Introduction to Data Science in Python

Introduction to Data Science in Python

This course is part of Applied Data Science with Python Specialization

Instructor: Christopher Brooks

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861,975 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

27,291 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

27,291 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

What you'll learn

Understand techniques such as lambdas and manipulating csv files
Describe common Python functionality and features used for data science
Query DataFrame structures for cleaning and processing
Explain distributions, sampling, and t-tests

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the Applied Data Science with Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

In this module, you'll get an introduction to the field of data science, review common Python functionality and features that data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.

What's included

20 videos4 readings3 assignments1 programming assignment2 ungraded labs1 plugin

20 videosTotal 112 minutes

Introduction to Specialization4 minutes
Introduction to the Course5 minutes
The Coursera Jupyter Notebook System8 minutes
Python Functions8 minutes
Python Types and Sequences9 minutes
Python More on Strings3 minutes
Python Demonstration: Reading and Writing CSV files4 minutes
Python Dates and Times2 minutes
Advanced Python Objects, map()6 minutes
Advanced Python Lambda and List Comprehensions3 minutes
Creating Array4 minutes
Manipulating Array6 minutes
From Arrays to Images and Back6 minutes
Indexing, Slicing, and Iterating6 minutes
Trying NumPy with Datasets10 minutes
Regex Matching and Anchors6 minutes
Patterns and Character Classes3 minutes
Quantifiers7 minutes
Groups5 minutes
Advanced Assertions and Applications7 minutes

4 readingsTotal 200 minutes

Syllabus10 minutes
Pre-Course Survey10 minutes
Regular Expression Operations Documentation60 minutes
Module 1 Textbook Reading Assignment120 minutes

3 assignmentsTotal 50 minutes

Quiz 130 minutes
Practice Quiz: Python Programming10 minutes
Practice Quiz: Numerical Python Library (NumPy)10 minutes

1 programming assignmentTotal 180 minutes

Assignment 1180 minutes

2 ungraded labsTotal 30 minutes

Your Personal Jupyter Notebook Workspace15 minutes
Module 1 Jupyter Notebooks15 minutes

1 pluginTotal 60 minutes

Regex Practice Session60 minutes

In this module of the course, you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

What's included

13 videos1 reading3 assignments1 programming assignment1 ungraded lab

13 videosTotal 89 minutes

Introduction to Pandas3 minutes
The Series Data Structure6 minutes
Creating Pandas' Series4 minutes
Querying a Series4 minutes
Vectorized Operations8 minutes
Appending Series4 minutes
DataFrame Data Structure13 minutes
DataFrame Indexing and Loading9 minutes
Querying a DataFrame10 minutes
Indexing Dataframes8 minutes
Missing Values, Part 14 minutes
Missing Values, Part 27 minutes
Example: Manipulating DataFrame9 minutes

1 readingTotal 60 minutes

Module 2 Textbook Reading Assignment60 minutes

3 assignmentsTotal 50 minutes

Quiz 2 30 minutes
Practice Quiz: Pandas and Series Data10 minutes
Practice Quiz: DataFrame10 minutes

1 programming assignmentTotal 180 minutes

Assignment 2180 minutes

1 ungraded labTotal 15 minutes

Module 2 Jupyter Notebooks15 minutes

In this module, you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.

What's included

12 videos1 reading2 assignments1 programming assignment1 ungraded lab

12 videosTotal 84 minutes

Merging Dataframes7 minutes
Handling Conflicts between Dataframes4 minutes
Concatenating DataFrames5 minutes
Pandas Idioms, Part 17 minutes
Pandas Idioms, Part 29 minutes
Group by, Part 19 minutes
Group by, Part 25 minutes
Group by, Part 37 minutes
Scales10 minutes
Pivot Table10 minutes
Date/Time Functionality8 minutes
Working with Dates in a Dataframe5 minutes

1 readingTotal 120 minutes

Module 3 Textbook Reading Assignment120 minutes

2 assignmentsTotal 40 minutes

Quiz 3 30 minutes
Practice Quiz: More Data Processing with Pandas10 minutes

1 programming assignmentTotal 180 minutes

Assignment 3180 minutes

1 ungraded labTotal 15 minutes

Module 3 Jupyter Notebooks15 minutes

In this final module of the course, you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.

What's included

2 videos6 readings1 assignment1 programming assignment1 ungraded lab

2 videosTotal 20 minutes

Basic Statistical Testing14 minutes
Other Forms of Structured Data6 minutes

6 readingsTotal 115 minutes

Science Isn't Broken: p-hacking45 minutes
Goodhart's Law30 minutes
The 5 Graph Algorithms that you should know10 minutes
Post-Course Survey10 minutes
Keep Learning with Michigan Online!10 minutes
Progress Note from Christopher Brooks10 minutes

1 assignmentTotal 30 minutes

Final Quiz 30 minutes

1 programming assignmentTotal 180 minutes

Assignment 4180 minutes

1 ungraded labTotal 15 minutes

Module 4 Jupyter Notebooks15 minutes

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Instructor

Instructor ratings

(3,904 ratings)

Christopher Brooks

15 Courses966,147 learners

Offered by

University of Michigan

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Showing 3 of 27291

Reviewed on Aug 15, 2020

I found this course appealing because it was more practical based.it helped me alot in getting hands on experience and most of all I have learned how to solve real world problem with python libraries

Reviewed on Jul 26, 2020

Quizzes were very challenging and interesting. I learned alot. The main drawback in this course is that the materials are insufficient to answer the assignments.And some questions were not so clear.

Reviewed on Jun 17, 2020

Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!

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