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Ilkay Altintas
and
Leo Porter
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
Read more
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
Read more
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:
- python
- jupyter notebooks
- pandas
- numpy
- matplotlib
- git
- and many other tools.
You will learn these tools all within the context of solving compelling data science problems.
After completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
By learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.
Register for this course and see more details by visiting:
OpenCourser.com/course/poeo5g/python
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What's inside
Learning objectives
- Basic process of data science
- Python and jupyter notebooks
- An applied understanding of how to manipulate and analyze uncurated datasets
- Basic statistical analysis and machine learning methods
- How to effectively visualize results
- Basic process of data science
- Python and jupyter notebooks
- An applied understanding of how to manipulate and analyze uncurated datasets
- Basic statistical analysis and machine learning methods
- How to effectively visualize results
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data science tools, which are in high demand by large employers
Taught by Leo Porter and Ilkay Altintas, who are experienced data scientists
Introduces learners to pandas and numpy, which are core tools for data analysis
Focuses on how to find answers within large datasets, which is a key skill for data scientists
Suitable for beginners who want to learn the basics of data science
Emphasizes data manipulation and analysis techniques, which are valuable skills for many industries
Save this course
Save Python for Data Science to your list so you can find it easily later:
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Reviews summary
Python for data science: supervised learning
Learners say that this course is an engaging introduction to data science concepts and Python tools. Students enjoyed the practical aspects of the course, such as the Jupyter notebooks and projects. While the course received positive reviews, some learners found it lacking in-depth content and assessments.
The course materials are organized clearly.
"This course is a perfect mix of coding and lectures to kick you off to a perfect start in the field of data science."
The video lectures and Jupyter notebooks make the content engaging.
"The content is excellent, and the instructors are also excellent."
Students enjoyed the hands-on experience of working with Jupyter notebooks and projects.
"The jupyter notebooks are great and I reference them even after completing the course."
"Really enjoyed the instructors as well."
"I myself am a mechanical engineer and had no idea about CS. This course however doesn't need any pre-requisites in CS to do well."
Students had mixed feelings about the peer review process.
"The best thing about the course are the jupyter notebook notes and exercises."
"The jupyter notebooks are great and I reference them even after completing the course."
"One thing I wish the instructors did was actually grade the projects instead of leaving it to peer review."
Some students found the course lacked in-depth content and assessments.
"Everything in this course about Python is too SHALLOW."
"Overall a pretty good course and intro to data science using Python."
"The grades are very easy to earn and earning a high grade doesn't necessarily mean that you learned a lot."
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 Python for Data Science with these
activities:
Review basic programming syntax
Show steps
The ability to code is a critical component of working with data. Spend time reviewing Python syntax so that your foundation is solid going into the course.
Browse courses on
Python Syntax
Show steps
-
Review variables and data types
-
Recall how to declare a function
-
Recall how to iterate through a list or dictionary
Jupyter notebook tutorials
Show steps
Jupyter notebooks is a primary tool that you will use to analyze data. Follow some tutorials or go through the user docs to become familiar with the tool.
Browse courses on
Jupyter Notebooks
Show steps
-
Find tutorials on getting started with Jupyter notebooks
-
Work through the tutorials and set up a personal notebook environment
Practice using Numpy
Show steps
Numpy is one of the essential Python libraries you will use for data manipulation and analysis.
Browse courses on
NumPy
Show steps
-
Find practice problems or tutorials on Numpy
-
Go through the exercises or problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data visualization using Matplotlib
Show steps
Matplotlib is one of the essential Python libraries you will use to create visualizations.
Browse courses on
Matplotlib
Show steps
-
Find practice problems or tutorials on Matplotlib
-
Go through the exercises or problems
Mentor other students in the course
Show steps
Teaching others can solidify your own understanding of the material.
Show steps
-
Join the course forums or discussion boards
-
Identify students who are struggling or have questions
-
Offer your help and provide guidance
Attend data science workshops and conferences
Show steps
Attending workshops and conferences can help you stay up-to-date on the latest trends and developments in data science.
Show steps
-
Find data science workshops and conferences in your area or online
-
Register for the events and attend the sessions
-
Take notes and ask questions during the presentations
Build a portfolio of data analysis projects
Show steps
Build a portfolio of data science projects to showcase your skills. This will help you demonstrate your understanding of the course topics.
