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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
Introduction to Data Science in Python
proposé par
À propos de ce cours
4.5
10,051 notes
Ce que vous allez apprendre
Describe common Python functionality and features used for data science
Explain distributions, sampling, and t-tests
Query DataFrame structures for cleaning and processing
Understand techniques such as lambdas and manipulating csv files
Compétences que vous acquerrez
Python ProgrammingNumpyPandasData Cleansing
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
13 heures pour terminer
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 vidéos (Total 58 min), 4 lectures, 1 quiz
11 vidéos
Data Science7 min
The Coursera Jupyter Notebook System3 min
Python Functions8 min
Python Types and Sequences8 min
Python More on Strings3 min
Python Demonstration: Reading and Writing CSV files3 min
Python Dates and Times2 min
Advanced Python Objects, map()5 min
Advanced Python Lambda and List Comprehensions2 min
Advanced Python Demonstration: The Numerical Python Library (NumPy)7 min
4 lectures
Syllabus10 min
Help us learn more about you!10 min
50 years of Data Science, David Donoho (optional)30 min
Notice for Auditing Learners: Assignment Submission10 min
1 exercice pour s'entraîner
Week One Quiz12 min
Semaine
23 heures pour terminer
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 vidéos (Total 45 min), 2 quiz
8 vidéos
Introduction2 min
The Series Data Structure4 min
Querying a Series8 min
The DataFrame Data Structure7 min
DataFrame Indexing and Loading5 min
Querying a DataFrame5 min
Indexing Dataframes5 min
Missing Values4 min
Semaine
33 heures pour terminer
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 vidéos (Total 35 min), 1 quiz
6 vidéos
Merging Dataframes7 min
Pandas Idioms6 min
Group by6 min
Scales7 min
Pivot Tables2 min
Date Functionality5 min
Semaine
46 heures pour terminer
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 vidéos (Total 25 min), 1 lecture, 2 quiz
4 vidéos
Introduction1 min
Distributions4 min
More Distributions8 min
Hypothesis Testing in Python10 min
1 lecture
Post-course Survey10 min
4.5
2,396 avis33%
a commencé une nouvelle carrière après avoir terminé ces cours
34%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
Wow, this was amazing. Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!
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 .
À propos de Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
À propos de la Spécialisation Science des données appliquée avec Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
Foire Aux Questions
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