Wharton Undergraduate Data Analytics Club (WUDAC)

Welcome!

Welcome to Wharton Undergraduate Data Analytics Club. In this repository we host and compile resources for students in the hopes that this will aid in their learning process.

Getting Started

If you are a member of WUDAC, our private resources are hosted below. Please make sure to have a Github account, and sign up for the Student pack for free private repositories (2 years).

• WUDAC Private Resources (Password-protected)
• GitHub Student Pack

For the long haul

• Teach Yourself Programming in Ten Years
• Python Deliberate Practice
• Advanced R Material

Get the tools

Text Editors

• Sublime Text 3 - Several addi-ins available
  • VBScript (See VBA below)
  • Anaconda IDE

Visualization Frameworks

• D3.js

Database

• Amazon Web Services - AWS RBS
• AWS - Database Products Overview
• CouchDB

Penn Academics & Organizations

From our WUDAC Alum, James Wang: What classes should I take at UPenn if I want to become a data scientist?

Newest Classes (Updated Fall 2017)

• CIS545 - Big Data Analytics (Spring 2017)

Penn has several organizations and academic programs tailored towards Data Analytics and Data Science, our club is only one of many.

• Wharton Customer Analytics Initiative - WCAI Home

  • WCAI Workshops: Events
  • WCAI Recommended Courses for Analytics

• UPenn Data Science Minor

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Graduate Programs:

• NYU: MS in Data Science

Fellowships

• The Data Incubator
• Insight - Data Engineering Fellows Program
• Insight - Data Science Fellows Program

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Sources / Repositories:

• Udacity: Nanodegree courses

• Data Science Masters

• University of Waterloo Data Science

• Machine Learning / Data Mining

• Learning Machine Learning and from 187 Quora Questions

• Curated list of AI and ML resources

• 150 ML, NLP & Python Tutorials

• Compilation of ML / Python / Math Cheatsheets

• Curated list Machine Learning and NLP resources for healthcare

• Grow with Google

• Startup with Google - Pre-built ML

• Bento - Learning Tracks

Github

• Data Science Python Notebooks Repo
• Pattern Classification Resources
• Deep Learning in Python

Machine Learning Glossary - Google Developers

Good Reads:

• Doing Data Science at Twitter

Data

• Data Viz - Roundup

Open Source Projects

• Stanford Open Policing Project

• Kaggle

  • New York City Taxi Trip Duration
  • People HR Analytics Repository

Data Sources by size

Massive

A. OpenDataSoft - 2600+ OPEN DATA PORTALS AROUND THE WORLD

Large

• A. Kaggle

Medium

• A. Springboard
• B. Healthcare

Small

• A. Airbnb Listings
• B. Mode Analytics - 5 Public Datasets

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Career

• Incredible Repository: Everything you need to know to get the job
  • More tailored towards Software Engineering, but worth checking out.

Job Portals / Leads

• Uncubed
• Velvet Jobs - Paid but can help find applications
• The 2018 Wealthfront Career Launching Companies List

Resume / Cover Letters

A. Blogs / Sources

• Towards Data Science:Medium

B. Opinion Articles / Tips

• (Op) Covers Letters + Data Science = What You Need to Know
• (Op) Even for Data and Tech Jobs, a Cover Letter is the Best Way to Sell Your Human Skills

Interview Prep

• How do I prepare for a Data Scientist interview?
• How to ace the Data Science interview

By Topic

Introduction

• To-do: Elevator pitch

Product/Case

• Cracking the PM Interview, Ch. 14-15
• Customer Lifetime Value: How to calculate CLV
• The Power of CLV: Managing Customer Lifetime Value at IBM

A/B Testing

• Intro to A/B Testing
• Quora: Expected A/B testing D.S. interviews and how to prepare
  • Statistical Confidence: How is it calculated?
• A/B Testing & SEO: Guide
• 19 A/B Testing Q&A's
• Statistical Significance vs. Validity
• Columbia Paper: P-values and Statistical Practice

Sample Q&A:

• Explaining CI's and Significance: If the statistical test returns significant, then you conclude that the effect is unlikely to arise from random chance alone. If you reject something with 95% confidence, then in the case that there is no true effect, then a result like ours (or a result more extreme than ours) will happen in less than 5% of all possible samples.

• Why is randomization important in experimental design? How would you answer the question, does attending local meetups cause Etsy sellers to gather more sales?

  • Randomization is at the core of experimentation because it balances out these confounding variables. By assigning 50% of users to a control group and 50% of users to a treatment group, you can ensure that the rough level of seller commitment is on average balanced between the two groups, as is every single other possible confounding variable, measured or not.

• What things might we need to be worried about if we have an experiment with 20 different metrics? What if we run 20 experiments simultaneously?

  • The more metrics you are measuring, the more likely you are to get at least one false positive. Ways to attempt to correct for this include changing your confidence level (e.g. Bonferroni Correction) or doing family-wide tests before you dive in to the individual metrics (e.g. Fisher's Protected LSD). However, these are not used often in practice, and most people decide to just proceed with caution and be wary of spurious results.