Show steps
-
Find data sets online or collect your own
-
Develop a problem statement or hypothesis to investigate
-
Analyze the data and interpret the results
-
Create visualizations of your results
Contribute to open source projects in data science
Show steps
Contributing to open source projects can help you deepen your understanding of data science.
Browse courses on
Open Source
Show steps
-
Find open source projects related to data science on platforms like GitHub
-
Review the project documentation and identify ways to contribute
-
Make a pull request to the project with your contribution
Review basic programming syntax
Show steps
The ability to code is a critical component of working with data. Spend time reviewing Python syntax so that your foundation is solid going into the course.
Browse courses on
Python Syntax
Show steps
Show steps
Show steps
- Review variables and data types
- Recall how to declare a function
- Recall how to iterate through a list or dictionary
Jupyter notebook tutorials
Show steps
Jupyter notebooks is a primary tool that you will use to analyze data. Follow some tutorials or go through the user docs to become familiar with the tool.
Browse courses on
Jupyter Notebooks
Show steps
Show steps
Show steps
- Find tutorials on getting started with Jupyter notebooks
- Work through the tutorials and set up a personal notebook environment
Practice using Numpy
Show steps
Numpy is one of the essential Python libraries you will use for data manipulation and analysis.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Find practice problems or tutorials on Numpy
- Go through the exercises or problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data visualization using Matplotlib
Show steps
Matplotlib is one of the essential Python libraries you will use to create visualizations.
Browse courses on
Matplotlib
Show steps
Show steps
Show steps
- Find practice problems or tutorials on Matplotlib
- Go through the exercises or problems
Mentor other students in the course
Show steps
Teaching others can solidify your own understanding of the material.
Show steps
Show steps
Show steps
- Join the course forums or discussion boards
- Identify students who are struggling or have questions
- Offer your help and provide guidance
Attend data science workshops and conferences
Show steps
Attending workshops and conferences can help you stay up-to-date on the latest trends and developments in data science.
Show steps
Show steps
Show steps
- Find data science workshops and conferences in your area or online
- Register for the events and attend the sessions
- Take notes and ask questions during the presentations
Build a portfolio of data analysis projects
Show steps
Build a portfolio of data science projects to showcase your skills. This will help you demonstrate your understanding of the course topics.
Show steps
Show steps
Show steps
- Find data sets online or collect your own
- Develop a problem statement or hypothesis to investigate
- Analyze the data and interpret the results
- Create visualizations of your results
Contribute to open source projects in data science
Show steps
Contributing to open source projects can help you deepen your understanding of data science.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find open source projects related to data science on platforms like GitHub
- Review the project documentation and identify ways to contribute
- Make a pull request to the project with your contribution
Career center
Learners who complete Python for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for analyzing and interpreting large amounts of data to uncover hidden patterns, trends, and insights. This course gives you a firm understanding of the data science process, from data collection and preparation to analysis and visualization. With the skills gained in this course, you can kickstart or advance your journey in the exciting field of Data Science.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts have the ability to transform complex data into easy-to-understand visual representations, such as charts, graphs, and dashboards. This course provides a solid foundation in data visualization using Matplotlib, a popular Python library for creating publication-quality 2D graphics. By mastering data visualization techniques, you can effectively communicate data insights and make informed decisions.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts play a pivotal role in helping organizations make data-driven decisions. They analyze data to identify trends, forecast outcomes, and provide insights that drive business strategy. This course provides a solid foundation in Python and essential data analysis tools, allowing you to extract valuable insights from data and contribute to strategic decision-making within an organization.
Data Analyst
Data Analyst
As a Data Analyst, you use your skills in Python programming to import, clean, prepare, and analyze complex data sets and find patterns and trends. In this career, you will be working with Jupyter Notebooks which is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. This course helps you learn Python and Jupyter notebooks, so you can take your first steps into a rewarding career as a Data Analyst.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help organizations make informed decisions. This course provides a basic understanding of statistical analysis and machine learning methods, which are essential skills for Statisticians. By gaining proficiency in these areas, you can pave the way for a successful career in Statistics.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment recommendations. This course provides a solid foundation in Python and essential data analysis tools, which are widely used in the field of Quantitative Analysis. With the skills gained in this course, you can enhance your understanding of financial data and pursue a career as a Quantitative Analyst.
