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Xavier Morera
In order to work with data in Python, you need to know how to get data into Python. This playbook defines data import recipes for common data import problems you’ll encounter using Python.
Read more
In order to work with data in Python, you need to know how to get data into Python. This playbook defines data import recipes for common data import problems you’ll encounter using Python.
Read more
In order to work with data in Python, you need to know how to get data into Python. This playbook defines data import recipes for common data import problems you’ll encounter using Python.
Python is one of the most powerful and widely used languages to work with data. In this course, Importing Data: Python Data Playbook, you will learn foundational knowledge and gain the ability to import data from multiple different file formats, including: text, tabular data, binary formats as well as from databases. First, you will learn how to import text and CSV files. Next, you will discover how to import data from JSON, XML, SAS, Stata, HDF5, Matlab, Pickle files, and more. Finally, you will explore how to import relational data from databases, including: SQLite, MySQL, and PostgreSQL. When you're finished with this course, you will have the skills and knowledge of importing data into Python needed to analyze, visualize, and in general work with data.
Register for this course and see more details by visiting:
OpenCourser.com/course/1psgvz/git
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What's inside
Syllabus
Course Overview
Importing Text Data into Python Using NumPy
Importing CSV Data into Python Using csv and pandas
Import Data into Python from JSON and XML Files
Read more
Syllabus
Course Overview
Importing Text Data into Python Using NumPy
Importing CSV Data into Python Using csv and pandas
Import Data into Python from JSON and XML Files
Read more
Import Data into Python from Excel Files
Import Data into Python from Common Binary Data File Formats
Import Data into Python from Relational Databases
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds foundational skills for importing data into Python, which is essential for data analysis and visualization
Provides comprehensive coverage of data import techniques, including file formats and databases
Taught by instructors with extensive experience in teaching data science and working with Python
Suitable for beginners and intermediate learners looking to enhance their Python data import skills
Course materials include hands-on labs and interactive exercises to reinforce learning
Requires students to have basic knowledge of Python and programming concepts
Save this course
Save Git: The Big Picture to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Git: The Big Picture with these
activities:
Review data types and structures
Show steps
Revisit the concepts of data types and structures to strengthen the foundation for working with imported data.
Browse courses on
Data Types
Show steps
-
Review basic data types in Python
-
Explore different data structures such as lists, tuples, and dictionaries
Review file handling basics
Show steps
Review the methods to read and write files in Python to refresh basic file handling skills.
Browse courses on
File Handling
Show steps
-
Read about 'open()' function
-
Practice reading and writing to a file
Organize notes on importing data
Show steps
Consolidate notes and materials on importing data to improve understanding and retention.
Show steps
-
Gather notes and materials from the course
-
Organize the materials into a logical structure
Two other activities
Expand to see all activities and additional details
Show all five activities
Help a peer with importing data
Show steps
Assist a fellow student with understanding the concepts of importing data.
Show steps
-
Identify a peer who needs help with importing data
-
Explain the concepts and demonstrate the techniques
-
Provide guidance and support
Create a Python module for importing data
Show steps
Develop a reusable Python module that encapsulates the functionality for importing data from various sources.
Show steps
-
Design the module's architecture and interfaces
-
Implement the import functionality for different data formats
-
Test the module thoroughly
Review data types and structures
Show steps
Revisit the concepts of data types and structures to strengthen the foundation for working with imported data.
Browse courses on
Data Types
Show steps
Show steps
Show steps
- Review basic data types in Python
- Explore different data structures such as lists, tuples, and dictionaries
Review file handling basics
Show steps
Review the methods to read and write files in Python to refresh basic file handling skills.
Browse courses on
File Handling
Show steps
Show steps
Show steps
- Read about 'open()' function
- Practice reading and writing to a file
Organize notes on importing data
Show steps
Consolidate notes and materials on importing data to improve understanding and retention.
Show steps
Show steps
Show steps
- Gather notes and materials from the course
- Organize the materials into a logical structure
Two other activities
Expand to see all activities and additional details
Show all five activities
Help a peer with importing data
Show steps
Assist a fellow student with understanding the concepts of importing data.
Show steps
Show steps
Show steps
- Identify a peer who needs help with importing data
- Explain the concepts and demonstrate the techniques
- Provide guidance and support
Create a Python module for importing data
Show steps
Develop a reusable Python module that encapsulates the functionality for importing data from various sources.
Show steps
Show steps
Show steps
- Design the module's architecture and interfaces
- Implement the import functionality for different data formats
- Test the module thoroughly
Career center
Learners who complete Git: The Big Picture will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts apply their understanding of data to help businesses with decision-making. They clean and analyze data to identify trends and insights, and they communicate their findings to stakeholders. This course provides a foundation in data import techniques, which are essential for Data Analysts who need to access and analyze data from a variety of sources.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work with Data Scientists to identify the right data and algorithms for a given problem, and they build and maintain the infrastructure that supports machine learning models. This course provides a foundation in data import techniques, which are essential for Machine Learning Engineers who need to access and analyze data from a variety of sources.
Data Scientist
Data Scientist
Data Scientists develop and apply statistical and machine learning models to solve business problems. They use data to build predictive models, identify patterns, and make recommendations. This course provides a foundation in data import techniques, which are essential for Data Scientists who need to access and analyze data from a variety of sources.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with data to create features and functionality, and they need to be able to access and analyze data from a variety of sources. This course provides a foundation in data import techniques, which are essential for Software Engineers who need to work with data.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data storage and processing. They work with data to ensure that it is clean, consistent, and accessible. This course provides a foundation in data import techniques, which are essential for Data Engineers who need to access and analyze data from a variety of sources.
