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Getting Started with Kaggle
Abhishek Jha
Abhishek Jha
In this guided project, you will explore Kaggle Competitions, Kaggle Datasets, Kaggle Notebooks which is a cloud-based coding environment, Kaggle Discussion forum and Kaggle Courses.
We will begin this course by creating a Kaggle account. We will then...
Read more
In this guided project, you will explore Kaggle Competitions, Kaggle Datasets, Kaggle Notebooks which is a cloud-based coding environment, Kaggle Discussion forum and Kaggle Courses.
We will begin this course by creating a Kaggle account. We will then...
Read more
In this guided project, you will explore Kaggle Competitions, Kaggle Datasets, Kaggle Notebooks which is a cloud-based coding environment, Kaggle Discussion forum and Kaggle Courses.
We will begin this course by creating a Kaggle account. We will then explore Kaggle competitions, the prize money and how to participate in them. We will focus primarily on the legendary Titanic Machine learning competition. We will explore Kaggle datasets. We will also explore Kaggle Notebooks which is a cloud-based coding environment. We will also explore the awesome “Copy and Edit” feature from Kaggle notebooks that enables us to work on and improvise on the work of others. In the final tasks, we will explore the Kaggle community discussion forum and explore the theoretical and practical sections of Kaggle courses.
By the end of this project, you will be confident in using Kaggle for your data science and machine learning needs.
Register for this course and see more details by visiting:
OpenCourser.com/course/3daldq/getting
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on real-world problems and solutions through competitions, datasets, notebooks, discussions, and courses
Provides a hands-on learning environment for practicing data science and machine learning skills
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Getting Started with Kaggle with these
activities:
Connect with Kaggle Mentors
Show steps
Gain guidance and support from experienced Kagglers who can provide personalized feedback and advice.
Browse courses on
Kaggle
Show steps
-
Create a profile on Kaggle and express your interest in mentorship.
-
Reach out to potential mentors who align with your skills and interests.
-
Set up a regular meeting schedule and discuss your progress and challenges.
Review Linear Algebra Concepts
Show steps
Strengthen your foundation in linear algebra, which is essential for many data science techniques.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of matrices, vectors, and systems of equations.
-
Practice solving linear algebra problems.
-
Apply your knowledge to data science examples.
Read 'Python for Data Analysis'
Show steps
Build a solid foundation in Python, a widely-used language in data science and machine learning.
View
Python for Data Analysis
on Amazon
Show steps
-
Read Chapters 1-3 to understand the basics of Python syntax and data structures.
-
Complete the exercises at the end of each chapter to practice what you've learned.
-
Create a small Python project to apply your knowledge.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Complete Kaggle Beginner Tutorials
Show steps
Familiarize yourself with Kaggle's platform, tools, and community resources.
Browse courses on
Kaggle
Show steps
-
Create a Kaggle account and explore the website.
-
Complete the 'Introduction to Kaggle' tutorial.
-
Try out a few beginner-friendly competitions or datasets.
Join a Kaggle Study Group
Show steps
Connect with other learners, share knowledge, and get support on Kaggle.
Browse courses on
Kaggle
Show steps
-
Find a study group relevant to your interests.
-
Participate in discussions and ask questions.
-
Collaborate on projects or challenges.
Participate in the Titanic Machine Learning Competition
Show steps
Apply your skills and knowledge to a real-world data science challenge and get feedback from experts.
Browse courses on
Machine Learning
Show steps
-
Download the Titanic dataset from Kaggle.
-
Explore the data and preprocess it for analysis.
-
Build and evaluate machine learning models to predict passenger survival.
-
Submit your predictions to the competition.
Create a Kaggle Notebook on Data Exploration
Show steps
Demonstrate your understanding of data exploration techniques by creating a shareable notebook.
Browse courses on
Kaggle
Show steps
-
Choose an interesting dataset from Kaggle.
-
Explore the data using visualizations and statistical analysis.
-
Draw insights and conclusions from your analysis.
-
Publish your notebook on Kaggle.
Build a Personal Data Science Portfolio
Show steps
Showcase your skills and knowledge by creating a portfolio of personal data science projects.
Browse courses on
Data Science
Show steps
-
Identify a problem or dataset that interests you.
-
Develop a project plan and gather the necessary data.
