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Ilkay Altintas
and
Julian McAuley
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive 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/avqz1p/design
What's inside
Syllabus
Week 1: Supervised Learning & Regression
Welcome to the second course in this specialization! This week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression.
Read more
Syllabus
Week 1: Supervised Learning & Regression
Welcome to the second course in this specialization! This week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression.
Read more
Week 2: Features
This week, we will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks.
Week 3: Classification
This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
Week 4: Gradient Descent
This week, we will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow.
Final Project
In the final week of this course, you will continue building on the project from the first course of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches supervised learning and regression, which are foundational skills for data analysts
Develops skills in data manipulation and analysis, which are useful in a variety of fields
Introduces methods for building predictive models, such as K-nearest neighbors and logistic regression
Covers advanced concepts such as gradient descent, which are important for understanding machine learning algorithms
Includes a final project where learners apply the skills they have learned to a real-world dataset
Save this course
Save Design Thinking and Predictive Analytics for Data Products to your list so you can find it easily later:
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Reviews summary
Data analytics and machine learning foundation
Learners say that this course provides a solid foundation in data analytics and machine learning by taking learners from doing tasks manually to using Python libraries to do them efficiently. It teaches concepts like linear regression and logistic regression. The instructor is knowledgeable, but some mathematical concepts are not always followed up with Python implementation.
Instructor is very knowledgeable.
"While the instructor does appear to be very knowledgeable"
Provides a strong basis in data analytics and machine learning.
"Overall, a great course to give you a rock solid foundation in this field."
Not all concepts are followed up with Python implementation.
"many mathematic concepts are brought up during this course that are not always followed up with implementation in Python."
"It is a shame, though, because this course had a lot of promise."
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 Design Thinking and Predictive Analytics for Data Products with these
activities:
Review Linear Algebra
Show steps
Sharpen your understanding of linear algebra, a foundational topic for machine learning and data science, ensuring a stronger foundation for success in this course.
Browse courses on
Linear Algebra
Show steps
-
Review your notes or textbooks on linear algebra.
-
Solve practice problems to reinforce your understanding.
-
Complete an online refresher course or tutorial.
Review Data Science from Scratch: First Principles with Python
Show steps
Review the fundamentals of Python, data science, and supervised learning to strengthen your understanding of the course material.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Complete the associated exercises in the book.
-
Summarize the key concepts covered in each chapter.
Practice Data Cleaning and Manipulation
Show steps
Reinforce your data cleaning and manipulation skills by completing practice exercises to improve your proficiency in these techniques.
Browse courses on
Data Cleaning
Show steps
-
Find a publicly available dataset and import it into a Python notebook.
-
Clean the data by removing duplicates and handling missing values.
-
Manipulate the data by creating new features and performing transformations.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a Study Group
Show steps
Enhance your understanding by collaborating with peers, discussing course material, and working on problems together.
Show steps
-
Find a study group or form one with classmates.
-
Meet regularly to discuss course material and assignments.
-
Collaborate on practice problems and projects.
Follow a Tutorial on Feature Engineering
Show steps
Enhance your understanding of feature engineering by following a tutorial that provides step-by-step instructions and examples.
Browse courses on
Feature Engineering
Show steps
-
Find a comprehensive tutorial on feature engineering.
-
Work through the tutorial, implementing the techniques in a Python notebook.
-
Experiment with different feature engineering techniques and observe their impact on model performance.
Build a Predictive Model for a Real-World Problem
Show steps
Apply your knowledge of predictive modeling by building a model to solve a real-world problem, solidifying your understanding of the process and its applications.
Browse courses on
Machine Learning
Show steps
-
Identify a real-world problem that can be addressed using predictive modeling.
-
Gather and prepare a relevant dataset.
-
Build and train a predictive model using Python.
-
Evaluate the performance of the model and make necessary adjustments.
-
Write a report summarizing your findings and insights.
Contribute to an Open-Source Machine Learning Project
Show steps
Gain practical experience and deepen your understanding by contributing to an open-source machine learning project, immersing yourself in real-world applications and development processes.
Browse courses on
Open Source
Show steps
-
Implement your changes and submit a pull request.
-
Identify an open-source machine learning project that aligns with your interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify an issue or feature to work on.
-
Collaborate with other contributors to refine your work.
Review Linear Algebra
Show steps
Sharpen your understanding of linear algebra, a foundational topic for machine learning and data science, ensuring a stronger foundation for success in this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes or textbooks on linear algebra.