• Null Hypothesis: "Disputing a null hypothesis is a matter of running the experiment long enough to rule out an incidental outcome. This concept is also referred to as reaching statistical significance."

Coding

• Practice problems: Leetcode
• HackerRank

Statistics & Probability

• Quora: Repository
• Jane Street: Probability & Markets Guide

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General Topics / Papers

Chatbots

Guides / Tutorials

• Compiled How-to Guide

APIs & Engines

• api.ai
• A List of Recommender Systems and Resources
• Amazon:
  • Amazon’s Giving Away the AI Behind Its Product Recommendations
  • Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE)

Computer Vision

• ChainerCV: a Library for Deep Learning in Computer Vision
• Tutorial: Road-traffic counting with Computer Vision

Deep Learning

Deep Learning Reading Roadmap Generating text with deep learning

Machine Learning

ML Algorithms explained

Time-dependent Analysis

• Time-dependent Classification

Natural Language Processing (NLP) / Advanced Text Mining

Courses

• Cornell CS 4740/5740 : Introduction to Natural Language Processing

Algorithms / Key Concepts

• Porter Stemmer Algorithm
• Radix Tree
• Convolutional Neural Networks (CNN)

Courses

Online Courses

• Computer Science

  Harvard: CS50 - Introduction to the intellectual enterprises of computer science and the art of programming.

• Machine Learning

  Intro to ML - Stanford (Andrew NG) ML for Trading

• Recommender Systems

  Recommender Systems Specialization

• Deep Learning

  Deep Learning Specialization

• Databases

Stanford Repository, self-paced

• Natural Language Processing (NLP) / Advanced Text Mining

CS224n: Natural Language Processing with Deep Learning

Portfolio

How to share your data science portfolio Github Project - Best Practices

Regex

• Regular-expressions Tutorial

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Programming Languages - Data Science Core (Python / R / SQL + Databases + Shell)

Databases and Data Engineering

• Stack Overflow - Types of Databases

Amazon Web Services (AWS) - Cloud Computing Services:

• Create an account: Home
• AWS Educate for Students: Apply

AWS Lambda

• Guides Youtube

Command Line / Linux:

• Code Academy: Learn the command line = NYT Configuration: Tutorial

Python:

• Awesome Python - A curated list of awesome Python frameworks, libraries, software and resources.
• Python Style Guide
• Practice Python
• Resources for Learner

Topics

• Pandas - Github

• Word2vec Tutorial

• Scikit learn - Main site

  Pipeline

• Data Cleaning / Wrangling / Manipulation

  Tidy data in Python

• Other (ie C, AI, etc)

  • Cython
  • OpenAI Universe
  • Shogun
  • Caffe(deprecated)
  • Caffe2
  • Theano
  • Zappa
  • Arrow
  • Theano
  • Keras
  • NLTK (NLP)
  • Gensim
  • Statsmodels

Tutorials:

• Mode Analytics: Python Tutorial

Algorithms

PCA

• PCA Step-by-step
• SK-Learn PCA
• t-SNE

K-Means

• Intro to K-Means Clustering

Beginner / Intermediate

• Mode Analytics: Python Tutorial

• Pandas Cookbook

• FAQS / Troubleshooting: a. Dealing with dataframes/dictionaries (i) Dataframe from Dictionary with different lengths

Advanced:

• PCA in Python
  • Other Guides: 2
• Introduction to Ensembling/Stacking in Python
• Kaggle's 2017 Data Science Bowl 2017: Full Preprocessing Tutorial
• Tensorflow
• TensorBoard API
• TensorBoard Github
• Simple Reinforcement Learning
• CIS 545 - Deep Learning with Tensorflow Recitation

R

Getting started: Installation, setup, learning from the basics

Check the WUDAC Dropbox for the main resources (WIP)

• RStudio Cheatsheets

• Using SQL in R: Database Strategy

Tutorials:

• Tidyverse: Home
• Data Manipulation: dplyr package
  • dplyr tutorial
  • Introduction to dplyr for Faster Data Manipulation in R
  • DPLYR TUTORIAL (WITH 50 EXAMPLES)
  • Hands-on dplyr tutorial for faster data manipulation in R

Beginner / Intermediate

• Data Analysis with R

Advanced:

SQL

• Crash course: SQL Teaching (!)

• MySQL Workbench: Download here

• Quora: Learning

• SQLite DataBase Browser

• Database/SQL Interview Questions - ProgrammerInterview.com

• Google Cloud Platform - BigQuery Reference

• PostgreSQL Exercises

Tutorials:

• WCAI SQL Course (Availability varies per semester, register here)
• Khan Academy

  Intro to SQL: Querying and Managing Data Google BigQuery Tutorial New SQL Script

• Tutorials Point SQL
• Mode Analytics: SQL Tutorial for Data Analysis
• W3Schools: SQL Tutorial
• Head First Labs: Practice Problems
• SQL Zoo: Tutorial
• SQL Course: Tutorial
• HackerRank: SQL Challenges
• SQL Indexing Advanced Tutorial
• LinkedIn Learning (Paywall): SQL
• DataWorld: Intro to SQL Functions and Groupby

Topics:

Overview: SQL As Understood By SQLite

• Data types

  • Datatypes
  • Understanding the data types in SQL Server
  • Datatypes In SQLite Version 3 (!)

• Functions

  • Functions
  • SQL Lite Core Functions (!)
  • SQL Lite Date and Time Functions (!)

• Databases

Database Fundamentals - Microsoft Virtual Academy

• Other

Query Planner

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Programming Languages - Software Engineering / Other skills

Android

• LinkedIn Learning (Paywall): Android

AngularJS

• LinkedIn Learning (Paywall): AngularJS

Bootstrap

• LinkedIn Learning (Paywall): Bootstrap

C++ / C / C# / Objective-C

• Quora: Learning
• Stack Overflow: The Definitive C++ Book Guide and List
• LinkedIn Learning (Paywall):

C++ C C# Objective-C

Cassandra

• LinkedIn Learning (Paywall): Cassandra

Excel / VBA

• Sublime Add-in: VBScript

• Excel Training

• LinkedIn Learning (Paywall):

Excel VBA (Visual Basic for Applications)

Git / GitHub

• Intro to GitHub - Main Guide
• Intro to GitHub - Learning Resources

Go

• LinkedIn Learning (Paywall): Go

Hadoop (Apache Hadoop)

• Hadoop MapReduce Tutorial - Github Wiki
• Quora: Learning
• Cognitive Class.ai: Hadoop 101
• MapReduce: Tutorial - See NETS212

Haskell

• Haskell Tutorial: Learn You a Haskell
• LinkedIn Learning (Paywall): Haskell

HTML/CSS/JS/Other:

• Quora: Learning
• Dash: LEARN TO CODE AWESOME WEBSITES IN HTML, CSS, AND JAVASCRIPT
• W3Schools
• Mozilla Development Network (MDN - Github Repo
• LinkedIn Learning (Paywall):

HTML CSS

• Khan Academy Tutorials

  Intro to HTML/CSS: Making webpages HTML/JS: Making webpages interactive HTML/JS: Making webpages interactive with jQuery

iOS

• LinkedIn Learning (Paywall): iOS

Java

• CIS 110 / 120 (...)
• Testing Framework - CompileJava
• LinkedIn Learning (Paywall): Java

Javascript:

• Udacity - Introduction to Javascript
• AirBnB - Javascript Style Guide
• Quora: What is the best way to learn JS?
• Book: JavaScript: The Good Parts
• Khan Academy Tutorials

  Intro to JS: Drawing & Animation Advanced JS: Games & Visualizations Advanced JS: Natural Simulations HTML/JS: Making webpages interactive

General Topics

• Asynchronous Javascript:

  Art of Node

• Chat Interface: Socket.io

  Socket.io

Node.js

• Guide to Node.js - InfoWorld
• Art of Node - Intro Guide

Julia

• LinkedIn Learning (Paywall): Julia

Kotlin

• LinkedIn Learning (Paywall): Kotlin

MATLAB:

• OIDD 311 (...)
• Quora: Learning / Resources

Markdown

• Intro to Markdown
• Markdown Cheatsheet

MongoDB:

• Quora:

Prerequisites Learning

Perl

• LinkedIn Learning (Paywall): Perl

PHP

• LinkedIn Learning (Paywall): PHP

Ruby:

• Quora: Learning

Scala

• LinkedIn Learning (Paywall): Scala

Spark

• Apache Spark
• Spark Examples
• LinkedIn Learning (Paywall): Apache Spark

SPSS

• LinkedIn Learning (Paywall): SPSS

Swift

• LinkedIn Learning (Paywall): Swift

Tableau

• LinkedIn Learning (Paywall): Tableau

Wordpress

• LinkedIn Learning (Paywall): Wordpress

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Official Blogs / Research Sites:

AirBnB

• Data Projects
• Airmap View

Facebook Research

• Research Areas - Data Science:

  https://research.fb.com/category/data-science/ https://research.fb.com/blog/

• Publications:

  https://research.fb.com/publications/

• Engineering Blog:

  https://code.facebook.com/posts/datascience/ https://code.facebook.com/posts/data/

• Research Areas - Data Science:

  https://research.fb.com/category/data-science/

Netflix

• Engineering Blog:

Wealthfront

• Engineering Blog:

University Data Science Teams / Clubs

• Machine Learning at Berkeley

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Podcasts:

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Other Blogs:

• Ray Zhang (OneRaynyDay) - Github
• Hacker Paradise
• Exercise Repo - LearnNPractice