Data-Science-Assignments

Repository for the Data Science learning track to host assignments.

Week 1

In Class Assignment due Saturday July 20, 2019 @ 8pm

• Readings (The Unix Shell)
  • Introducing the Shell
  • Navigating Files and Directories
  • Working with Files and Directories
  • Pipes and Filters

Week 2

Homework due Wednesday July 24, 2019 @ 6pm

• Readings

  • Hello World tutorial
  • Data types & data structures
  • String manipulations
  • If-else

• Signup for Hackerrank and complete the following Hackerrank challenges.

  • Python Hello World
  • Basic math
  • String manipulations
  • String manipulations 2
  • Python If-Else

• Setup & configure git

  • Create a GitHub account.
  • Create an SSH key.
  • Link your SSH key to your GitHub account
  • Verify your SSH connection.

In Class Assignment due Friday July 26, 2019 @ 8pm

• Readings (Plotting and Programming in Python)
  • Running and Quiting
  • Variables and Assignment
  • Data Types and Type Conversion
  • Built-In Functions and Help
  • Libraries

Week 3

Homework due Wednesday July 31, 2019 @ 6pm

• Readings

  • Loops
  • Functions
  • Modulo documentation
    • Supplemental (not required): Modulo video. You'll need to use the modulo operator for one of your homeworks. If the documentation below isn't enough to help you solve the challenge, see the video as well.
  • List comprehensions
    • Supplemental (not required): more on list comprehensions
  • Lambda functions & map, filter, reduce

• Cheat Sheets

  • Jupyter notebooks
  • Python for Data Science

• Hackerrank Python

  • Loops
  • List comprehensions
    • The Problem statement and Tutorial should be sufficient to guide you through this exercise, but the example code using list comprehensions on the Problem page is difficult to read. Here it is line by line (and in Python3): Code using list comprehensions:
      x = int( raw_input() )
y = int( raw_input() )
n = int( raw_input() )
print( [ [i, j] for i in range(x + 1) for j in range(y + 1) if( ( i + j ) != n )] )
  • Your first Python function
    • Bonus: Try testing your code against custom input (your name!).
  • More functions
  • Bonus (not required): Nested lists

In Class Assignment due Friday August 2, 2019 @ 8pm

• Finish working through the Intro to Python 2 notebook

Week 4

Homework due Wednesday August 7, 2019 @ 6pm

• Readings

  • Type conversion
  • Numpy overview
    • Note: You won't need to install numpy (it should be included with your Anaconda installation).

• Jupyter notebooks

  • Tutorial: Computing with NumPy arrays. You can download the data and execute the code in this notebook yourself, or just read through the executed version. Make sure you understand all the numpy methods.

• Cheat Sheets

  • NumPy

• Hackerrank

  • Numpy arrays
  • Zeros and ones
  • Vector math
  • Min and max
  • Floor/ceiling/rint
  • Reshape arrays

In Class Assignment due Friday August 9, 2019 @ 8pm

• Finish working through the Numerical computing notebook and the associated readings (linked in the notebook).

Week 5

Homework due Wednesday August 14, 2019 @ 6pm

• Readings

  • Pandas DataFrames
  • Time Series tutorial with Pandas
  • Matplotlib tutorial
  • Notebooks to read through:
    • Pulling data & assembling Pandas DataFrame - Chipotle dataset
    • Exploratory analysis with matplotlib - Retail dataset

• Coding challenges:

  • Notebooks with fill-in-the-blank code blocks - will be posted by 12p Friday, Feb 15.
    • Reading in & assembling Pandas Data Frames - Occupations dataset
      • Executed but no solutions.
    • Plotting practice - Titanic dataset
      • Executed but no solutions.
  • Hackerrank
    • Numpy array stats
    • Floating point practice
    • Working with integers

In Class Assignment due Friday August 16, 2019 @ 8pm

• Work through ONE notebooks:
  • More plotting practice - tips notebook
    • Executed

Week 6

Homework due Wednesday August 21, 2019 @ 6pm

• Readings

  • Think Stats: Chapter 1 - Exploratory Data Analysis
  • Exploratory Data Analysis I
  • Exploratory Data Analysis II
  • Hierarchical Indicies, Groupby
  • Think Stats: Chapter 2 - Distributions

• Cheat Sheets

  • Pandas

• Hackerrank

  • Statistics Warmup

• Jupyter Notebooks

  • Pandas Groupby
  • Pandas Apply
  • Pandas Stats
  • Pandas Visualization

In Class Assignment due Friday August 23, 2019 @ 8pm

• Read through and run commands for following tutorials:
  • Seaborn Introduction
  • Seaborn Tutorial

Week 7

Homework due Wednesday August 28, 2019 @ 6pm

• Reading

  • ThinkStats
    • Chapters 3-5. Don't worry about the thinkplot code or the exercises at the end of each chapter. Focus on the content! The goal is to be familiar with different types of distributions.

• Watch

  • Videos on normal distributions.
    • Work through practice exercises.
    • Understand the review page.

