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Di Wu
The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.
Read more
The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.
Read more
The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.
By the end of this course, students will be able to:
1. Understand the fundamental concepts and methodologies of data analysis in diverse directions, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection.
2. Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
3. Apply various classification algorithms, such as Nearest Neighbors, Decision Trees, SVM, Naive Bayes, and Logistic Regression, for predictive modeling tasks.
4. Implement cross-validation and ensemble techniques to enhance the performance and generalizability of classification models.
5. Apply regression algorithms, including Simple Linear, Polynomial Linear, and Linear with regularization, to model and predict numerical outcomes.
6. Perform multivariate regression and apply cross-validation and ensemble methods in regression analysis.
7. Explore clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, to discover underlying patterns and structures in data.
8. Apply Principal Component Analysis (PCA) for dimension reduction to simplify high-dimensional data and aid in data visualization.
9. Utilize Apriori and FPGrowth algorithms to mine association rules and discover interesting item associations within transactional data.
10. Apply outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, and LOF, to identify anomalous data points and contextual outliers.
Throughout the course, students will actively engage in tutorials, practical exercises, and the data analysis project case study, gaining hands-on experience in diverse data analysis techniques. By achieving the learning objectives, participants will be well-equipped to excel in data analysis projects and make data-driven decisions in real-world scenarios.
Register for this course and see more details by visiting:
OpenCourser.com/course/b3nagq/data
What's inside
Syllabus
Data Analysis Overview
In this first week, you will gain an overview of data analysis, understanding supervised and unsupervised learning directions. You will learn how to define the scope and direction of their data analysis project effectively.
Read more
Syllabus
Data Analysis Overview
In this first week, you will gain an overview of data analysis, understanding supervised and unsupervised learning directions. You will learn how to define the scope and direction of their data analysis project effectively.
Read more
Classification Analysis
This week focuses on classification techniques, where you will explore Nearest Neighbors, Decision Trees, SVM, Naive Bayes, Logistic Regression, cross-validation, ensemble methods, and evaluation metrics.
Regression Analysis
This week you will delve into regression techniques, including Simple Linear, Polynomial Linear, Linear with regularization, multivariate regression, cross-validation, ensemble methods, and evaluation metrics.
Clustering Analysis
This week introduces clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, for unsupervised pattern discovery.
Dimension Reduction
This week will focus on dimension reduction techniques, with a particular emphasis on Principal Component Analysis (PCA).
Association Rules
This week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.
Outlier Detection
This final week focuses on outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, LOF, and contextual outliers.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Empowers students to apply skills gained in the specialization to real-world data analysis projects based on their own interests
Covers various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection
Taught by Di Wu, an accomplished instructor in the field of data analysis
Emphasizes hands-on experience through tutorials and practical exercises
Provides a comprehensive view of essential data analysis techniques and methodologies
May require additional resources for learners with limited data analysis background
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Activities
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before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Analysis with Python Project with these
activities:
Practice solving classification problems
Show steps
Solve as many classification problems as possible. This will help reinforce your understanding of the concepts and techniques involved in classification.
Browse courses on
Classification
Show steps
-
Find a dataset that contains classification problems.
-
Choose a classification algorithm to use.
-
Train the classifier on the dataset.
-
Test the classifier on a held-out dataset.
-
Evaluate the performance of the classifier.
Practice solving regression problems
Show steps
Solve as many regression problems as possible. This will help reinforce your understanding of the concepts and techniques involved in regression.
Browse courses on
Regression
Show steps
-
Find a dataset that contains regression problems.
-
Choose a regression algorithm to use.
-
Train the regressor on the dataset.
-
Test the regressor on a held-out dataset.
-
Evaluate the performance of the regressor.
Practice solving clustering problems
Show steps
Solve as many clustering problems as possible. This will help reinforce your understanding of the concepts and techniques involved in clustering.
