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Priya Jha
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes.
Read more
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes.
Read more
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes.
By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances.You will also be able to setup and work with Pyspark on the Google Colab environment. Additionally, you will also be able to clean and prepare data for analysis.
You should be familiar with the Python Programming language and you should have a theoretical understanding of Linear Regression algorithm.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/uxdpws/graduate
What's inside
Syllabus
Project Overview
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict student's admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression machine learning algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for learning purposes.By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances. You will also be able to set up and work with Pyspark dataframes on Google colab environment. Additionally, you will also be able to clean and prepare data for analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the Pyspark ML library, which is a valuable tool for data scientists and machine learning engineers
Suitable for beginners who want to build machine learning models with Pyspark
Uses a hands-on approach with practical examples, making it easy to understand the concepts
Provides a comprehensive overview of the linear regression algorithm and its implementation using Pyspark ML
Taught by an experienced instructor with a strong understanding of Pyspark ML
May not be suitable for advanced learners who are looking for in-depth knowledge of linear regression
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Reviews summary
Pyspark ml graduate admission prediction
Learners say this Pyspark ML course is well explained and provides a clear walkthrough of how to use Pyspark for graduate admission prediction. The course is rated highly with many learners stating that they found it to be straightforward and easy to understand. It is worth noting that one learner commented that they found the course to be more on the basic side and not quite as intermediate as advertised.
Concepts explained clearly
"This class is explained very clearly, so that I could understand how to use pyspark completely."
"Great project very clear and easy to understand."
"Very well explained."
Could go into more detail
"More details were required."
Course is more on the basic side
"I wouldn't say it is intermediate though, it was quite easy and basic."
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 Graduate Admission Prediction with Pyspark ML with these
activities:
Review Python programming basics
Show steps
Review the fundamentals of Python to ensure a strong foundation for the course.
Browse courses on
Python Basics
Show steps
-
Go through online tutorials or documentation on Python basics.
-
Complete practice exercises or quizzes on Python syntax and data structures.
Read Introduction to Statistical Learning
Show steps
Gain a foundational understanding of statistical learning concepts that are relevant to linear regression.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the relevant chapters
-
Take notes and highlight key concepts
-
Apply the concepts to the course material
Write a summary of the linear regression model
Show steps
Summarize the linear regression model to enhance understanding and recall of key concepts.
Show steps
-
Identify key concepts
-
Write a concise summary
-
Review and refine the summary
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss linear regression concepts with classmates
Show steps
Engage in peer discussions to clarify concepts, share insights, and reinforce understanding.
Browse courses on
Linear Regression
Show steps
-
Find a study partner or group
-
Prepare discussion questions
-
Meet and discuss the concepts
Solve practice problems on linear regression
Show steps
Reinforce understanding of linear regression concepts through problem-solving.
Browse courses on
Linear Regression
Show steps
-
Identify practice problems
-
Solve the problems
-
Review the solutions
Follow an online tutorial on linear regression with Pyspark
Show steps
Supplement the course material with guided tutorials to reinforce understanding of linear regression and its implementation using Pyspark.
Browse courses on
Linear Regression
Show steps
-
Identify a relevant tutorial
-
Follow the tutorial steps
-
Apply the concepts to a personal project
Build a linear regression model from scratch
Show steps
Build a linear regression model from scratch to solidify understanding of the model and its implementation.
Browse courses on
Linear Regression
Show steps
-
Gather and prepare data
-
Choose features and target variable
-
Train and test the model
-
Evaluate the model's performance
Attend a workshop on machine learning with Pyspark
Show steps
Gain practical experience with machine learning and Pyspark through a hands-on workshop.
Browse courses on
Machine Learning
Show steps
-
Identify a relevant workshop
-
Register for the workshop
-
Attend the workshop and participate actively
Review Python programming basics
Show steps
Review the fundamentals of Python to ensure a strong foundation for the course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through online tutorials or documentation on Python basics.
- Complete practice exercises or quizzes on Python syntax and data structures.
Read Introduction to Statistical Learning
Show steps
Gain a foundational understanding of statistical learning concepts that are relevant to linear regression.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters
- Take notes and highlight key concepts
- Apply the concepts to the course material
Write a summary of the linear regression model
Show steps
Summarize the linear regression model to enhance understanding and recall of key concepts.
