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Data Analysis Using Python
  • Level Foundation
  • المدة 17 ساعات hours
  • الطبع بواسطة University of Pennsylvania
  • Offered by

عن

This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.

الوحدات

1. Course Introduction

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Learning Style Preference Survey

2 Videos

  • Course Introduction
  • About the Instructor : Brandon Krakowsky

2 Readings

  • Course Layout & Syllabus
  • Tips to succeed in this course

2. Module Resources

    • 1 Readings
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1 Readings

  • Module 1 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)

3. Loading, Querying, & Filtering .csv Files

    • 4 Videos
    • 3 Readings
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4 Videos

  • Downloading & Installing Jupyter Notebook
  • Using Jupyter Notebook
  • Importing and reading a file using the csv module
  • Coding demonstration : Analyzing the 500 Greatest Albums of All Time

3 Readings

  • Review of dictionaries
  • Review of lists
  • Review of loops

4. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 1 - Loading, Querying, & Filtering Data

5. Catching Data Errors & Sorting

    • 2 Videos
    • 3 Readings
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2 Videos

  • Coding demonstration : Catching data errors and sorting
  • Coding demonstration : Calculating max and min

3 Readings

  • Review of functions
  • Lambda with max and min
  • Opt-in to Penn Engineering Online Communications

6. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 2 - Catching Errors & Sorting

7. Assessment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Homework 1 - Import, Read, & Explore UFO Sightings Data

1 Readings

  • Homework 1 - Instructions

1. Module Resources

    • 1 Readings
Show info about module content

1 Readings

  • Module 2 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)

2. Loading, Inspecting, & Querying Data

    • 4 Videos
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4 Videos

  • The pandas module
  • Loading data
  • Inspecting data
  • Querying data

3. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 3 - Loading, Inspecting, & Querying Data

4. Assessment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Homework 2 - Load, Inspect, & Query Movie & Ratings Data

1 Readings

  • Homework 2 - Instructions

5. Joining & Filtering Data

    • 10 Videos
    • 2 Readings
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10 Videos

  • Joining data
  • Code Along Exercise : Join data
  • Slicing rows
  • Querying data using boolean indexing
  • Code Along Exercise : Dive bar recommendation in Las Vegas
  • Computations - sum()
  • Computations - mean()
  • Other methods
  • Updating & creating data
  • Code Along Exercise : Add rating column

2 Readings

  • Casting Data
  • Cleaning data & dealing with missing values

6. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 4 - Joining & Filtering Data

7. Assessment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Homework 3 - Join & Filter Movie & Ratings Data

1 Readings

  • Homework 3 - Instructions

1. Module Resources

    • 2 Readings
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2 Readings

  • Module 3 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)
  • Opt-in to Penn Engineering Online Communications

2. Summarizing Data

    • 6 Videos
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6 Videos

  • Summarizing groups
  • The numpy library
  • Pivot tables
  • Using an index
  • Code Along Exercise : Average review count and rating
  • Aggregate functions

3. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 5 - Summarizing Data

4. Assessment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Homework 4 - Summarize Movie & Ratings Data

1 Readings

  • Homework 4 - Instructions

5. Visualizing Data

    • 12 Videos
    • 2 Readings
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12 Videos

  • Jupyter notebook magic functions
  • The matplotlib library
  • Histograms
  • Histograms Coding Demonstration : To show distribution of data
  • Histograms Coding Demonstration : Preparing data
  • Histograms Coding Demonstration : Setting options for PyPlot
  • Histograms Coding Demonstration : Displaying the visualization
  • Scatterplots
  • Scatterplots Coding Demonstration : To compare data points on different dimensions
  • Scatterplots Coding Demonstration : Preparing data
  • Scatterplots Coding Demonstration : Setting options for PyPlot
  • Scatterplots Coding Demonstration : Displaying the visualization

2 Readings

  • Bar charts and plotting pivot tables
  • For reference: Seaborn

6. Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 6 - Visualizing Data

7. Assessment

    • 1 Readings
    • 1 Programming
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1 Programming

  • Homework 5 - Visualize Movie & Ratings Data

1 Readings

  • Homework 5 - Instructions

Auto Summary

Explore the fundamentals of data science with "Data Analysis Using Python." Taught by expert instructors from Coursera, this foundational course delves into essential concepts such as Data Frames, data aggregation, and visualization using Python libraries like pandas, numpy, and matplotlib. Perfect for beginners, the course spans 1020 minutes and offers a comprehensive introduction to loading, inspecting, and querying real-world data. Enroll with a Starter subscription and start your journey into the dynamic field of Data Science & AI today.

Brandon Krakowsky
Instructor

Brandon Krakowsky