• Notebooks

  • Download and work through the following 3 notebooks. Make sure you understand the concepts as well as the python code. You should complete the exercises throughout.
    • Warm-up.
    • Baisc Metrics
    • Distributions
    • Directory with datasets for the above 3 notebooks. You should download these data to the same directory where the notebooks are. You will need to provide the correct path to your data in each Jupyter Notebook.

In Class Assignment due Friday August 30, 2019 @ 8pm

• Readings

  • Groupby documentation - this entire page is really good, but read at least the first two sections ("Splitting an object into groups" and "Aggregation," up to the "Transformation" section).
    • Relevant to class Wednesday: attributes for GroupBy objcets (e.g. how we found you can call head on a GroupBy object).
    • For in-class assignment: Iterating through groups.

• Complete blank code blocks in groupby notebook.

Week 8

Homework due Wednesday September 4, 2019 @ 6pm

• Readings

  • ThinkStats
    • Chapters 6-8. Don't worry about the thinkplot code or the exercises at the end of each chapter. Focus on the content!

• Kahn Academy

  • Probabilities
    • Take this quiz. If you have trouble with the answers, please watch the videos for the basic probability section.
    • Videos, articles & quizzes for Experimental probability.
    • Videos, articles & quizzes for Randomness, probability & simulation.
  • Hypothesis testing
    • Videos & quizzes for Significance tests.
  • Scatterplots & correlations
    • Videos, articles & quizzes for Introduction to scatterplots.
    • Videos, articles & quizzes for Correlation

• Notebooks

  • Hypothesis testing

In Class Assignment due Friday September 6, 2019 @ 8pm

• Readings & video
  • Bootstrapping introduction
  • Bootstrapping video

Week 9

Homework due Wednesday September 11, 2019 @ 6pm

• Coursera Machine Learning

  • Enroll in Coursera's Machine Learning Cohort that starts on March 18th: Coursera Machine Learning You will be able to access lecture material today.
  • Finish all Readings, Videos, and Quizzes for the following sections in Week 1: Introduction:
    • Introduction
    • Linear Regression with One Variable

• Google Machine Learning Crash Course

  • Enroll in Google's Machine Learning Crash course. The material is available antyime. Google ML Crash Course
  • Finish all Videos, Readings, Key Terminology, and Check Your Understanding for the following sections:
    • Introduction to ML
    • Framing
    • Descending into ML
    • Reducing Loss

• Introduction to Machine Learning with Python

  • Clone or download (click the green Clone or Download button) the entire Introduction to Machine Learning with Python GitHub Repo. Ensure that all appropriate packages are installed on your computer before class Wednesday March 20th. We will be following through these notebooks as our In-Class Assignments.

• HackerRank

  • Solve the following HackerRank Linear Algebra Problems:
    • Linear Algebra Foundations #1 - Matrix Addition
    • Linear Algebra Foundations #2 - Matrix Subtraction
    • Linear Algebra Foundations #3 - Matrix Multiplication
    • Linear Algebra Foundations #4 - Matrix Multiplication
  • If you have trouble with any of the problems above, check out some of the review material on Khan Academy:
    • Adding vectors algebraically & graphically
    • Vector Examples

In Class Assignment due Friday, September 13, 2019 @ 8pm

• Introduction to Machine Learning with Python
  • Work through Introduction to ML Notebook
• Bootstrapping Followup
  • Work through Bootstrapping Notebook

Week 10

Homework due Wednesday September 18, 2019 @ 6pm

• Coursera Machine Learning

  • Finish all Readings, Videos, and Quizzes for the following sections in Week 1 and Week 2:
    • Linear Algebra Review
    • Linear Regression with Multiple Variables

• Google Machine Learning Crash Course

  • If you didn't complete all items in Week 9 (including Playground Exercises) in Introduction to ML, Framing, Descending into ML, or Reducing Loss, go back and finish all items.
  • Finish all Videos, Readings, Key Terminology, Playground Exercises, Programming Assignments, and Check Your Understanding for the following sections.
    • First Steps with TF: There's are 3 Programming Assignments with this section which will run on Google's Colaboratory platform. These are very similar to Jupyter notebooks, but will not run locally.
      • Quick Introduction to pandas
      • First Steps with TensorFlow
      • Synthetic Features and Outliers
    • Generalization
    • Training and Test Sets

In Class Assignment due Friday, September 20, 2019 @ 8pm

• Introduction to Machine Learning with Python
  • Work through Supervised Learning Notebook through only Section: Linear regression aka ordinary least squares This is a very long notebook and we will be working through the ML algorithms week-by-week.