Actuary
Actuary
Actuaries analyze and interpret data to assess risk and uncertainty. This course provides a solid foundation in statistical analysis and machine learning methods, which are essential skills for Actuaries. By gaining proficiency in these areas, you can pave the way for a successful career in Actuarial Science.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health and disease in populations. This course provides a basic understanding of statistical analysis and machine learning methods, which are essential skills for Epidemiologists. By gaining proficiency in these areas, you can contribute to the prevention and control of diseases and improve public health.
Market Researcher
Market Researcher
Market Researchers analyze market trends, customer preferences, and competitor strategies to provide insights for businesses. This course provides a solid foundation in data analysis and visualization techniques, which are essential for Market Researchers. By mastering these skills, you can gain a competitive edge in understanding market dynamics and driving business growth.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and uncover insights that resonate with audiences. This course provides a solid foundation in data analysis and visualization techniques, which are essential for Data Journalists. By mastering these skills, you can gain a competitive edge in transforming data into compelling narratives that inform and engage the public.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use programming languages like Python and statistical techniques to design, build, and implement machine learning algorithms for data analysis and prediction. This course provides a solid foundation in Python, as well as an introduction to essential libraries like NumPy and Pandas, which are commonly used in the field of Machine Learning.
Financial Analyst
Financial Analyst
Financial Analysts use their understanding of financial data to make investment recommendations and provide guidance to businesses. This course provides a solid foundation in data analysis and visualization techniques, which are essential for Financial Analysts. By gaining proficiency in these areas, you can enhance your ability to analyze financial data and contribute to sound investment decisions.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks within organizations. This course provides a solid foundation in data analysis and visualization techniques, which are essential for Risk Analysts. By mastering these skills, you can gain a competitive edge in understanding risks and developing strategies to mitigate them.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines and infrastructure. This course provides a foundation in Python and essential data analysis tools, which are widely used in Data Engineering. With the skills gained in this course, you can lay the groundwork for a career in Data Engineering.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. While Python is not the only programming language used in software engineering, it has become increasingly popular due to its versatility and simplicity. This course provides a strong foundation in Python, making it an excellent starting point for those aspiring to become Software Engineers.
For more career information including salaries, visit:
OpenCourser.com/course/poeo5g/python
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
Python for Data Science.
Provides a comprehensive overview of Python for data science, covering topics such as data manipulation, analysis, and visualization. It valuable resource for beginners and experienced data scientists alike.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and tree-based methods. It valuable resource for those who want to learn about statistical learning and its applications.
Provides a more advanced treatment of statistical learning, covering topics such as support vector machines, kernel methods, and Bayesian methods. It valuable resource for those who want to learn about the latest advances in statistical learning.
Save
Provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for those who want to learn about the latest advances in deep learning.
Provides a hands-on introduction to Python for data science, covering topics such as data manipulation, analysis, and visualization. It valuable resource for those who want to learn how to use Python for data science.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for those who want to learn about machine learning and its applications.
Provides a comprehensive overview of natural language processing, covering topics such as text classification, text clustering, and machine translation. It valuable resource for those who want to learn about the latest advances in natural language processing.
Save
Provides a comprehensive overview of computer vision, covering topics such as image processing, object detection, and face recognition. It valuable resource for those who want to learn about the latest advances in computer vision.
Save
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and speech synthesis. It valuable resource for those who want to learn about the latest advances in speech and language processing.
Provides a practical introduction to data science for business, covering topics such as data collection, data analysis, and data visualization. It valuable resource for those who want to learn about how to use data science to improve their business.
Save
Provides a guide to the design and implementation of data science projects, covering topics such as data collection, data analysis, and data visualization. It valuable resource for those who want to learn about the best practices of data science.
Provides a gentle introduction to data science, covering topics such as data cleaning, exploration, and visualization. It valuable resource for beginners who want to learn the basics of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/poeo5g/python
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Effort
8 to 10 hours per week for 10 weeks
Level
Advanced
Via
edX
Institution
The University of California, San Diego
Instructors
Ilkay Altintas
Leo Porter
Session
March 26, 2024
- July 12, 2024
Language
English
Transcript
English
This course belongs to a
collection
called
Data Science. It's comprised of five courses:
Good to know
Develops data science tools, which are in high demand by large employers
Taught by Leo Porter and Ilkay Altintas, who are experienced data scientists
Introduces learners to pandas and numpy, which are core tools for data analysis
Focuses on how to find answers within large datasets, which is a key skill for data scientists
Suitable for beginners who want to learn the basics of data science
Emphasizes data manipulation and analysis techniques, which are valuable skills for many industries
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