Database Administrator
Database Administrator
Database Administrators maintain and optimize databases. They work with data to ensure that it is stored efficiently and securely. This course provides a foundation in data import techniques, which are essential for Database Administrators who need to import data from a variety of sources.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs, and they develop solutions that meet those needs. This course provides a foundation in data import techniques, which are essential for Business Analysts who need to access and analyze data from a variety of sources.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They analyze financial data to identify trends and opportunities, and they make recommendations to clients. This course provides a foundation in data import techniques, which are essential for Financial Analysts who need to access and analyze data from a variety of sources.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They collect and analyze data to identify trends and opportunities, and they make recommendations to businesses. This course provides a foundation in data import techniques, which are essential for Market Researchers who need to access and analyze data from a variety of sources.
Statistician
Statistician
Statisticians use data to solve problems and make decisions. They collect and analyze data to identify trends and patterns, and they develop statistical models to make predictions. This course provides a foundation in data import techniques, which are essential for Statisticians who need to access and analyze data from a variety of sources.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. They collect and analyze data to find insights, and they write articles and create visualizations to share their findings with the public. This course provides a foundation in data import techniques, which are essential for Data Journalists who need to access and analyze data from a variety of sources.
UX Researcher
UX Researcher
UX Researchers use data to improve the user experience of products and services. They collect and analyze data to understand how users interact with products and services, and they make recommendations to improve the user experience. This course provides a foundation in data import techniques, which are essential for UX Researchers who need to access and analyze data from a variety of sources.
Product Manager
Product Manager
Product Managers develop and manage products. They work with engineers, designers, and marketers to bring products to market, and they use data to track the success of products. This course provides a foundation in data import techniques, which are essential for Product Managers who need to access and analyze data from a variety of sources.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to measure the effectiveness of marketing campaigns. They collect and analyze data to track key metrics, and they make recommendations to improve the effectiveness of marketing campaigns. This course provides a foundation in data import techniques, which are essential for Marketing Analysts who need to access and analyze data from a variety of sources.
Sales Analyst
Sales Analyst
Sales Analysts use data to track and forecast sales. They collect and analyze data to identify trends and opportunities, and they make recommendations to improve sales performance. This course provides a foundation in data import techniques, which are essential for Sales Analysts who need to access and analyze data from a variety of sources.
For more career information including salaries, visit:
OpenCourser.com/course/1psgvz/git
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Git: The Big Picture.
Provides a comprehensive overview of data science with a focus on Python. It covers essential tools and techniques for data cleaning, exploration, analysis, and visualization. It valuable resource for anyone looking to gain a strong foundation in data science using Python.
Save
Provides a comprehensive and in-depth overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation.
Provides a comprehensive and practical guide to machine learning with Python. It covers a wide range of machine learning topics, including data preprocessing, model selection, and model evaluation.
Provides a comprehensive and hands-on guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning topics, including data preprocessing, model selection, and model evaluation.
Save
Provides a comprehensive and in-depth overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and computational linguistics.
Provides a comprehensive and authoritative introduction to reinforcement learning. It covers the fundamental principles and algorithms of reinforcement learning, as well as a range of applications.
Save
Provides a comprehensive and in-depth overview of generative adversarial networks (GANs). It covers the fundamental principles and algorithms of GANs, as well as a range of applications.
Provides a comprehensive and authoritative overview of statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation.
Provides a comprehensive overview of statistical learning methods, including supervised learning, unsupervised learning, and model evaluation. It valuable resource for anyone looking to gain a deeper understanding of the statistical foundations of machine learning.
Provides a comprehensive and practical guide to deep learning using Python. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Introduces the core principles and tools of data analysis in Python, using popular libraries such as Pandas, NumPy, and Jupyter. It provides hands-on examples and exercises, making it an excellent choice for beginners looking to get started with data analysis in Python.
Provides an introduction to data mining techniques and algorithms for handling large datasets. It covers topics such as data cleaning, feature engineering, cluster analysis, and classification.
Provides a practical introduction to machine learning with Python. It covers a range of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive introduction to natural language processing (NLP) using Python. It covers a range of NLP topics, including text preprocessing, text classification, and text generation.
Provides a comprehensive and practical guide to computer vision using Python. It covers a range of computer vision topics, including image processing, object detection, and image classification.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1psgvz/git
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Time
67 hours
Level
Introductory
Via
Pluralsight
Instructor
Xavier Morera
Language
English
This course belongs to a
collection
called
Managing Source Code with Git. It's comprised of seven courses:
- Getting Started with Git
- How Git Works
- Mastering Git
- Git Administration
- Git: The Big Picture (viewing)
- Working with Git Branches
- Advanced Git Techniques
Good to know
Builds foundational skills for importing data into Python, which is essential for data analysis and visualization
Provides comprehensive coverage of data import techniques, including file formats and databases
Taught by instructors with extensive experience in teaching data science and working with Python
Suitable for beginners and intermediate learners looking to enhance their Python data import skills
Course materials include hands-on labs and interactive exercises to reinforce learning
Requires students to have basic knowledge of Python and programming concepts
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