6/14/2016 to 8/18/2016

Instructor: Hamed Hasheminia

##Lecture 1 Summary (Data Science - Introduction Part I)

• Data Science - meaning
• Continuous, Discrete and Qualitative Data
• Supervised vs Unsupervised Learning
• Classification vs Regression
• Time series vs cross-sectional data
• Numpy
• Pandas

Resources

• Lecture 1 - Introduction - Slides
• Intro Numpy - Code
• Intro Numpy - Code - Solutions
• Intro Pandas - Code
• InClass Practice Code - Pandas
• InClass Practice Code - Solutions

Set up GitHub - Self-study guide

• Lecture 0 - GitHub - Slides
• excellent videos to set-up github. Students who have not used GitHub before must watch these videos.
• A hands-on introduction to Git and GitHub, and how to make them work together! More Git resources for beginners here

Pre-work for second lecture

• Review all lecture notes including Lecture Slides, Numpy notebook, and Pandas notebook
• Finish self-study Github guidlines listed above
• Finish Inclass Practice Code
• Review final project requirements. You can find timelines for final project at slide 11 of Lecture 1 PowerPoint Slides

Additional Resources

• Official Pandas Tutorials. Wes & Company's selection of tutorials and lectures
• Julia Evans Pandas Cookbook. Great resource with examples from weather, bikes and 311 calls
• Learn Pandas Tutorials. A great series of Pandas tutorials from Dave Rojas
• Research Computing Python Data PYNBs. A super awesome set of python notebooks from a meetup-based course exclusively devoted to pandas

• Measures of central tendency (Mean, Median, Mode, Quartiles, Percentiles)
• Measures of Variability (IQR, Standard Deviation, Variance)
• Skewness Coefficient
• Boxplots, Histograms, Scatterplots
• Central Limit Theorem
• Class/Dummy Variables
• Walkthrough describing and visualizing data in Pandas

Resources

• Lecture 2 - Slides
• Basic Statistics - Part 2 - Lab Codes
• Basic Statistics - Part 2 - Practice Code
• Basic Statistics - Part 2 - Practice Code - Solutions

HW 1 is Assigned

• Please read and follow instructions from readme
• This homework is due on June 23rd, 2016 at 6:30PM

Additional Resources

• Here you can find valuable resources for matplotlib
• A good Video on Centeral Limit Theorem

• Linear Regression lines
• Single Variable and Multi-Variable Regression Lines
• Capture non-linearity using Linear Regression lines.
• Interpretting regression coefficients
• Dealing with dummy variables in regression lines
• intro on sklearn and searborn library

Resources

• Lecture 3 - Slides
• Linear Regression - Part I - Lab Codes
• Linear Regression - Part I - Practice Code
• Linear Regression - Part I - Practice Solutions

Additional Resources

• My videos on regression lines. Video 1, Video 2
• This is an excellent book. In Lecture 3 and Lecture 4, we are going to cover Chapter 3 of this textbook.
• Seaborn
• Weighted Least Square Method (WLS)
• Good resource for heteroskedasticity
• Here Contours are elegantly introduced.)

• Hypothesis test - test of significance on regression coefficients
• p-values
• Capture non-linearity using Linear Regression lines.
• R-squared
• Interaction Effects

Resources

• Lecture 4 - Slides
• Linear Regression - Part II - Lab Codes
• Linear Regression - Part II - Practice Code
• Linear Regression - Part II - Practice Solution

Additional Resources

• My videos on regression lines. Video 1, Video 2
• This is an excellent book. In Lecture 3 and Lecture 4, we covered Chapter 3 of this textbook.
• statmodels.formula.api

HW 2 is Assigned

• Please read and follow instructions from readme
• Here you can find iPython notebook of your 2nd assignment.
• This homework is due on June 30th, 2016 at 6:30PM