- Solve practice problems to reinforce your understanding.
- Complete an online refresher course or tutorial.
Review Data Science from Scratch: First Principles with Python
Show steps
Review the fundamentals of Python, data science, and supervised learning to strengthen your understanding of the course material.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Complete the associated exercises in the book.
- Summarize the key concepts covered in each chapter.
Practice Data Cleaning and Manipulation
Show steps
Reinforce your data cleaning and manipulation skills by completing practice exercises to improve your proficiency in these techniques.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find a publicly available dataset and import it into a Python notebook.
- Clean the data by removing duplicates and handling missing values.
- Manipulate the data by creating new features and performing transformations.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a Study Group
Show steps
Enhance your understanding by collaborating with peers, discussing course material, and working on problems together.
Show steps
Show steps
Show steps
- Find a study group or form one with classmates.
- Meet regularly to discuss course material and assignments.
- Collaborate on practice problems and projects.
Follow a Tutorial on Feature Engineering
Show steps
Enhance your understanding of feature engineering by following a tutorial that provides step-by-step instructions and examples.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Find a comprehensive tutorial on feature engineering.
- Work through the tutorial, implementing the techniques in a Python notebook.
- Experiment with different feature engineering techniques and observe their impact on model performance.
Build a Predictive Model for a Real-World Problem
Show steps
Apply your knowledge of predictive modeling by building a model to solve a real-world problem, solidifying your understanding of the process and its applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using predictive modeling.
- Gather and prepare a relevant dataset.
- Build and train a predictive model using Python.
- Evaluate the performance of the model and make necessary adjustments.
- Write a report summarizing your findings and insights.
Contribute to an Open-Source Machine Learning Project
Show steps
Gain practical experience and deepen your understanding by contributing to an open-source machine learning project, immersing yourself in real-world applications and development processes.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Implement your changes and submit a pull request.
- Identify an open-source machine learning project that aligns with your interests.
- Familiarize yourself with the project's codebase and documentation.
- Identify an issue or feature to work on.
- Collaborate with other contributors to refine your work.
Career center
Learners who complete Design Thinking and Predictive Analytics for Data Products will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and maintaining the machine learning models that power many of today's most popular products and services. They work with data scientists to identify the right problems to solve with machine learning, and then design and implement the models that will solve those problems. This course will provide you with the foundational knowledge and skills you need to start a career as a Machine Learning Engineer. You will learn about the different types of machine learning models, how to train and evaluate them, and how to deploy them into production.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. They work with businesses to identify the right questions to ask of their data, and then design and implement the data science projects that will answer those questions. This course will provide you with the foundational knowledge and skills you need to start a career as a Data Scientist. You will learn about the different types of data science projects, how to design and implement them, and how to communicate your findings to stakeholders.
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics and programming to analyze data and identify trends. They work with businesses to help them make better decisions by providing them with insights into their data. This course will provide you with the foundational knowledge and skills you need to start a career as a Data Analyst. You will learn about the different types of data analysis projects, how to design and implement them, and how to communicate your findings to stakeholders.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and programming to develop and implement financial models. They work with investment banks and hedge funds to help them make better investment decisions. This course will provide you with the foundational knowledge and skills you need to start a career as a Quantitative Analyst. You will learn about the different types of financial models, how to develop and implement them, and how to evaluate their performance.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to help businesses improve their operations. They work with businesses to identify areas for improvement, and then design and implement the solutions that will help them achieve their goals. This course will provide you with the foundational knowledge and skills you need to start a career as a Business Analyst. You will learn about the different types of business analysis projects, how to design and implement them, and how to communicate your findings to stakeholders.
BI Developer
BI Developer
Business intelligence (BI) Developers are responsible for developing and maintaining the business intelligence applications that help businesses make better decisions. They work with businesses to identify the right BI solutions for their needs, and then design and implement those solutions. This course will provide you with the foundational knowledge and skills you need to start a career as a BI Developer. You will learn about the different types of BI projects, how to design and implement BI applications, and how to manage BI data.
