Intro to Data Science

The Introduction to Data Science class will survey the foundational topics in data science, namely:

• Data Manipulation
• Data Analysis with Statistics and Machine Learning
• Data Communication with Information Visualization
• Data at Scale -- Working with Big Data

The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.

This course is also a part of our Data Analyst Nanodegree.

• Introduction to Data Science
• What is a Data Scientist
• Pi-Chaun (Data Scientist @ Google): What is Data Science?
• Gabor (Data Scientist @ Twitter): What is Data Science?
• Problems Solved by Data Science
• Pandas
• Dataframes
• Create a New Dataframe

• What is Data Wrangling?
• Acquiring Data
• Common Data Formats
• What are Relational Databases?
• Aadhaar Data
• Aadhaar Data and Relational Databases
• Introduction to Databases Schemas
• API’s
• Data in JSON Format
• How to Access an API efficiently
• Missing Values
• Easy Imputation
• Impute using Linear Regression
• Tip of the Imputation Iceberg

• Statistical Rigor
• Kurt (Data Scientist @ Twitter) - Why is Stats Useful?
• Introduction to Normal Distribution
• T Test
• Welch T Test
• Non-Parametric Tests
• Non-Normal Data
• Stats vs. Machine Learning
• Different Types of Machine Learning
• Prediction with Regression
• Cost Function
• How to Minimize Cost Function
• Coefficients of Determination

• Effective Information Visualization
• Napoleon's March on Russia
• Don (Principal Data Scientist @ AT&T): Communicating Findings
• Rishiraj (Principal Data Scientist @ AT&T): Communicating Findings Well
• Visual Encodings
• Perception of Visual Cues
• Plotting in Python
• Data Scales
• Visualizing Time Series Data

• Big Data and MapReduce
• Basics of MapReduce
• Mapper
• Reducer
• MapReduce with Aadhaar Data
• MapReduce with Subway Data