Browse courses on
Clustering
Show steps
-
Find a dataset that contains clustering problems.
-
Choose a clustering algorithm to use.
-
Train the clustering algorithm on the dataset.
-
Test the clustering algorithm on a held-out dataset.
-
Evaluate the performance of the clustering algorithm.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice solving dimension reduction problems
Show steps
Solve as many dimension reduction problems as possible. This will help reinforce your understanding of the concepts and techniques involved in dimension reduction.
Browse courses on
Dimension Reduction
Show steps
-
Find a dataset that contains dimension reduction problems.
-
Choose a dimension reduction algorithm to use.
-
Train the dimension reduction algorithm on the dataset.
-
Test the dimension reduction algorithm on a held-out dataset.
-
Evaluate the performance of the dimension reduction algorithm.
Practice solving association rules problems
Show steps
Solve as many association rules problems as possible. This will help reinforce your understanding of the concepts and techniques involved in association rules.
Browse courses on
Association Rules
Show steps
-
Find a dataset that contains association rules problems.
-
Choose an association rules algorithm to use.
-
Train the association rules algorithm on the dataset.
-
Test the association rules algorithm on a held-out dataset.
-
Evaluate the performance of the association rules algorithm.
Practice solving outlier detection problems
Show steps
Solve as many outlier detection problems as possible. This will help reinforce your understanding of the concepts and techniques involved in outlier detection.
Browse courses on
Outlier Detection
Show steps
-
Find a dataset that contains outlier detection problems.
-
Choose an outlier detection algorithm to use.
-
Train the outlier detection algorithm on the dataset.
-
Test the outlier detection algorithm on a held-out dataset.
-
Evaluate the performance of the outlier detection algorithm.
Build a data analysis project
Show steps
Build a data analysis project from start to finish. This will allow you to apply the skills and techniques you've learned in this course to a real-world problem.
Browse courses on
Data Analysis
Show steps
-
Analyze your data.
-
Interpret your results.
-
Define the scope of your project.
-
Collect and prepare your data.
-
Communicate your findings.
Create a data visualization
Show steps
Create a data visualization that helps to communicate the results of your data analysis. This will allow you to share your findings with others in a clear and concise way.
Browse courses on
Data Visualization
Show steps
-
Choose a data visualization technique.
-
Gather the data you need.
-
Create your visualization.
-
Evaluate your visualization.
Practice solving classification problems
Show steps
Solve as many classification problems as possible. This will help reinforce your understanding of the concepts and techniques involved in classification.
Browse courses on
Classification
Show steps
Show steps
Show steps
- Find a dataset that contains classification problems.
- Choose a classification algorithm to use.
- Train the classifier on the dataset.
- Test the classifier on a held-out dataset.
- Evaluate the performance of the classifier.
Practice solving regression problems
Show steps
Solve as many regression problems as possible. This will help reinforce your understanding of the concepts and techniques involved in regression.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find a dataset that contains regression problems.
- Choose a regression algorithm to use.
- Train the regressor on the dataset.
- Test the regressor on a held-out dataset.
- Evaluate the performance of the regressor.
Practice solving clustering problems
Show steps
Solve as many clustering problems as possible. This will help reinforce your understanding of the concepts and techniques involved in clustering.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Find a dataset that contains clustering problems.
- Choose a clustering algorithm to use.
- Train the clustering algorithm on the dataset.
- Test the clustering algorithm on a held-out dataset.
- Evaluate the performance of the clustering algorithm.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice solving dimension reduction problems
Show steps
Solve as many dimension reduction problems as possible. This will help reinforce your understanding of the concepts and techniques involved in dimension reduction.
Browse courses on
Dimension Reduction
Show steps
Show steps
Show steps
- Find a dataset that contains dimension reduction problems.
- Choose a dimension reduction algorithm to use.
- Train the dimension reduction algorithm on the dataset.
- Test the dimension reduction algorithm on a held-out dataset.
