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Using Python for Research

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Course Description

This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings.\nUsing a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.This consists of the homeworks for each week.

  1. 1

    Homework 1

    Free

    Exercises for homework (Week 1). In this homework, we will use objects, functions, and randomness to find the length of documents, approximate pi, and smooth out random noise.

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    Exercise 1a
    100 xp
    Exercise 1b
    100 xp
    Exercise 1c
    100 xp
    Exercise 1d
    100 xp
    Exercise 1e
    100 xp
    Exercise 2a
    100 xp
    Exercise 2b
    100 xp
    Exercise 2c
    100 xp
    Exercise 2d
    100 xp
    Exercise 2e
    100 xp
    Exercise 2f
    100 xp
    Exercise 3a
    100 xp
    Exercise 3b
    100 xp
    Exercise 3c
    100 xp
  2. 2

    Homework 2

    Free

    Exercises for Homework (Week 2). Tic-tac-toe (or noughts and crosses) is a simple strategy game in which two players take turns placing a mark on a 3x3 board, attempting to make a row, column, or diagonal of three with their mark. In this homework, we will use the tools we've covered in the past two weeks to create a tic-tac-toe simulator and evaluate basic winning strategies.

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  3. 3

    Case Study 1 - Caesar Cipher

    Free

    A cipher is a secret code for a language. In this case study, we will explore a cipher that is reported by contemporary Greek historians to have been used by Julius Caesar to send secret messages to generals during times of war.

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  4. 4

    Case Study 2 - Translations of Hamlet

    Free

    In this case study, we will find and plot the distribution of word frequencies for each translation of Hamlet. Perhaps the distribution of word frequencies of Hamlet depends on the translation - let's find out!

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  5. 5

    Case Study 3 - Practice with Classification

    Free

    In this case study, we will analyze a dataset consisting of an assortment of wines classified into "high quality" and "low quality", and will use k-Nearest Neighbors to predict whether or not other information about the wine helps us correctly guess whether a new wine will be of high quality.

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  6. 6

    Case Study 4 - Visualizing Whisky Classification

    Free

    In this case study, we have prepared step-by-step instructions for you on how to prepare plots in Bokeh, a library designed for simple and interactive plotting. We will demonstrate Bokeh by continuing the analysis of Scotch whiskies.

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  7. 7

    Case Study 5 - Bird Migration

    Free

    In this case study, we will continue taking a look at patterns of flight for each of the three birds in our dataset.

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  8. 8

    Case Study 6 - Social Network Analysis

    Free

    Homophily is a network characteristic. Homophily occurs when nodes that share an edge share a characteristic more often than nodes that do not share an edge. In this case study, we will investigate homophily of several characteristics of individuals connected in social networks in rural India.

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  9. 9

    Case Study 7 - Movie Analysis, Part 1 - Data Preparation

    Free

    The [movie dataset on which this case study is based](https://www.kaggle.com/tmdb/tmdb-movie-metadata) is a database of 5000 movies catalogued by [The Movie Database (TMDb)](https://www.themoviedb.org/?language=en). The information available about each movie is its budget, revenue, rating, actors and actresses, etc. In this case study, we will use this dataset to determine whether any information about a movie can predict the total revenue of a movie. We will also attempt to predict whether a movie's revenue will exceed its budget. In Part 1, we will inspect, clean, and transform the data.

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  10. 10

    Case Study 7 - Movie Analysis, Part 2 - Modeling

    Free

    The [movie dataset on which this case study is based](https://www.kaggle.com/tmdb/tmdb-movie-metadata) is a database of 5000 movies catalogued by [The Movie Database (TMDb)](https://www.themoviedb.org/?language=en). The information available about each movie is its budget, revenue, rating, actors and actresses, etc. In this case study, we will use this dataset to determine whether any information about a movie can predict the total revenue of a movie. We will also attempt to predict whether a movie's revenue will exceed its budget. In Part 2, we will use the dataset prepared in Part 1 for an applied analysis.

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Datasets

Adj allvillagerelationships vilno 1Adj allvillagerelationships vilno 2Bird trackingGettysburgIndividual characteristicsKey vilno 1Key vilno 2Merged movie dataMerged movie data smallRegionsWhiskies

Collaborators

weston8936ff8baada4c859ad99410410c3555Weston Stearns
Patrick Staples Headshot

Patrick Staples

Patrick Staples is a biostatistics post-doctoral fellow at Harvard University. He likes to study epidemic processes in networks, and develops methods to determine clinically relevant behavior from smartphone data.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA