation and Confidence Intervals** * Introduction to Simulation and Hypothesis Testing * Simulation * Demo: Performing a Simulation * Confidence Intervals * Demo: Confidence Intervals * Further Reading **Hypothesis Testing** * Overview of Hypothesis Testing * Introduction to Hypothesis Testing * Z-Tests, T-Tests, and Other Tests * Hypothesis Test Examples * Type 1 and Type 2 Errors * Demo: Hypothesis Testing * Misconceptions About Hypothesis Testing * Further Reading **Lab** * Lab This content is graded * Lab Instructions * Lab Verification **Module 4: Exploring and Visualizing Data** **Exploring Data** * Introduction to Data Exploration * Data and Data Frames * Working with Data in Code * Demo: Getting Started with Data Frames * Working with Data Frames in Azure ML * Demo: Working with Data Frames in Azure ML * Metadata * Demo: Working with Metadata * Further Reading **Visualizing Data** * Overview of Data Visualization * Introduction to Data Visualization * Conditioned Plots * Plotting in R or Python * Demo: Plotting in R or Python * Demo: Plotting in Azure ML * Further Reading **Lab** * Lab This content is graded * Lab Instructions * Lab Verification **Module 5: Data Cleansing and Manipulation** **Data Ingestion and Flow** * Overview of Data Ingestion and Flow * Data Flow in Azure ML * Joining Data Sets * Demo: Ingesting and Joining Data * Demo: Joins in R or Python * Further Reading **Data Cleansing** * Introduction to Data Cleansing * Overview of Data Cleansing * Missing and Repeated Values * Demo: Handling Missing and Repeated Values * Feature Engineering * Outliers and Errors * Demo: Finding Outliers * Demo: Handling Outliers in Azure ML * Demo: Cleaning Data with R or Python * Introduction to Data Scaling * Demo: Scaling Data in Azure ML * Demo: Scaling Data in R or Python * Further Reading **Lab** * Lab This content is graded * Lab Instructions * Lab Verification **Module 6: Introduction to Machine Learning** **Getting Started with Machine Learning** * Machine Learing Overview * Introduction to Machine Learning - Classification * Evaluating Classifiers * Demo: Creating a Classification Model in Azure ML * Regression * Evaluating Regression Models * Demo: Creating a Regression Model * Clustering * Demo: K-Means Clustering * Further Reading **Publishing a Machine Learning Web Service** * Introduction to Azure ML Web Services * Overview of Publishing a Web Service * Demo: Publishing a Web Service * Demo: Consuming a Web Service * Custom Code Considerations * Key Points and Further Reading **Lab** **Final Exam and Survey** Course Exam Post-Course Survey