À propos de ce cours : 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.
Les destinataires de ce cours : 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. The class is taught in a tutorial format using the pandas library, and only a 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.
Enseigné par : Christopher Brooks
| Informations de base | Cours 1 de 5 dans Applied Data Science with Python Specialization |
| Niveau | Intermédiaire |
| Langue | English, Sous-titres : Korean, Vietnamese, Chinese (Traditional) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Материал для самостоятельного изучения: Syllabus
- Материал для самостоятельного изучения: Help us learn more about you!
- Vidéo: Data Science
- Материал для самостоятельного изучения: 50 years of Data Science, David Donoho (optional)
- Vidéo: The Coursera Jupyter Notebook System
- Материал для самостоятельного изучения: Notice for Auditing Learners: Assignment Submission
- Блокнот: Week 1 Lectures Jupyter Notebook
- Vidéo: Python Functions
- Vidéo: Python Types and Sequences
- Vidéo: Python More on Strings
- Vidéo: Python Demonstration: Reading and Writing CSV files
- Vidéo: Python Dates and Times
- Vidéo: Advanced Python Objects, map()
- Vidéo: Advanced Python Lambda and List Comprehensions
- Vidéo: Advanced Python Demonstration: The Numerical Python Library (NumPy)
- Блокнот: Week 2 Lectures Jupyter Notebook
- Vidéo: The Series Data Structure
- Vidéo: Querying a Series
- Vidéo: The DataFrame Data Structure
- Vidéo: DataFrame Indexing and Loading
- Vidéo: Querying a DataFrame
- Vidéo: Indexing Dataframes
- Vidéo: Missing Values
- Элемент LTI: Hacked Data
- Блокнот: Assignment 2
- Блокнот: Week 3 Lectures Jupyter Notebook
- Vidéo: Pandas Idioms
- Vidéo: Group by
- Vidéo: Scales
- Vidéo: Pivot Tables
- Vidéo: Date Functionality
- Вопрос для обсуждения: Goodhart's Law
- Блокнот: Assignment 3
- Блокнот: Week 4 Lectures Jupyter Notebook
- Vidéo: Distributions
- Vidéo: More Distributions
- Vidéo: Hypothesis Testing in Python
- Элемент LTI: End of Theory
- Вопрос для обсуждения: Science Isn't Broken: p-hacking activity
- Блокнот: Assignment 4 - Project
- Материал для самостоятельного изучения: Post-course Survey
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Well designed course
Excellent basics
Learnt a lot
Very Good learning
Nice intro for Data Science with Python