-
Build and evaluate a data science model.
-
Deploy your model and track its performance.
-
Document your project and share it on GitHub or Kaggle.
Volunteer at a Data Science Organization
Show steps
Gain practical experience and contribute to the data science community by volunteering your skills.
Browse courses on
Data Science
Show steps
-
Find a data science organization or project that aligns with your interests.
-
Offer your help with data analysis, modeling, or other tasks.
-
Contribute to the project and learn from experienced data scientists.
Connect with Kaggle Mentors
Show steps
Gain guidance and support from experienced Kagglers who can provide personalized feedback and advice.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Create a profile on Kaggle and express your interest in mentorship.
- Reach out to potential mentors who align with your skills and interests.
- Set up a regular meeting schedule and discuss your progress and challenges.
Review Linear Algebra Concepts
Show steps
Strengthen your foundation in linear algebra, which is essential for many data science techniques.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of matrices, vectors, and systems of equations.
- Practice solving linear algebra problems.
- Apply your knowledge to data science examples.
Read 'Python for Data Analysis'
Show steps
Build a solid foundation in Python, a widely-used language in data science and machine learning.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to understand the basics of Python syntax and data structures.
- Complete the exercises at the end of each chapter to practice what you've learned.
- Create a small Python project to apply your knowledge.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Complete Kaggle Beginner Tutorials
Show steps
Familiarize yourself with Kaggle's platform, tools, and community resources.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Create a Kaggle account and explore the website.
- Complete the 'Introduction to Kaggle' tutorial.
- Try out a few beginner-friendly competitions or datasets.
Join a Kaggle Study Group
Show steps
Connect with other learners, share knowledge, and get support on Kaggle.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a study group relevant to your interests.
- Participate in discussions and ask questions.
- Collaborate on projects or challenges.
Participate in the Titanic Machine Learning Competition
Show steps
Apply your skills and knowledge to a real-world data science challenge and get feedback from experts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Download the Titanic dataset from Kaggle.
- Explore the data and preprocess it for analysis.
- Build and evaluate machine learning models to predict passenger survival.
- Submit your predictions to the competition.
Create a Kaggle Notebook on Data Exploration
Show steps
Demonstrate your understanding of data exploration techniques by creating a shareable notebook.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Choose an interesting dataset from Kaggle.
- Explore the data using visualizations and statistical analysis.
- Draw insights and conclusions from your analysis.
- Publish your notebook on Kaggle.
Build a Personal Data Science Portfolio
Show steps
Showcase your skills and knowledge by creating a portfolio of personal data science projects.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify a problem or dataset that interests you.
- Develop a project plan and gather the necessary data.
- Build and evaluate a data science model.
- Deploy your model and track its performance.
- Document your project and share it on GitHub or Kaggle.
Volunteer at a Data Science Organization
Show steps
Gain practical experience and contribute to the data science community by volunteering your skills.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science organization or project that aligns with your interests.
- Offer your help with data analysis, modeling, or other tasks.
- Contribute to the project and learn from experienced data scientists.
Career center
Learners who complete Getting Started with Kaggle will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with data scientists and other engineers to ensure that machine learning models are accurate, efficient, and scalable. This course can help Machine Learning Engineers build a strong foundation in using Kaggle, which is a popular platform for machine learning engineers to share and collaborate on their work. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to enhance your machine learning skills and advance your career.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course can help Data Analysts build a strong foundation in using Kaggle, which is a popular platform for data analysts to share and collaborate on their work. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to enhance your data analysis skills and advance your career.