Week 11

Homework due Wednesday September 25, 2019 @ 6pm

• Coursera Machine Learning

  • Finish all Readings, Videos, and Quizzes for the following sections in Week 3:
    • Logisitic Regression
    • Regularization

• Google Machine Learning Crash Course

  • Finish all Videos, Readings, Key Terminology, Playground Exercises, and Check Your Understanding for the following sections:
    • Training and Test Sets
    • Validation Set
    • Regularization for Simplicity
    • Logistic Regression

• Harvard CS109

  • Work through Bias, Variance, Cross-Validation Notebook

In Class Assignment due Friday September 27, 2019 @ 8pm

• Harvard CS109
  • Work through Sklearn, Regression, PCA Notebook

Week 12

Homework due Wednesday October 2, 2019 @ 6pm

• Coursera Machine Learning

  • Finish all Readings, Videos, and Quizzes for the following sections in Week 4:
    • Neural Networks: Representation

• Google Machine Learning Crash Course

  • Finish all Videos, Readings, Key Terminology, Playground Exercises, and Check Your Understanding for the following sections:
    • Representation
    • Feature Crosses

In Class Assignment due Friday October 4, 2019 @ 6pm

• Work through Dense NN MINST Notebook

Week 13

Homework due Wednesday October 9, 2019 @ 6pm

• Coursera Machine Learning

  • Finish all Readings, Videos, and Quizzes for the following sections in Week 5:
    • Neural Networks: Learning

• Google Machine Learning Crash Course

  • Finish all Videos, Readings, Key Terminology, Playground Exercises, and Check Your Understanding for the following sections:
    • Neural Networks
    • Training Neural Networks

In Class Assignment due Wednesday October 11, 2019 @ 6pm

• No In-Class Assignment due. Make sure to be caught up

Deep Learning Specialization

• For those of you interested in learning more in-depth material about Neural Networks, we highly recommend you to complete the Deep Learning Specialization. This is a 5 course series from Coursera which deals with implementing a set of state-of-the-art Neural Networks. This is well beyond the scope of CoderGirl- Data Science, but we wanted to keep this here as a reference.

Week 14

Homework due Wednesday October 16, 2019 @ 6pm

• Google Machine Learning Crash Course

  • Finish all Videos, Readings, Key Terminology, Playground Exercises, and Check Your Understanding for the following sections:
    • Classification
    • Regularization: Sparsity
    • Mutli-Class Neural Networks

• Introduction to Convolutional Neural Networks

  • Read An Initutive Explanation of Convnets

In Class Assignment due Friday October 18, 2019 @ 8pm

• Work through Fashion CNN Notebook
• Use Keras Cheat Sheet if needed

Week 15

Homework due Wednesday October 23, 2019 @ 6pm

• Coursera Machine Learning
  • Finish all Readings, Videos, and Quizzes for the following sections in Week 6:
    • Advice for Applying Machine Learning
    • Machine Learning System Design
• Work through notebook on Parameter Selection, Validation & Testing

In Class Assignment due Friday October 25, 2019 @ 8pm

• Read Bias Variance Tradeoff Post

Week 16

Homework due Wednesday October 30, 2019 @ 6pm

• Read the following post on Decision Trees
• Read the following post on Decision Trees and Random Forest
• Read the following post on Random Forest, AdaBoost, and Gradient Boosted Trees

In Class Assignment due Friday November 1, 2019 @ 8pm

• Read the following post on AdaBoost
• Read the following post on Gradient Boosting

Week 17

Homework due Wednesday November 6, 2019 @ 6pm

• Coursera Machine Learning
  • Finish all Readings, Videos, and Quizzes for the following sections in Week 8:
    • Unsupervised Learning
• Google Machine Learning Crash Course
  • Finish all Videos, Readings, Key Terminology, Playground Exercises, and Check Your Understanding for the following sections:
    • Overview
    • Clustering Workflow
      • Create a Manual Similarity Measure section is optional
    • Summary

In Class Assignment due Friday, November 8 @ 8pm

• k-means notebook up through Example 1. Stop at Example 2.

Week 18

Homework due Wednesday November 13, 2019 @ 6pm

• Coursera Machine Learning
  • Finish all Readings, Videos, and Quizzes for the following sections in Week 8:
    • Dimensionality Reduction
• Read survey on Dimensionality Reduction Techniques

In Class Assignment due Friday November 15, 2019 @ 8pm

• Work through PCA step-by-step

Week 19

Homework due Wednesday, November 20 @ 6pm

• Git lesson on Software Carpentry.
  • Work through the first 10 lessons.
• Additional resources (not homework):
  • git cheatsheet
  • git for scientists

In Class Assignment due Friday November 22 @ 8pm

• Read following material on Scientific Presentation

Week 20

No Class - Thanksgiving

Week 21

Mini-Project I: Homework due Wednesday, December 4 @ 6pm

• Perform Explortatory Data Analysis (EDA) on Heart Disease Kaggle Project
• Post the link to your GitHub repo for Mini-Project I: EDA

Week 22

Mini-Project II: Homework due Wednesday, December 11 @ 6pm

• Post the link to your GitHub repo for Mini-Project II: Modeling

Week 23

Mini-Project III: Homework due Wednesday, December 18 @ 6pm

• Post the link to your GitHub repo for Mini-Project III: Presentation