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Ilkay Altintas
and
Julian McAuley
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
Read more
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
Read more
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/26xjr8/basic
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What's inside
Syllabus
Week 1: Introduction to Data Products
This week, we will go over the syllabus and set you up with the course materials and software. We will introduce you to data products and refresh your memory on Python and Jupyter notebooks.
Read more
Syllabus
Week 1: Introduction to Data Products
This week, we will go over the syllabus and set you up with the course materials and software. We will introduce you to data products and refresh your memory on Python and Jupyter notebooks.
Read more
Week 2: Reading Data in Python
This week, we will learn how to load in datasets from CSV and JSON files. We will also practice manipulating data from these datasets with basic Python commands.
Week 3: Data Processing in Python
This week, our goal is to understand how to clean up a dataset before analyzing it. We will go over how to work with different types of data, such as strings and dates.
Week 4: Python Libraries and Toolkits
In this last week, we will get a sense of common libraries in Python and how they can be useful. We will cover data visualization with numpy and MatPlotLib, and also introduce you to the basics of webscraping with urllib and BeautifulSoup.
Final Project
Create your own Jupyter notebook with a dataset of your own choosing and practice data manipulation. Show off the skills you've learned and the libraries you know about in this project. We hope you enjoyed the course, and best of luck in your future learning!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to Python fundamentals, such as data reading, processing, and visualization, which are applicable in various domains
Provides hands-on experience in working with data, strengthening learners' data manipulation skills
Familiarizes learners with various Python libraries, including NumPy and MatPlotLib, equipping them for data visualization tasks
Prepares learners for subsequent courses in the Python Data Products for Predictive Analytics specialization, providing a foundation for deeper study
Suitable for beginners seeking an introduction to data science and Python-based data manipulation
Provides a solid foundation for learners who wish to pursue further studies in data analysis and predictive modeling
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Reviews summary
Well-received data processing course
Learners say that Basic Data Processing and Visualization is a valuable course for beginners in data science, providing a solid foundation and understandable practical examples. Although some learners believe the course is too basic and lacks depth, most learners appreciate the engaging assignments and clear explanations. Students do ask for more engaging lectures. Overall, reviewers are largely positive about this course.
Assignments and projects help keep learners engaged and motivated.
"engaging assignments,quizzes and projects."
"The presentation skills are excellent"
Valuable course for those new to data processing and visualization.
"good to learn."
"Great one! Made me learn from scratch."
"nice to have something a bit more meatier"
Concepts are presented in an easy-to-understand manner.
"topics were clearly explained"
"Clear explanation and good example"
"Overall, a good course, clear presentation and explanations"
Provides practical examples and helps learners develop useful skills.
"apply yourself to this course , there's no "dirty" data you can't handle."
"Goes into great detail on ways to actually use the code"
"clear presentation and explanations"
Some learners report that the datasets used in the course are outdated.
"all of the data sets either no longer exist or are out of data"
"No clear instruction on how to download datasets"
"not very user-friendly. why they cannot link the data set directly"
Course is geared towards beginners and may lack depth for experienced learners.
"it's not a intermediate level course, it's a really basic one"
"This is not a Python introduction"
"But not for data science"
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 Basic Data Processing and Visualization with these
activities:
Review basic Python programming concepts
Show steps
Strengthen understanding of Python fundamentals essential for data manipulation.
Browse courses on
Python
Show steps
-
Revisit variables, data types, and operators
-
Recall looping and conditional statements
-
Practice writing simple functions and classes
Create a course notebook with notes and resources
Show steps
Organize and reinforce learning by compiling materials in a central location.
Show steps
-
Set up a notebook or document to store notes, code snippets, and resources
-
Regularly add and update notes from lectures and readings
-
Include helpful links to external resources and tutorials
-
Review and summarize key concepts and formulas
Read 'Python Data Science Handbook' by Jake VanderPlas
Show steps
Gain a comprehensive understanding of Python's capabilities for data science and analytics.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Review the fundamentals of Python programming
-
Explore data analysis techniques using Python libraries
-
Practice data visualization and data manipulation
-
Complete exercises and assignments to reinforce concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for data analysis
Show steps
Engage with peers to discuss concepts, exchange insights, and enhance understanding.
Browse courses on
Data Analytics
Show steps
-
Identify or form a study group with fellow students
-
Set regular meeting times and establish a study schedule
-
Prepare for meetings by reviewing materials and preparing questions
-
Take turns presenting concepts and facilitate discussions
Follow tutorials on using NumPy and Pandas
Show steps
Enhance proficiency in using these essential Python libraries for data manipulation and analysis.
