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.
This course is part of the Introduction to Programming with Python and Java Specialization
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Introduction to Programming with Python and Java SpecializationUniversity of PennsylvaniaAbout this Course
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Course 2 of 4 in the
Beginner Level
High school or college math.
“Introduction to Python Programming” Coursera course or equivalent prior knowledge of introductory Python.
Approx. 17 hours to complete
English
Subtitles: English
What you will 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
Skills you will gain
Data SciencePython LibrariesPython ProgrammingData AnalysisData Visualization (DataViz)
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Beginner Level
High school or college math.
“Introduction to Python Programming” Coursera course or equivalent prior knowledge of introductory Python.
Approx. 17 hours to complete
English
Subtitles: English
Offered by
University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Syllabus - What you will learn from this course
6 hours to complete
Module 1 : Loading, Querying, & Filtering Data Using the csv Module
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.
6 hours to complete
8 videos (Total 31 min), 9 readings, 3 quizzes
5 hours to complete
Module 2 : Loading, Querying, Joining & Filtering Data Using pandas
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.
5 hours to complete
14 videos (Total 31 min), 5 readings, 4 quizzes
5 hours to complete
Module 3 : Summarizing & Visualizing Data
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.
5 hours to complete
18 videos (Total 22 min), 5 readings, 4 quizzes
Reviews
TOP REVIEWS FROM DATA ANALYSIS USING PYTHON
by LHMay 24, 2021
I'd like for this to be a little more in-depth. I had fun with the data visualisations. However, probably more of manipulating the data would be good.
by KGApr 12, 2021
Excellent course. Assignments /home work explaination need to be rethought. Special thanks to Jahnavi for helping through out the course.
by ALMar 24, 2021
Very informative course. However lessons feel more like telling than teaching. The lecturer could go more in depth.
by AMFeb 7, 2021
Videos touching same theme were too short (26 sec, 1 min, etc.) and disruptive. Wish there could be more practices.
About the Introduction to Programming with Python and Java Specialization
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today.
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