Database Administrator
Database Administrator
Database Administrators are responsible for the design, implementation, and maintenance of databases. They work with businesses to identify the right database solutions for their needs, and then design and implement those solutions. This course will provide you with the foundational knowledge and skills you need to start a career as a Database Administrator. You will learn about the different types of database administration projects, how to design and implement databases, and how to manage databases.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining the data infrastructure that powers many of today's most popular products and services. They work with businesses to identify the right data infrastructure for their needs, and then design and implement that infrastructure. This course will provide you with the foundational knowledge and skills you need to start a career as a Data Engineer. You will learn about the different types of data engineering projects, how to design and implement them, and how to manage data infrastructure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with businesses to identify the right software solutions for their needs, and then design and implement those solutions. This course will provide you with the foundational knowledge and skills you need to start a career as a Software Engineer. You will learn about the different types of software engineering projects, how to design and implement them, and how to test and debug software.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with businesses to identify the right products to develop, and then lead the team that develops and launches those products. This course will provide you with the foundational knowledge and skills you need to start a career as a Product Manager. You will learn about the different types of product management projects, how to manage a product team, and how to launch a new product.
Cloud Architect
Cloud Architect
Cloud Architects are responsible for designing and implementing cloud computing solutions. They work with businesses to identify the right cloud solutions for their needs, and then design and implement those solutions. This course will provide you with the foundational knowledge and skills you need to start a career as a Cloud Architect. You will learn about the different types of cloud computing projects, how to design and implement cloud solutions, and how to manage cloud infrastructure.
Machine Learning Expert
Machine Learning Expert
Machine learning experts use their knowledge of machine learning to develop and implement machine learning solutions for a variety of businesses. They work with businesses to identify the right machine learning solutions for their needs, and then design and implement those solutions. This course may provide you with the foundational knowledge and skills you need to become a Machine Learning Expert.
IT Manager
IT Manager
IT Managers are responsible for the planning, implementation, and management of IT systems. They work with businesses to identify the right IT solutions for their needs, and then design and implement those solutions. This course will provide you with the foundational knowledge and skills you need to start a career as an IT Manager. You will learn about the different types of IT management projects, how to plan and implement IT systems, and how to manage IT staff.
Data Privacy Analyst
Data Privacy Analyst
Data privacy analysts use their knowledge of data privacy laws and regulations to help businesses protect the privacy of their customers' data. They work with businesses to identify and mitigate data privacy risks.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with businesses to identify the right projects to undertake, and then lead the team that executes those projects. This course will provide you with the foundational knowledge and skills you need to start a career as a Project Manager. You will learn about the different types of project management projects, how to plan and execute projects, and how to close projects.
For more career information including salaries, visit:
OpenCourser.com/course/avqz1p/design
Reading list
We've selected 14 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
Design Thinking and Predictive Analytics for Data Products.
Provides a comprehensive introduction to statistical learning, covering supervised and unsupervised learning methods. It valuable resource for understanding the fundamental concepts of predictive modeling.
More advanced treatment of statistical learning, covering topics such as model selection, regularization, and ensemble methods. It useful reference for those who want to delve deeper into the theory and practice of predictive modeling.
Provides a hands-on introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for those who want to learn how to build and deploy machine learning models.
Save
Provides a comprehensive introduction to 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 comprehensive introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn how to build and deploy machine learning models in Python.
Provides a practical guide to machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn how to apply machine learning to real-world problems.
Provides a comprehensive introduction to machine learning using R. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn how to build and deploy machine learning models in R.
Provides a practical guide to machine learning for those with a hacking background. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn how to apply machine learning to real-world problems.
Provides a gentle introduction to machine learning for those with no prior experience. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn the basics of machine learning.
Save
Provides a comprehensive introduction to data mining, covering topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for those who want to learn how to extract knowledge from data.
Save
Provides a practical guide to predictive modeling, covering topics such as model selection, regularization, and ensemble methods. It valuable resource for those who want to learn how to build and deploy predictive models.
Provides a comprehensive introduction to data mining, covering topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for those who want to learn how to extract knowledge from data.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn about the latest advances in machine learning.
Save
Provides a comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn about the algorithms that power machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/avqz1p/design
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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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Português
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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 (viewing)
- Meaningful Predictive Modeling
- Basic Data Processing and Visualization
- Deploying Machine Learning Models
Good to know
Teaches supervised learning and regression, which are foundational skills for data analysts
Develops skills in data manipulation and analysis, which are useful in a variety of fields
Introduces methods for building predictive models, such as K-nearest neighbors and logistic regression
Covers advanced concepts such as gradient descent, which are important for understanding machine learning algorithms
Includes a final project where learners apply the skills they have learned to a real-world dataset
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