Fundamental Tools of Data Wrangling from Coursera

Data wrangling is a crucial step in the data analysis process, as it involves the transformation and preparation of raw data into a suitable format for analysis. The "Fundamental Tools for Data Wrangling" course is designed to provide participants with essential skills and knowledge to effectively manipulate, clean, and analyze data. Participants will be introduced to the fundamental tools commonly used in data wrangling, including Python, data structures, NumPy, and pandas. Through hands-on exercises and practical examples, participants will gain the necessary proficiency to work with various data formats and effectively prepare data for analysis.

In this course, participants will dive into the world of data manipulation using Python as the primary programming language. They will learn about data structures, such as lists, dictionaries, and arrays, and how to use them to store and organize different types of data.

Furthermore, participants will explore the power of Python packages like random and math for generating and performing mathematical operations on data. They will also be introduced to NumPy, a powerful library for numerical computing, and learn how to efficiently work with multi-dimensional arrays and matrices.

A significant focus of the course will be on pandas, a versatile library for data manipulation and analysis. Participants will discover various techniques to clean, reshape, and aggregate data using pandas, enabling them to derive valuable insights from messy datasets.

What's inside

Syllabus

Python

This week provides an introduction to the Python programming language, covering fundamental concepts and practical applications. You will gain a solid understanding of Python's syntax and semantics, enabling you to write efficient and concise code. We will also cover essential topics such as basic variables and operations, flow control structures, functions, and the utilization of external packages to enhance Python's capabilities.

Data Structures

The "Data Structures" week provides you with a comprehensive understanding of commonly used data structures for efficient organization and manipulation of data. You will explore various data structures, including strings, lists, sets, and dictionaries. Through theoretical explanations and practical examples, you will grasp the advantages of using each data structure and learn the fundamental operations associated with them.

Numpy

The "NumPy" week serves as an introduction to the fundamental concepts and practical applications of NumPy, a powerful library for numerical computing in Python. You will gain insights into the advantages of utilizing NumPy for efficient data manipulation and mathematical operations. The week will cover the underlying data structure of NumPy arrays and guide students through basic array operations, including accessing and manipulation. Moreover, you will delve into advanced operations, such as masking and filtering, to perform complex data manipulations effectively.

Pandas

The "Pandas" week provides you with a comprehensive introduction to Pandas, a powerful and widely used library for data manipulation and analysis in Python. You will explore the advantages of using Pandas for handling structured data efficiently. The week will cover the underlying data structure of Pandas, namely DataFrames and Series, and guide you through basic data operations, including accessing and manipulation. Moreover, you will delve into advanced data manipulations, such as masking, filtering, aggregating, pivot tables, and more, to effectively analyze and transform datasets.

Case Study

The "Case Study" week offers you the opportunity to apply the knowledge you have gained throughout the course in a practical simulation case study. Through hands-on exercises and real-world scenarios, you will use Python and relevant packages to create a dummy dataset, mimicking a real dataset they might encounter in data analysis or scientific research. Throughout the case study, you will face challenges commonly encountered in real-world data analysis and will be encouraged to employ critical thinking and problem-solving skills to overcome them. This practical exercise will not only consolidate their understanding of Python and relevant packages but also foster a deeper appreciation for the importance of data preparation and analysis in various domains.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Useful for data analysis and scientific research professionals

Covers essential data wrangling techniques

Develops fundamental data manipulation skills in Python

Leverages industry-standard data analysis tools and libraries

Emphasizes hands-on learning through practical examples and exercises

Taught by instructors with expertise in data wrangling

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 Fundamental Tools of Data Wrangling with these activities:

Read 'Python Data Science Handbook'

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Expand your knowledge of Python for data science by reading a comprehensive guide.

View Python Data Science Handbook: Essential Tools... on Amazon

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Acquire the book 'Python Data Science Handbook'.
Read and study the chapters relevant to the course.
Apply the concepts and techniques discussed in the book to your data wrangling tasks.

Follow Data Wrangling Tutorials

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Gain practical skills by watching and following data wrangling tutorials.

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Search for data wrangling tutorials.
Select reputable tutorials and follow the instructions.
Experiment with the techniques demonstrated in the tutorials.

Practice Python Exercises

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Reinforce your understanding of Python fundamentals by solving practice exercises.

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Find online Python practice exercises.
Solve Python exercises independently.
Review solutions and identify areas for improvement.

Four other activities

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Work through NumPy Tutorials

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Develop proficiency in NumPy by completing hands-on tutorials.

Browse courses on NumPy

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Locate online NumPy tutorials.
Follow the tutorials and complete the exercises.
Experiment with NumPy functions and methods.

Complete Pandas Exercises

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Enhance your proficiency in Pandas by practicing with exercises.

Browse courses on Pandas

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Find online Pandas exercises.
Solve Pandas exercises independently.
Review solutions and identify areas for improvement.

Develop a Data Wrangling Workflow

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Create a data wrangling workflow that streamlines your data preparation process.