Show steps
Show steps
Show steps
- Identify key concepts
- Write a concise summary
- Review and refine the summary
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss linear regression concepts with classmates
Show steps
Engage in peer discussions to clarify concepts, share insights, and reinforce understanding.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find a study partner or group
- Prepare discussion questions
- Meet and discuss the concepts
Solve practice problems on linear regression
Show steps
Reinforce understanding of linear regression concepts through problem-solving.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify practice problems
- Solve the problems
- Review the solutions
Follow an online tutorial on linear regression with Pyspark
Show steps
Supplement the course material with guided tutorials to reinforce understanding of linear regression and its implementation using Pyspark.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a relevant tutorial
- Follow the tutorial steps
- Apply the concepts to a personal project
Build a linear regression model from scratch
Show steps
Build a linear regression model from scratch to solidify understanding of the model and its implementation.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Gather and prepare data
- Choose features and target variable
- Train and test the model
- Evaluate the model's performance
Attend a workshop on machine learning with Pyspark
Show steps
Gain practical experience with machine learning and Pyspark through a hands-on workshop.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a relevant workshop
- Register for the workshop
- Attend the workshop and participate actively
Career center
Learners who complete Graduate Admission Prediction with Pyspark ML will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data to extract usable information that can be used to make informed business decisions. They may also develop algorithms and models to automate this process. This course can help Data Scientists build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as predicting customer churn, sales trends, and marketing campaign effectiveness.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They may also work on developing new machine learning algorithms and techniques. This course can help Machine Learning Engineers build a foundation in linear regression, which is a simple but powerful machine learning algorithm. This knowledge can be applied to a variety of tasks, such as building predictive models for customer segmentation, fraud detection, and personalized recommendations.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. They may also develop visualizations and reports to communicate their findings to stakeholders. This course can help Data Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as analyzing customer behavior, identifying sales trends, and forecasting demand.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions about a population. They may also develop statistical models to predict outcomes and make inferences. This course can help Statisticians build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as conducting research, developing marketing campaigns, and making public policy decisions.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They may also develop and implement solutions to improve efficiency and profitability. This course can help Business Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as analyzing customer behavior, identifying sales trends, and forecasting demand.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They may also develop and implement investment strategies. This course can help Financial Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as analyzing financial data, identifying investment opportunities, and making portfolio recommendations.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and make marketing decisions. They may also develop and implement marketing campaigns. This course can help Marketing Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as analyzing customer behavior, identifying marketing opportunities, and developing marketing strategies.
Product Manager
Product Manager
Product Managers lead the development and launch of new products. They may also be responsible for marketing and sales of the products. This course can help Product Managers build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as identifying customer needs, developing product specifications, and forecasting demand.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. They may also work on developing new software technologies and techniques. This course can help Software Engineers build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as building predictive models for customer churn, sales trends, and marketing campaign effectiveness.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They may also develop and implement trading strategies. This course can help Quantitative Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as analyzing financial data, identifying investment opportunities, and developing trading strategies.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They may also develop and implement insurance and pension plans. This course can help Actuaries build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as assessing insurance risk, developing pension plans, and pricing insurance products.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. They may also develop and implement optimization solutions. This course can help Operations Research Analysts build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as optimizing production schedules, routing vehicles, and managing inventory.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological and medical data. They may also develop and implement statistical models to predict outcomes and make inferences. This course can help Biostatisticians build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as conducting clinical trials, analyzing medical data, and developing public health policies.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and distribution of disease. They may also develop and implement public health interventions to prevent and control disease. This course can help Epidemiologists build a foundation in linear regression, which is a statistical technique used to predict outcomes based on historical data. This knowledge can be applied to a variety of tasks, such as identifying risk factors for disease, developing public health campaigns, and evaluating the effectiveness of public health interventions.
For more career information including salaries, visit:
OpenCourser.com/course/uxdpws/graduate
Reading list
We've selected 13 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
Graduate Admission Prediction with Pyspark ML.
Provides a detailed overview of linear regression models, including both the theory and practice of using these models for prediction and inference. It valuable resource for anyone interested in learning about linear regression models and how to use them effectively.
Provides a comprehensive overview of statistical learning methods, including both supervised and unsupervised learning. It valuable resource for anyone interested in learning about statistical learning methods and how to use them effectively.
Provides a more advanced overview of statistical learning methods, including a focus on regularization and model selection. It valuable resource for anyone interested in learning about more advanced statistical learning methods.
Provides a practical overview of machine learning methods, with a focus on using Python for data analysis and modeling. It valuable resource for anyone interested in learning about machine learning and how to use it to solve real-world problems.
Save
Provides a comprehensive overview of data mining techniques, including both supervised and unsupervised learning. It valuable resource for anyone interested in learning about data mining and how to use it to extract knowledge from data.
Provides a comprehensive overview of Python for data analysis, including both the basics of Python and more advanced topics such as data cleaning, data manipulation, and data visualization. It valuable resource for anyone interested in learning about Python and how to use it for data analysis.
Save
Provides a comprehensive overview of R for data science, including both the basics of R and more advanced topics such as data cleaning, data manipulation, and data visualization. It valuable resource for anyone interested in learning about R and how to use it for data science.
Provides a practical overview of machine learning methods, with a focus on using Python for data analysis and modeling. It valuable resource for anyone interested in learning about machine learning and how to use it to solve real-world problems.
Provides a comprehensive overview of machine learning methods, with a focus on using Python for data analysis and modeling. It valuable resource for anyone interested in learning about machine learning and how to use it to solve real-world problems.
Provides a comprehensive overview of machine learning methods, with a focus on using R for data analysis and modeling. It valuable resource for anyone interested in learning about machine learning and how to use it to solve real-world problems.
Provides a more advanced overview of machine learning methods, with a focus on Bayesian and optimization techniques. It valuable resource for anyone interested in learning about more advanced machine learning methods.
Provides a comprehensive overview of probabilistic graphical models, including both the theory and practice of using these models for machine learning. It valuable resource for anyone interested in learning about probabilistic graphical models and how to use them effectively.
Save
Provides a comprehensive overview of deep learning, including both the theory and practice of using deep learning for machine learning. It valuable resource for anyone interested in learning about deep learning and how to use it to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uxdpws/graduate
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Priya Jha
Language
English
Good to know
Teaches the Pyspark ML library, which is a valuable tool for data scientists and machine learning engineers
Suitable for beginners who want to build machine learning models with Pyspark
Uses a hands-on approach with practical examples, making it easy to understand the concepts
Provides a comprehensive overview of the linear regression algorithm and its implementation using Pyspark ML
Taught by an experienced instructor with a strong understanding of Pyspark ML
May not be suitable for advanced learners who are looking for in-depth knowledge of linear regression
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