Introduction to Data Science in Python

4.5
6,223 ratings
1,708 reviews

Course 1 of 5 in the Applied Data Science with Python Specialization

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.
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Intermediate Level

Промежуточный уровень

Clock

Прибл. 18 ч. на завершение

Предполагаемая нагрузка: 6 hours/week
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English

Субтитры: English, Korean, Vietnamese, Chinese (Traditional)

Чему вы научитесь

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

Приобретаемые навыки

PandasPython ProgrammingData CleansingData Analysis
Globe

Только онлайн-курс

Начните сейчас и учитесь по собственному графику.
Intermediate Level

Промежуточный уровень

Clock

Прибл. 18 ч. на завершение

Предполагаемая нагрузка: 6 hours/week
Comment Dots

English

Субтитры: English, Korean, Vietnamese, Chinese (Traditional)

Syllabus - What you will learn from this course

1

Section
Clock
3 hours to complete

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....
Reading
11 videos (Total 58 min), 4 readings, 1 quiz
Video11 videos
Data Science7m
The Coursera Jupyter Notebook System3m
Python Functions8m
Python Types and Sequences8m
Python More on Strings3m
Python Demonstration: Reading and Writing CSV files3m
Python Dates and Times2m
Advanced Python Objects, map()5m
Advanced Python Lambda and List Comprehensions2m
Advanced Python Demonstration: The Numerical Python Library (NumPy)7m
Reading4 readings
Syllabus10m
Help us learn more about you!10m
50 years of Data Science, David Donoho (optional)30m
Notice for Auditing Learners: Assignment Submission10m
Quiz1 practice exercises
Week One Quiz12m

2

Section
Clock
3 hours to complete

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....
Reading
8 videos (Total 45 min), 2 quizzes
Video8 videos
The Series Data Structure4m
Querying a Series8m
The DataFrame Data Structure7m
DataFrame Indexing and Loading5m
Querying a DataFrame5m
Indexing Dataframes5m
Missing Values4m

3

Section
Clock
3 hours to complete

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....
Reading
6 videos (Total 35 min), 1 quiz
Video6 videos
Pandas Idioms6m
Group by6m
Scales7m
Pivot Tables2m
Date Functionality5m

4

Section
Clock
6 hours to complete

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....
Reading
4 videos (Total 25 min), 1 reading, 2 quizzes
Video4 videos
Distributions4m
More Distributions8m
Hypothesis Testing in Python10m
Reading1 readings
Post-course Survey10m
4.5
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Top Reviews

By AVJan 1st 2017

To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente

By SIMar 16th 2018

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

About University of Michigan

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Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.

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