- Evaluate the performance of the dimension reduction algorithm.
Practice solving association rules problems
Show steps
Solve as many association rules problems as possible. This will help reinforce your understanding of the concepts and techniques involved in association rules.
Browse courses on
Association Rules
Show steps
Show steps
Show steps
- Find a dataset that contains association rules problems.
- Choose an association rules algorithm to use.
- Train the association rules algorithm on the dataset.
- Test the association rules algorithm on a held-out dataset.
- Evaluate the performance of the association rules algorithm.
Practice solving outlier detection problems
Show steps
Solve as many outlier detection problems as possible. This will help reinforce your understanding of the concepts and techniques involved in outlier detection.
Browse courses on
Outlier Detection
Show steps
Show steps
Show steps
- Find a dataset that contains outlier detection problems.
- Choose an outlier detection algorithm to use.
- Train the outlier detection algorithm on the dataset.
- Test the outlier detection algorithm on a held-out dataset.
- Evaluate the performance of the outlier detection algorithm.
Build a data analysis project
Show steps
Build a data analysis project from start to finish. This will allow you to apply the skills and techniques you've learned in this course to a real-world problem.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Analyze your data.
- Interpret your results.
- Define the scope of your project.
- Collect and prepare your data.
- Communicate your findings.
Create a data visualization
Show steps
Create a data visualization that helps to communicate the results of your data analysis. This will allow you to share your findings with others in a clear and concise way.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a data visualization technique.
- Gather the data you need.
- Create your visualization.
- Evaluate your visualization.
Career center
Learners who complete Data Analysis with Python Project 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 identify trends and patterns. They use this information to make recommendations to businesses on how to improve their operations. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Data Analyst by providing you with a strong foundation in data analysis techniques and methodologies. This course will also teach you how to use Python, a popular programming language for data analysis, to gather, clean, and analyze data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. These models can be used to solve a variety of problems, such as predicting customer behavior, detecting fraud, and optimizing business processes. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Machine Learning Engineer by providing you with a strong foundation in machine learning algorithms and techniques. This course will also teach you how to use Python to implement machine learning models.
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve business problems. They use a variety of skills, including data analysis, machine learning, and statistics, to identify trends and patterns in data. They then use this information to make recommendations to businesses on how to improve their operations. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Data Scientist by providing you with a strong foundation in data analysis, machine learning, and statistics. This course will also teach you how to use Python to gather, clean, and analyze data.
Business Analyst
Business Analyst
Business Analysts are responsible for identifying and solving business problems. They use a variety of skills, including data analysis, process improvement, and communication, to gather and analyze data on business processes. They then use this information to make recommendations to businesses on how to improve their operations. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Business Analyst by providing you with a strong foundation in data analysis and process improvement. This course will also teach you how to use Python to gather and analyze data on business processes.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make recommendations to businesses and investors. They use a variety of skills, including data analysis, financial modeling, and valuation, to assess the financial health of companies and make investment recommendations. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Financial Analyst by providing you with a strong foundation in data analysis and financial modeling. This course will also teach you how to use Python to gather and analyze financial data.
Marketing Analyst
Marketing Analyst
Marketing Analysts are responsible for analyzing marketing data to make recommendations to businesses on how to improve their marketing campaigns. They use a variety of skills, including data analysis, market research, and campaign management, to track the performance of marketing campaigns and make recommendations on how to improve them. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Marketing Analyst by providing you with a strong foundation in data analysis and market research. This course will also teach you how to use Python to gather and analyze marketing data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve business problems. They use a variety of skills, including data analysis, optimization, and simulation, to improve the efficiency and effectiveness of business processes. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Operations Research Analyst by providing you with a strong foundation in data analysis and optimization. This course will also teach you how to use Python to implement operations research models.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use a variety of skills, including data analysis, probability, and inference, to draw conclusions from data. The 'Data Analysis with Python Project' course can help you develop the skills you need to become a successful Statistician by providing you with a strong foundation in data analysis and probability. This course will also teach you how to use Python to gather and analyze data.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They use a variety of skills, including data analysis, database design, and performance tuning, to ensure that databases are running efficiently and effectively. The 'Data Analysis with Python Project' course may be useful for Database Administrators by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from databases.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and testing software applications. They use a variety of skills, including data analysis, software design, and programming, to create software that meets the needs of users. The 'Data Analysis with Python Project' course may be useful for Software Engineers by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from software applications.