Browse courses on
NumPy
Show steps
-
Identify helpful tutorials and online resources
-
Follow step-by-step instructions to perform data operations
-
Experiment with different functions and methods
-
Apply the acquired knowledge to real-world datasets
Practice loading and cleaning datasets
Show steps
Reinforce techniques for loading and cleaning datasets before analysis.
Browse courses on
Data Retrieval
Show steps
-
Load data from multiple sources, including CSV and JSON files
-
Identify and remove duplicate data points
-
Convert data types and handle missing values
Create a visual data dictionary
Show steps
Enhance understanding of dataset structure and variables by representing them visually.
Browse courses on
Data Visualization
Show steps
-
Map variables to visual elements (e.g., colors, shapes, sizes)
-
Create a legend or key explaining the visual representation
-
Use color coding or patterns to distinguish between categories and values
-
Generate a visual summary of the data distribution and relationships
Develop a data manipulation pipeline
Show steps
Build a comprehensive process for handling and transforming data in a structured and repeatable manner.
Browse courses on
Data Pipelines
Show steps
-
Define the data flow and identify the required transformations
-
Implement data cleaning, transformation, and aggregation steps
-
Set up automated testing to ensure pipeline reliability
-
Document the pipeline for easy maintenance and collaboration
Review basic Python programming concepts
Show steps
Strengthen understanding of Python fundamentals essential for data manipulation.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit variables, data types, and operators
- Recall looping and conditional statements
- Practice writing simple functions and classes
Create a course notebook with notes and resources
Show steps
Organize and reinforce learning by compiling materials in a central location.
Show steps
Show steps
Show steps
- Set up a notebook or document to store notes, code snippets, and resources
- Regularly add and update notes from lectures and readings
- Include helpful links to external resources and tutorials
- Review and summarize key concepts and formulas
Read 'Python Data Science Handbook' by Jake VanderPlas
Show steps
Gain a comprehensive understanding of Python's capabilities for data science and analytics.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Review the fundamentals of Python programming
- Explore data analysis techniques using Python libraries
- Practice data visualization and data manipulation
- Complete exercises and assignments to reinforce concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for data analysis
Show steps
Engage with peers to discuss concepts, exchange insights, and enhance understanding.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Identify or form a study group with fellow students
- Set regular meeting times and establish a study schedule
- Prepare for meetings by reviewing materials and preparing questions
- Take turns presenting concepts and facilitate discussions
Follow tutorials on using NumPy and Pandas
Show steps
Enhance proficiency in using these essential Python libraries for data manipulation and analysis.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Identify helpful tutorials and online resources
- Follow step-by-step instructions to perform data operations
- Experiment with different functions and methods
- Apply the acquired knowledge to real-world datasets
Practice loading and cleaning datasets
Show steps
Reinforce techniques for loading and cleaning datasets before analysis.
Browse courses on
Data Retrieval
Show steps
Show steps
Show steps
- Load data from multiple sources, including CSV and JSON files
- Identify and remove duplicate data points
- Convert data types and handle missing values
Create a visual data dictionary
Show steps
Enhance understanding of dataset structure and variables by representing them visually.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Map variables to visual elements (e.g., colors, shapes, sizes)
- Create a legend or key explaining the visual representation
- Use color coding or patterns to distinguish between categories and values
- Generate a visual summary of the data distribution and relationships
Develop a data manipulation pipeline
Show steps
Build a comprehensive process for handling and transforming data in a structured and repeatable manner.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Define the data flow and identify the required transformations
- Implement data cleaning, transformation, and aggregation steps
- Set up automated testing to ensure pipeline reliability
- Document the pipeline for easy maintenance and collaboration
Career center
Learners who complete Basic Data Processing and Visualization will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course provides a strong foundation in data processing and visualization, which are essential skills for Data Analysts. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Data Analysts to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns, and then use this information to make recommendations to businesses. This course provides a strong foundation in data processing and visualization, which are essential skills for Data Scientists. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Data Scientists to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure and systems that store and process data. They work with Data Scientists and Data Analysts to ensure that data is available and accessible for analysis. This course provides a strong foundation in data processing and visualization, which are essential skills for Data Engineers. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Data Engineers to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and manufacturing. This course provides a strong foundation in data processing and visualization, which are essential skills for Statisticians. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Statisticians to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They work with stakeholders to identify business problems, and then use data to analyze and solve these problems. This course provides a strong foundation in data processing and visualization, which are essential skills for Business Analysts. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Business Analysts to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including healthcare, finance, and manufacturing. This course provides a strong foundation in data processing and visualization, which are essential skills for Software Engineers to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Software Engineers to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. They work with data to ensure that it is accurate, secure, and available. This course provides a strong foundation in data processing and visualization, which are essential skills for Database Administrators to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Database Administrators to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data to create visualizations that communicate insights to stakeholders. They work with data to identify trends and patterns, and then use this information to create visualizations that are easy to understand and interpret. This course provides a strong foundation in data processing and visualization, which are essential skills for Data Visualization Specialists to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Data Visualization Specialists to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with data to identify potential security threats and vulnerabilities, and then use this information to develop and implement security measures to mitigate these threats. This course provides a strong foundation in data processing and visualization, which are essential skills for Information Security Analysts to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Information Security Analysts to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Financial Analyst
Financial Analyst
Financial Analysts use data to evaluate the financial performance of companies and make investment recommendations. They work with data to identify trends and patterns, and then use this information to make recommendations to investors. This course provides a strong foundation in data processing and visualization, which are essential skills for Financial Analysts to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Financial Analysts to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve complex business problems. They work with data to identify inefficiencies and develop solutions to improve performance. This course provides a strong foundation in data processing and visualization, which are essential skills for Operations Research Analysts to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Operations Research Analysts to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. They work with data to identify opportunities and develop marketing strategies. This course provides a strong foundation in data processing and visualization, which are essential skills for Market Researchers to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Market Researchers to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
User Experience Researcher
User Experience Researcher
User Experience Researchers use data to understand how users interact with products and services. They work with data to identify pain points and develop solutions to improve the user experience. This course provides a strong foundation in data processing and visualization, which are essential skills for User Experience Researchers to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for User Experience Researchers to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
UX Designer
UX Designer
UX Designers use data to design user interfaces that are easy to use and understand. They work with data to identify user needs and develop solutions that meet those needs. This course provides a strong foundation in data processing and visualization, which are essential skills for UX Designers to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for UX Designers to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
Data Architect
Data Architect
Data Architects design and implement the architecture for data systems. They work with data to identify data needs and develop solutions to meet those needs. This course provides a strong foundation in data processing and visualization, which are essential skills for Data Architects to have. The course covers topics such as data retrieval, processing, and visualization, which are all essential skills for Data Architects to have. Additionally, the course introduces learners to the field of data science, which is a rapidly growing field with many career opportunities.
For more career information including salaries, visit:
OpenCourser.com/course/26xjr8/basic
Reading list
We've selected 12 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
Basic Data Processing and Visualization.
Provides a comprehensive overview of data analysis in Python, covering data manipulation, cleaning, exploration, and visualization.
Provides guidance on how to choose and create effective data visualizations for different types of data and audiences.
Provides a practical introduction to data analysis with Python and Pandas, covering data manipulation, cleaning, and visualization.
Provides a broad overview of data science, including data analysis, machine learning, and big data.
Covers machine learning techniques in Python, including supervised and unsupervised learning.
Save
Comprehensive reference on deep learning, covering theory, algorithms, and applications.
Save
Provides a theoretical foundation for machine learning, including supervised and unsupervised learning.
Classic reference on statistical learning, covering supervised and unsupervised learning.
More accessible introduction to statistical learning, covering supervised and unsupervised learning.
Provides a probabilistic perspective on machine learning, covering supervised and unsupervised learning.
Provides a comprehensive overview of Bayesian data analysis, including supervised and unsupervised learning.
Save
Provides a practical overview of data mining techniques, including supervised and unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/26xjr8/basic
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Level
Intermediate
Via
Coursera
Institution
University of California San Diego
Instructors
Ilkay Altintas
Julian McAuley
Language
English
Transcripts
Español
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Deutsch
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中文
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آذربایجان دیلی
This course belongs to a
collection
called
Python Data Products for Predictive Analytics. It's comprised of four courses:
- Design Thinking and Predictive Analytics for Data Products
- Meaningful Predictive Modeling
- Basic Data Processing and Visualization (viewing)
- Deploying Machine Learning Models
Good to know
Introduces learners to Python fundamentals, such as data reading, processing, and visualization, which are applicable in various domains
Provides hands-on experience in working with data, strengthening learners' data manipulation skills
Familiarizes learners with various Python libraries, including NumPy and MatPlotLib, equipping them for data visualization tasks
Prepares learners for subsequent courses in the Python Data Products for Predictive Analytics specialization, providing a foundation for deeper study
Suitable for beginners seeking an introduction to data science and Python-based data manipulation
Provides a solid foundation for learners who wish to pursue further studies in data analysis and predictive modeling
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