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

Access provided by MAHE Dubai Campus

855,338 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

27,271 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.

27,271 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

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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 videos Total 112 minutes

Introduction to Specialization 4 minutes
Introduction to the Course 5 minutes
The Coursera Jupyter Notebook System 8 minutes
Python Functions 8 minutes
Python Types and Sequences 9 minutes
Python More on Strings 3 minutes
Python Demonstration: Reading and Writing CSV files 4 minutes
Python Dates and Times 2 minutes
Advanced Python Objects, map() 6 minutes
Advanced Python Lambda and List Comprehensions 3 minutes
Creating Array 4 minutes
Manipulating Array 6 minutes
From Arrays to Images and Back 6 minutes
Indexing, Slicing, and Iterating 6 minutes
Trying NumPy with Datasets 10 minutes
Regex Matching and Anchors 6 minutes
Patterns and Character Classes 3 minutes
Quantifiers 7 minutes
Groups 5 minutes
Advanced Assertions and Applications 7 minutes

4 readings Total 200 minutes

Syllabus 10 minutes
Pre-Course Survey 10 minutes
Regular Expression Operations Documentation 60 minutes
Module 1 Textbook Reading Assignment 120 minutes

3 assignments Total 50 minutes

Quiz 1 30 minutes
Practice Quiz: Python Programming 10 minutes
Practice Quiz: Numerical Python Library (NumPy) 10 minutes

1 programming assignment Total 180 minutes

Assignment 1 180 minutes

2 ungraded labs Total 30 minutes

Your Personal Jupyter Notebook Workspace 15 minutes
Module 1 Jupyter Notebooks 15 minutes

1 plugin Total 60 minutes

Regex Practice Session 60 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 videos Total 89 minutes

Introduction to Pandas 3 minutes
The Series Data Structure 6 minutes
Creating Pandas' Series 4 minutes
Querying a Series 4 minutes
Vectorized Operations 8 minutes
Appending Series 4 minutes
DataFrame Data Structure 13 minutes
DataFrame Indexing and Loading 9 minutes
Querying a DataFrame 10 minutes
Indexing Dataframes 8 minutes
Missing Values, Part 1 4 minutes
Missing Values, Part 2 7 minutes
Example: Manipulating DataFrame 9 minutes

1 reading Total 60 minutes

Module 2 Textbook Reading Assignment 60 minutes

3 assignments Total 50 minutes

Quiz 2 30 minutes
Practice Quiz: Pandas and Series Data 10 minutes
Practice Quiz: DataFrame 10 minutes

1 programming assignment Total 180 minutes

Assignment 2 180 minutes

1 ungraded lab Total 15 minutes

Module 2 Jupyter Notebooks 15 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 videos Total 84 minutes

Merging Dataframes 7 minutes
Handling Conflicts between Dataframes 4 minutes
Concatenating DataFrames 5 minutes
Pandas Idioms, Part 1 7 minutes
Pandas Idioms, Part 2 9 minutes
Group by, Part 1 9 minutes
Group by, Part 2 5 minutes
Group by, Part 3 7 minutes
Scales 10 minutes
Pivot Table 10 minutes
Date/Time Functionality 8 minutes
Working with Dates in a Dataframe 5 minutes

1 reading Total 120 minutes

Module 3 Textbook Reading Assignment 120 minutes

2 assignments Total 40 minutes

Quiz 3 30 minutes
Practice Quiz: More Data Processing with Pandas 10 minutes

1 programming assignment Total 180 minutes

Assignment 3 180 minutes

1 ungraded lab Total 15 minutes

Module 3 Jupyter Notebooks 15 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 videos Total 20 minutes

Basic Statistical Testing 14 minutes
Other Forms of Structured Data 6 minutes

6 readings Total 115 minutes

Science Isn't Broken: p-hacking 45 minutes
Goodhart's Law 30 minutes
The 5 Graph Algorithms that you should know 10 minutes
Post-Course Survey 10 minutes
Keep Learning with Michigan Online! 10 minutes
Progress Note from Christopher Brooks 10 minutes

1 assignment Total 30 minutes

Final Quiz 30 minutes

1 programming assignment Total 180 minutes

Assignment 4 180 minutes

1 ungraded lab Total 15 minutes

Module 4 Jupyter Notebooks 15 minutes

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Instructor

Instructor ratings

(3,904 ratings)

Christopher Brooks

15 Courses 956,683 learners

Offered by

University of Michigan

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Reviewed on Jun 17, 2018

I thought this was course was good, and was fairly challenging for an online-only course. I thought the lectures could have been a little longer to ensure proper coverage of materials and functions.

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 Apr 13, 2020

Awesome course! I haven't done any course like this. Explanations were very clear and deep, which is very helpful to learn the content. Thanks a lot to the professor and the University of Michigan.

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