Introduction to Data Science

Introduction

• Beginning your data science exploration

1. Defining Data Science

• Demystifying data science
• The value of data science
• Defining the data science life cycle

2. Starting with Data Design

• Reducing bias with probability sampling
• Using non-probability sampling

3. Utilizing Computational Tools

• Comparing Python and R
• Setting up your Jupyter environment

4. Structuring Your Tabular Data

• Defining tabular data
• Reading tabular data
• Interpreting tabular data
• Gathering insights
• Answering specific questions

5. Using Exploratory Data Analysis

• Defining exploratory data analysis
• Recognizing statistical data types
• Distinguishing properties of data

6. Cleaning Your Data

• Explaining data cleaning
• Questions to guide data cleaning

7. Using Data Visualization

• Demystifying data visualization
• Visualizing your qualitative data
• Visualizing your quantitative data

8. Using Inference and Statistical Analysis

• Defining inference
• Designing a hypothesis test
• Creating a permutation
• Conducting a permutation test
• Bootstrapping a confidence interval

9. Using Prediction in Data Science

• Defining prediction for data science
• Navigating classification
• Recognizing the k-NN algorithm
• Implementing k-Nearest Neighbors
• Navigating regression
• Checking assumptions of regression
• Implementing linear regression

Conclusion

• Next steps