Data Processing and Manipulation from Coursera

The "Data Processing and Manipulation" course provides students with a comprehensive understanding of various data processing and manipulation concepts and tools. Participants will learn how to handle missing values, detect outliers, perform sampling and dimension reduction, apply scaling and discretization techniques, and explore data cube and pivot table operations. This course equips students with essential skills for efficiently preparing and transforming data for analysis and decision-making.

Learning Objectives:

1. Understand the importance of data processing and manipulation in the data analysis pipeline.

2. Learn techniques to handle missing values in datasets, including imputation and exclusion strategies.

3. Identify and detect outliers to assess their impact on data analysis and decision-making.

4. Explore sampling methods and dimension reduction techniques for large datasets and high-dimensional data.

5. Apply data scaling techniques to normalize and standardize variables for meaningful comparisons.

6. Utilize discretization to transform continuous data into categorical representations, simplifying analysis.

7. Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.

8. Create pivot tables to summarize and reshape data, gaining valuable insights from complex datasets.

Throughout the course, students will actively engage in practical exercises and projects, allowing them to apply data processing and manipulation techniques to real-world datasets. By the end of the course, participants will be well-equipped to effectively prepare, clean, and transform data for subsequent analysis tasks and data-driven decision-making.

What's inside

Syllabus

Missing Values and Outliers

The "Missing Values and Outliers" week focuses on how to handle missing values and detect outliers using the Pandas library. You will learn essential techniques to identify and address missing data effectively, as well as methods to detect and manage outliers in datasets.

Data Reduction

The "Data Reduction" week focuses on how to reduce data through sampling and dimensionality reduction using the Pandas library. You will learn essential techniques to obtain manageable subsets of data while preserving meaningful information for analysis and visualization.

Scaling and Discretization

The "Scaling and Discretization" week focuses on the importance of data scaling and discretization in the data preprocessing process. You will learn why and how to perform data scaling to normalize variables and handle data with different scales. Additionally, you will explore the concept of data discretization and its application in transforming continuous data into categorical representations.

Data Warehouse

The "Data Warehouse" week focuses on the concepts and methodologies of organizing data using data cubes and pivot tables in Pandas. You will learn the importance of data warehousing for efficient data management and analysis, as well as how to construct data cubes and pivot tables to facilitate multidimensional data exploration.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops data manipulation and processing skills, which are necessary for data analysis and decision-making tasks

Provides practical exercises and projects to reinforce data manipulation and processing concepts

Taught by Di Wu, an industry expert in data manipulation and processing

Covers essential data manipulation and processing techniques used in industry

Provides a solid foundation for beginners seeking to build data manipulation and processing skills

May require prior knowledge of data analysis and manipulation concepts for optimal understanding

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 Processing and Manipulation with these activities:

Discretization Practice Exercises

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Gain hands-on experience with discretization techniques, enhancing your ability to transform continuous data into categorical representations.

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Solve practice problems on discretization using different methods.
Apply discretization techniques to real-world datasets.

Practice Missing Value Imputation Techniques

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Reinforce your understanding of various missing value imputation methods and their applicability in different scenarios.

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Explore different imputation techniques such as mean, median, and mode.
Implement these techniques on real-world datasets.
Analyze the results and compare the effectiveness of different methods.

Tutorial on Outlier Detection and Removal

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Enhance your proficiency in detecting and handling outliers in datasets, ensuring the reliability of your data analysis.

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Grasp the concepts and techniques of outlier detection.
Learn to identify different types of outliers and their potential impact.
Apply outlier removal techniques to cleanse real-world datasets.

Four other activities

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Tutorial on Data Cubing and Pivot Tables

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Familiarize yourself with the concepts of data cubing and pivot tables, enabling you to perform multidimensional data exploration and analysis effectively.

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Learn the principles of data cubing and its applications in data warehousing.
Create and manipulate pivot tables to summarize and reshape data.

Data Reduction Project: Sampling and Dimensionality Reduction

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Develop a comprehensive understanding of data reduction techniques and their applications in practical scenarios.

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Implement sampling techniques to obtain representative subsets of large datasets.
Apply dimensionality reduction methods such as PCA and LDA to reduce data dimensionality.
Analyze the results and evaluate the effectiveness of different techniques.

Data Scaling Blog Post

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Solidify your understanding of data scaling techniques and their importance in data analysis and machine learning.

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Write a comprehensive blog post explaining the concepts and methods of data scaling.
Include real-world examples to illustrate the practical applications of data scaling.

Data Manipulation Workshop

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Enhance your practical skills in data manipulation techniques, enabling you to efficiently clean, transform, and analyze data for decision-making.

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Participate in a hands-on workshop on data manipulation using tools like Pandas.
Apply data manipulation techniques to real-world datasets.

Career center

Learners who complete Data Processing and Manipulation will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will use data to solve business problems and make predictions. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Data Scientist

Data Analyst

As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Data Analyst

Statistician

As a Statistician, you will collect, analyze, and interpret data. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Statistician

Machine Learning Engineer

As a Machine Learning Engineer, you will build and maintain machine learning models. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

As a Business Analyst, you will help businesses analyze their data and make decisions. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Business Analyst

Data Engineer

As a Data Engineer, you will design and build data pipelines and systems. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

See salaries and explore the career path for Data Engineer

Operations Research Analyst

As an Operations Research Analyst, you will use mathematical and analytical techniques to solve business problems. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

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Data Architect

As a Data Architect, you will design and build data architectures. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

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Database Administrator

As a Database Administrator, you will manage and maintain databases. This course will help you build the skills you need to succeed in this role by teaching you how to handle missing values, detect outliers, and perform data reduction and manipulation techniques.

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Product Manager

As a Product Manager, you will manage the development and launch of products. This course may be useful for you if you are interested in developing data-driven products.

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Software Engineer

As a Software Engineer, you will develop software applications. This course may be useful for you if you are interested in developing data-driven applications.

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Financial Analyst

As a Financial Analyst, you will analyze financial data to make investment decisions. This course may be useful for you if you are interested in using data to make better investment decisions.

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Market Researcher

As a Market Researcher, you will collect and analyze data about markets and customers. This course may be useful for you if you are interested in using data to make better marketing decisions.

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Consultant

As a Consultant, you will advise clients on a variety of business issues. This course may be useful for you if you are interested in using data to solve business problems.

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Teacher

As a Teacher, you will teach students about a variety of subjects. This course may be useful for you if you are interested in teaching data science or data analysis.

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