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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
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Spécialisation Science des données appliquée avec PythonUniversité du MichiganÀ propos de ce cours
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Résultats de carrière des étudiants
29%
ont commencé une nouvelle carrière après avoir terminé ce cours
32%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 31 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Python ProgrammingNumpyPandasData Cleansing
Résultats de carrière des étudiants
29%
ont commencé une nouvelle carrière après avoir terminé ce cours
32%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 31 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
12 heures pour terminer
Fundamentals of Data Manipulation with Python
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.
12 heures pour terminer
12 vidéos (Total 111 min), 7 lectures, 2 quiz
12 vidéos
Introduction to the Course4 min
The Coursera Jupyter Notebook System8 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
Numerical Python Library (NumPy)32 min
Manipulating Text with Regular Expression27 min
7 lectures
Syllabus10 min
Notice for Auditing Learners: Assignment Submission10 min
Help Us Learn More About You!10 min
Week 1 Textbook Reading Assignment2 h
50 years of Data Science, David Donoho (Optional)1h 30min
Regular Expression Operations documentation1 h
Regular Expressions Debugging10 min
6 heures pour terminer
Basic Data Processing with Pandas
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.
6 heures pour terminer
9 vidéos (Total 89 min), 1 lecture, 2 quiz
9 vidéos
The Series Data Structure10 min
Querying a Series15 min
DataFrame Data Structure12 min
DataFrame Indexing and Loading8 min
Querying a DataFrame9 min
Indexing Dataframes8 min
Missing Values11 min
Example: Manipulating DataFrame8 min
1 lecture
Week 2 Reading Assignments1 h
7 heures pour terminer
More Data Processing with Pandas
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.
7 heures pour terminer
6 vidéos (Total 84 min), 1 lecture, 2 quiz
6 vidéos
Merging Dataframes15 min
Pandas Idioms15 min
Group by19 min
Scales10 min
Pivot Table9 min
Date/Time Functionality12 min
1 lecture
Week 3 Reading Assignments2 h
6 heures pour terminer
Answering Questions with Messy Data
In this week 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.
6 heures pour terminer
2 vidéos (Total 20 min), 5 lectures, 2 quiz
5 lectures
Science Isn't Broken: p-hacking45 min
Goodhart's Law (Optional)30 min
The 5 Graph Algorithms that you should know10 min
Post-course Survey10 min
Keep Learning with Michigan Online!10 min
1 exercice pour s'entraîner
Final Quiz30 min
Avis
Meilleurs avis pour INTRODUCTION TO DATA SCIENCE IN PYTHON
par PS11 août 2020
Assignments are way tougher than what is taught in the class, but they are challenging and the help in discussion forums is speechless. Without that, completion of assignments will take too much time.
par PK9 mai 2020
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
par BZ17 mai 2020
super hard course I enjoyed it but the thing that I didn't like specially when this is supposed to be the learning phase the assignments were hard and it sometimes pushes you to look for the solution
par BN1 mars 2017
Enjoyed every bit of it. The timings for the assignment could be a little bit more though. Also the expected output could be provided for validation, rather than the grader printing cryptic messages.
À propos du 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.
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