Introduction to Data Science in Python
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Introduction to Data Science in Python
This course is part of Applied Data Science with Python Specialization
Instructor: Christopher Brooks
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27,289 reviews
27,289 reviews
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
- Data Wrangling
- Data Analysis
- Data Transformation
- Data Manipulation
- Data-Driven Decision-Making
- Statistical Methods
- Programming Principles
- Data Science
- Data Import/Export
- Data Preprocessing
- Statistical Analysis
- Text Mining
- Pivot Tables And Charts
- Data Cleansing
- Scripting Languages
- Probability & Statistics
- Data Processing
Tools you'll learn
Details to know
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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.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
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
- Practice Quiz: Python Programming•10 minutes
- Practice Quiz: Numerical Python Library (NumPy)•10 minutes
- Quiz 1•30 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
- Practice Quiz: Pandas and Series Data•10 minutes
- Practice Quiz: DataFrame•10 minutes
- Quiz 2 •30 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
- Practice Quiz: More Data Processing with Pandas•10 minutes
- Quiz 3 •30 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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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 Aug 24, 2017
The course is good but the oral explanations are at times very tiresome. A more constructive approach in which the explanations are followed by step-by-step examples whould be far better.Best regards
Reviewed on Jul 17, 2022
An excellent course offered by the university of michigan which provides the basic knowledge required for starting career in data science and the concepts explianing by the proffesors were profound.
Frequently asked questions
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