Data Analysis with Python Project from Coursera

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.

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.

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

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

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 Data Analysis with Python Project with these activities:

Practice solving classification problems

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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

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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

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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

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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

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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

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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

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Practice solving dimension reduction problems

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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

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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

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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

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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

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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

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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

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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

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Analyze your data.
Interpret your results.
Define the scope of your project.
Collect and prepare your data.
Communicate your findings.

Create a data visualization

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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

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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 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Marketing 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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