Introduction to Data Science in Python

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Beschreibung

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About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  man…

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Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Data Science Python, Data Science, Python, Big Data und Data Analytics.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the 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.

Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Created by:  University of Michigan
  • Taught by:  Christopher Brooks

Basic Info Course 1 of 5 in the Applied Data Science with Python Specialization Level Intermediate Language English, Subtitles: Vietnamese How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


Week 1



In this week you'll get an introduction to the field of data science, review common Python functionality and features which 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.


11 videos, 3 readings expand


  1. Video: Introduction to Specialization
  2. Reading: Syllabus
  3. Reading: Help us learn more about you!
  4. Video: Data Science
  5. Reading: 50 years of Data Science, David Donoho (optional)
  6. Video: The Coursera Jupyter Notebook System
  7. Notebook: Week 1 Lectures Jupyter Notebook
  8. Video: Python Functions
  9. Video: Python Types and Sequences
  10. Video: Python on Strings
  11. Video: Python Demonstration: Reading and Writing CSV files
  12. Video: Python Dates and Times
  13. Video: Advanced Python Objects, map()
  14. Video: Advanced Python Lambda and List Comprehensions
  15. Video: Advanced Python Demonstration: The Numerical Python Library (NumPy)

Graded: Week One Quiz

WEEK 2


Week 2



In this week 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. The module ends with a programming assignment and a discussion question.


8 videos expand


  1. Video: Introduction
  2. Notebook: Week 2 Lectures Jupyter Notebook
  3. Video: The Series Data Structure
  4. Video: Querying a Series
  5. Video: The DataFrame Data Structure
  6. Video: DataFrame Indexing and Loading
  7. Video: Querying a DataFrame
  8. Video: Indexing Dataframes
  9. Video: Missing Values
  10. Discussion Prompt: The Ethics of Using Hacked Data
  11. Notebook: Assignment 2

Graded: Assignment 2 Submission

WEEK 3


Week 3



In this week 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.


6 videos expand


  1. Notebook: Week 3 Lectures Jupyter Notebook
  2. Video: Merging Dataframes
  3. Video: Pandas Idioms
  4. Video: Group by
  5. Video: Scales
  6. Video: Pivot Tables
  7. Video: Date Functionality
  8. Discussion Prompt: Goodhart's Law
  9. Notebook: Assignment 3

Graded: Assignment 3 Submission

WEEK 4


Week 4



In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.


4 videos, 1 reading expand


  1. Notebook: Week 4 Lectures Jupyter Notebook
  2. Video: Introduction
  3. Video: Distributions
  4. Video: Distributions
  5. Video: Hypothesis Testing in Python
  6. Discussion Prompt: The End of Theory
  7. Discussion Prompt: Science Isn't Broken: p-hacking activity
  8. Notebook: Assignment 4 - Project
  9. Reading: Post-course Survey

Graded: Assignment 4 Submission

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