University of Pennsylvania

Data Analysis Using Python

This course is part of Introduction to Programming with Python and Java Specialization

Taught in English

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Brandon Krakowsky

Instructor: Brandon Krakowsky

25,330 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(380 reviews)

|

94%

Beginner level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply basic data science techniques using Python

  • Understand and apply core concepts like Data Frames and joining data, and use data analysis libraries like pandas, numpy, and matplotlib

  • Demonstrate how to load, inspect, and query real-world data, and answer basic questions about that data

  • Analyze data further by applying learned skills in data aggregation and summarization, as well as basic data visualization

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Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(380 reviews)

|

94%

Beginner level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Introduction to Programming with Python and Java Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course

This first module provides students with an overview of loading, inspecting, and exploring data using Python's simple csv library. To get started, this module includes a brief overview of Jupyter Notebook and a concise review of basic Python, including data structures, loops, and functions. This module showcases to the students an in-depth analysis of data stored in a .csv file, including basic querying, approaches for dealing with data errors, and how to filter and sort data based on a variety of criteria.

What's included

8 videos9 readings2 quizzes1 programming assignment

In this module, students are introduced to core concepts like the Data Frame and joining data. Students will get experience using pandas, an industry-standard data analysis library, to load and query real-world data and to answer questions about that data. This module demonstrates how to do advanced filtering and indexing, slice subsets of data, restrict data attributes in query results, and do basic computations over the data. Includes how to build a simple recommendation system, and approaches for cleaning data, dealing with missing values, and creating new data.

What's included

14 videos5 readings2 quizzes2 programming assignments

This module takes data analysis a step further by providing an overview of the process of aggregating, summarizing, and visualizing data. Students are introduced to the concept of grouping and indexing data, and how to display results in a pivot table using pandas. This module also demonstrates how to prepare and visualize data using a histogram and scatterplot in Jupyter Notebook. Students will gain skills in data aggregation and summarization, as well as basic data visualization. In addition, students will get experience using data analysis libraries like numpy and matplotlib.

What's included

18 videos5 readings2 quizzes2 programming assignments

Instructor

Instructor ratings
4.5 (136 ratings)
Brandon Krakowsky
University of Pennsylvania
7 Courses116,715 learners

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4.5

380 reviews

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YH
4

Reviewed on May 22, 2023

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Reviewed on Feb 21, 2022

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5

Reviewed on Dec 19, 2022

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