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Design a workflow that automates data cleaning and transformation.
Implement the workflow using Python.
Test and refine the workflow to optimize performance.

Build a Data Wrangling Project

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Apply your data wrangling skills by creating a project that solves a real-world problem.

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Identify a problem that requires data wrangling.
Gather and prepare the necessary data.
Apply data wrangling techniques to clean and analyze the data.
Present your findings and insights.

Career center

Learners who complete Fundamental Tools of Data Wrangling will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, and interpret data to help organizations make informed decisions. They use a variety of methods to analyze data, including descriptive statistics, inferential statistics, and predictive modeling. This course will help build a strong foundation in data manipulation and analysis, and is highly recommended for those seeking to become Statisticians.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists develop models and algorithms that help organizations make predictions based on data analysis. They typically require at least a master's degree, and may start in entry-level roles such as Data Analyst or Research Analyst prior to moving into this role. A solid foundation in data wrangling is essential for Data Scientists, as they often invest considerable time into cleaning and organizing data, and would benefit from the techniques and tools this course will teach.

See salaries and explore the career path for Data Scientist

Data Visualization Specialist

Data Visualization Specialists create visual representations of data to help organizations communicate insights more effectively. They may work for marketing agencies, consulting firms, or other organizations. This course will help build a foundation in data manipulation and analysis, which is essential for Data Visualization Specialists.

See salaries and explore the career path for Data Visualization Specialist

Business Analyst

Business Analysts use data to help organizations make informed decisions. They may work on projects such as market research, financial analysis, and operations research. This course will help build a foundation in data manipulation and analysis, which is essential for Business Analysts.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts clean and prepare big data to help organizations gain insights from their operations. They utilize a variety of programming languages and statistical methods, and often assist Data Scientists with data preparation. A course on data wrangling will help Data Analysts build a foundation for this role, and is a good precursor to more advanced courses on data science and machine learning.

See salaries and explore the career path for Data Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to help organizations improve their operations. They may work on projects such as supply chain optimization, inventory management, and transportation planning. This course will help build a foundation in data manipulation and analysis, allowing these professionals to more effectively clean and prepare data for use in their models and algorithms.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

Data Engineers develop the infrastructure and backend technologies that Data Analysts and Data Scientists utilize to perform data analysis. They design and implement data pipelines, as well as manage large-scale data storage and processing systems. As they are heavily involved in data cleaning and preparation, this course can help Data Engineers develop some of the foundational skills they need to succeed in their role.

See salaries and explore the career path for Data Engineer

Computer Systems Analyst

Computer Systems Analysts design and implement computer systems. They may work for a variety of industries, including technology, finance, and healthcare. This course may be useful for those seeking to become Computer Systems Analysts, as it provides a foundation in data wrangling, a skill that is essential for working with large and complex datasets.

See salaries and explore the career path for Computer Systems Analyst

Machine Learning Engineer

Machine Learning Engineers apply machine learning techniques to solve complex business problems. They may be involved in developing new models, or integrating machine learning solutions into existing systems. This course may be useful for those seeking to become Machine Learning Engineers, as the tools and techniques taught in the course may help with data preparation and preprocessing tasks.

See salaries and explore the career path for Machine Learning Engineer

Database Administrator

Database Administrators manage and maintain databases. They may work for a variety of industries, including technology, finance, and healthcare. This course will help build a foundation in data manipulation and analysis, which is helpful for Database Administrators who work with large and complex datasets.

See salaries and explore the career path for Database Administrator

Financial Analyst

Financial Analysts use financial data to make investment decisions. They may work for investment banks, hedge funds, or other financial institutions. A course on data wrangling may be useful for those seeking to become Financial Analysts, as it will help them develop the skills necessary to clean and prepare financial data for analysis.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers conduct research to understand consumer trends and preferences. They may work for marketing agencies, consulting firms, or other organizations. A course on data wrangling may be useful for those seeking to become Market Researchers, as it will help them develop the skills necessary to clean and prepare data for analysis.

See salaries and explore the career path for Market Researcher

Web Developer

Web Developers develop and maintain websites. They may work for a variety of industries, including technology, finance, and healthcare. This course will help build a foundation in data manipulation and analysis, which is helpful for Web Developers who work with user data and other types of web data.

See salaries and explore the career path for Web Developer

Software Engineer

Software Engineers develop and maintain software applications. They may work for a variety of industries, including technology, finance, and healthcare. This course will help build a foundation in data manipulation and analysis, which is helpful for Software Engineers who work with data-intensive applications.

See salaries and explore the career path for Software Engineer

Information Security Analyst

Information Security Analysts protect computer systems and networks from unauthorized access. They may work for a variety of industries, including technology, finance, and government. This course will help build a foundation in data manipulation and analysis, which is helpful for Information Security Analysts who work with security logs and other data sources.

See salaries and explore the career path for Information Security Analyst

Fundamental Tools of Data Wrangling

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