Web Analyst
Web Analyst
Web Analysts are responsible for analyzing website data to make recommendations to businesses on how to improve their websites. They use a variety of skills, including data analysis, web traffic analysis, and conversion optimization, to track the performance of websites and make recommendations on how to improve them. The 'Data Analysis with Python Project' course may be useful for Web Analysts by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from websites.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They use a variety of skills, including data analysis, project management, and risk management, to ensure that projects are completed on time, within budget, and to the required quality standards. The 'Data Analysis with Python Project' course may be useful for Project Managers by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from projects.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. They use a variety of skills, including data analysis, market research, and product management, to identify market opportunities, develop product specifications, and launch new products. The 'Data Analysis with Python Project' course may be useful for Product Managers by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from product launches.
Consultant
Consultant
Consultants are responsible for providing advice and support to businesses on a variety of topics. They use a variety of skills, including data analysis, problem solving, and communication, to help businesses improve their performance. The 'Data Analysis with Python Project' course may be useful for Consultants by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from clients.
Teacher
Teacher
Teachers are responsible for educating students in a variety of subjects. They use a variety of skills, including data analysis, curriculum development, and classroom management, to create a positive learning environment for students. The 'Data Analysis with Python Project' course may be useful for Teachers by providing them with a strong foundation in data analysis. This course will also teach them how to use Python to gather and analyze data from students.
For more career information including salaries, visit:
OpenCourser.com/course/b3nagq/data
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
Data Analysis with Python Project .
This beginner-friendly book provides a comprehensive overview of data analysis using Python. It covers topics such as data wrangling, exploration, visualization, and modeling.
Valuable resource for those who want to learn the fundamentals of data analysis in Python. It covers topics such as data structures, data manipulation, and visualization.
Provides a comprehensive introduction to machine learning in Python. It covers a wide range of topics, including supervised and unsupervised learning, model evaluation, and deployment.
Must-read for those who want to learn about deep learning in Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Valuable resource for those who want to learn about the latest trends in data science. It covers topics such as big data, machine learning, and deep learning.
Provides a comprehensive overview of advanced data analysis techniques in Python. It covers topics such as time series analysis, natural language processing, and image analysis.
Provides a practical guide to machine learning in Python using popular libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of natural language processing in Python. It covers topics such as text classification, text summarization, and machine translation.
Provides a comprehensive overview of feature engineering for machine learning. It covers topics such as feature selection, feature extraction, and feature transformation.
Provides a comprehensive overview of machine learning for text. It covers topics such as text classification, text summarization, and machine translation.
Provides a comprehensive overview of machine learning in R. It covers a wide range of topics, including supervised and unsupervised learning, model evaluation, and deployment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b3nagq/data
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Effort
5-7 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Di Wu
Language
English
This course belongs to a
collection
called
Data Analysis with Python. It's comprised of five courses:
- Association Rules Analysis
- Data Analysis with Python Project (viewing)
- Classification Analysis
- Clustering Analysis
- Regression Analysis
Good to know
Empowers students to apply skills gained in the specialization to real-world data analysis projects based on their own interests
Covers various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection
Taught by Di Wu, an accomplished instructor in the field of data analysis
Emphasizes hands-on experience through tutorials and practical exercises
Provides a comprehensive view of essential data analysis techniques and methodologies
May require additional resources for learners with limited data analysis background
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