Data Scientist
Data Scientist
Data Scientists use various tools, modern technologies, and machine learning techniques to extract meaningful insights from data. They collaborate with cross-functional teams, including data engineers, statisticians, and business analysts, to find solutions that can improve business outcomes. This course can help you build a strong foundation in using Kaggle, which is a popular platform for data scientists to share and collaborate on their work. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to enhance your data science skills and advance your career.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, such as finance, healthcare, and technology. This course may be useful for Software Engineers who want to learn more about using Kaggle to enhance their software engineering skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest software engineering techniques.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make recommendations on investment decisions. They work in a variety of industries, such as banking, investment management, and insurance. This course may be useful for Financial Analysts who want to learn more about using Kaggle to enhance their financial analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest financial analysis techniques.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and make recommendations on how to improve marketing campaigns. They work in a variety of industries, such as retail, consumer goods, and technology. This course may be useful for Marketing Analysts who want to learn more about using Kaggle to enhance their marketing analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest marketing analysis techniques.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work closely with engineers, designers, and marketers to bring new products to market. This course may be useful for Product Managers who want to learn more about using Kaggle to enhance their product management skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest product management techniques.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work closely with data scientists and other engineers to ensure that data is available and accessible for analysis. This course may be useful for Data Engineers who want to learn more about using Kaggle to enhance their data engineering skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest data engineering techniques.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help people understand complex information. They work in a variety of industries, such as finance, healthcare, and marketing. This course may be useful for Data Visualization Specialists who want to learn more about using Kaggle to enhance their data visualization skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest data visualization techniques.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They make recommendations to investment managers on how to invest their money. This course may be useful for Quantitative Analysts who want to learn more about using Kaggle to enhance their data analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest financial data analysis techniques.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of industries, such as insurance, finance, and healthcare. This course may be useful for Actuaries who want to learn more about using Kaggle to enhance their data analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest risk assessment and uncertainty quantification techniques.
Data Science Manager
Data Science Manager
Data Science Managers lead and manage teams of data scientists. They are responsible for setting the vision and strategy for data science initiatives. This course may be useful for Data Science Managers who want to learn more about using Kaggle to enhance their data science management skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest data science management techniques.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work in a variety of settings, such as academia, industry, and government. This course may be useful for Machine Learning Researchers who want to learn more about using Kaggle to enhance their machine learning research skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest machine learning research topics.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and make recommendations on how to improve business operations. They work in a variety of industries, such as finance, healthcare, and retail. This course may be useful for Business Analysts who want to learn more about using Kaggle to enhance their data analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest business analysis techniques.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, such as finance, healthcare, and government. This course may be useful for Statisticians who want to learn more about using Kaggle to enhance their data analysis skills. You'll learn how to use Kaggle's competitions, datasets, notebooks, and discussion forums to stay up-to-date on the latest statistical methods and techniques.
For more career information including salaries, visit:
OpenCourser.com/course/3daldq/getting
Reading list
We've selected 11 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
Getting Started with Kaggle.
An excellent introduction to machine learning algorithms and techniques, from basic concepts to advanced models. Helps build a strong foundation for understanding the course content.
A comprehensive textbook covering a wide range of statistical learning methods, with a focus on practical implementation. Provides a solid theoretical foundation and complements the course's emphasis on practical skills.
A practical guide to applying machine learning algorithms in Python. Covers various real-world use cases and provides hands-on tutorials, reinforcing the course's focus on practical applications.
A comprehensive guide to using R for machine learning tasks. Covers various algorithms and techniques, providing a good complement to the course's focus on Kaggle competitions and datasets.
A practical guide to building and training deep learning models in Python. Provides valuable insights into the latest advancements in the field, complementing the course's exploration of Kaggle competitions and datasets.
An advanced textbook covering a wide range of statistical learning topics, including theoretical foundations and practical applications. A valuable reference for those seeking a deeper understanding of the concepts discussed in the course.
A comprehensive guide to natural language processing (NLP) in Python. Covers topics like text preprocessing, sentiment analysis, and machine translation, complementing the course's exploration of Kaggle datasets and competitions.
A comprehensive guide to machine learning in R, covering a wide range of topics from data visualization to predictive modeling. A valuable resource for those seeking to expand their knowledge beyond the course's focus on Kaggle competitions and datasets.
A collection of open-source code snippets, scripts, and notebooks from Kaggle competitions. Provides a valuable resource for exploring different approaches to data analysis and machine learning, complementing the course's practical focus.
A beginner-friendly guide to Python programming, covering basic concepts and practical applications. Provides a strong foundation for those new to programming, supporting the course's focus on cloud-based coding environments.
Save
A thought-provoking examination of the ethical and societal implications of data science. Raises important questions about the use of data in society, providing a valuable perspective beyond the course's technical focus.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3daldq/getting
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Abhishek Jha
Good to know
Focuses on real-world problems and solutions through competitions, datasets, notebooks, discussions, and courses
Provides a hands-on learning environment for practicing data science and machine learning skills
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