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Ryan Ahmed

In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the second of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.

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In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the second of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Project Overview
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the second of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops strong foundations in data analysis and manipulation for anyone, including beginners
Uses Python and Pandas, which are widely used tools in the industry for data analysis and manipulation
Involves hands-on, guided projects to enhance practical skills and understanding

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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 Mastering Data Analysis with Pandas: Learning Path Part 2 with these activities:
Read 'Python for Data Analysis'
Supplement your learning with a comprehensive guide to data analysis with Pandas. This book provides valuable insights and practical examples.
Show steps
  • Obtain a copy of 'Python for Data Analysis'.
  • Allocate time for reading and note-taking.
  • Apply the concepts presented in the book to your own practice.
Start a Side Project Using Pandas
Build practical experience by starting a side project that utilizes Pandas. This will provide a hands-on approach to reinforce your learning.
Browse courses on Data Analysis
Show steps
  • Identify a problem or opportunity that can be addressed with data analysis.
  • Research and gather relevant data.
  • Apply Pandas techniques to analyze and manipulate the data.
  • Develop a solution or product based on your analysis.
Follow Advanced Pandas Tutorial
Enhance your understanding of Pandas' advanced features by following online tutorials. This will improve your ability to handle complex data efficiently.
Browse courses on Pandas
Show steps
  • Identify reputable tutorials covering advanced Pandas concepts.
  • Set aside dedicated time to work through the tutorials.
  • Take notes and experiment with the techniques presented.
Four other activities
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Show all seven activities
Solve Pandas Coding Challenges
Reinforce your Pandas skills by solving coding challenges. This will sharpen your problem-solving abilities and solidify your understanding of data manipulation techniques.
Browse courses on Pandas
Show steps
  • Find online platforms or resources offering Pandas coding challenges.
  • Allocate time for regular practice sessions.
  • Analyze solutions and identify areas for improvement.
Join a Pandas Study Group
Collaborate with peers in a study group to enhance your understanding of Pandas. Engage in discussions, share knowledge, and work through challenges together.
Browse courses on Pandas
Show steps
  • Find or organize a study group with other Pandas learners.
  • Set regular meeting times and create a study plan.
  • Actively participate in discussions and share your insights.
Attend a Pandas Workshop
Enhance your understanding by attending a workshop focused on Pandas. Engage with experts and learn advanced techniques.
Browse courses on Pandas
Show steps
  • Find and register for a relevant Pandas workshop.
  • Actively participate in the workshop and ask questions.
  • Apply the knowledge and skills gained to your own practice.
Build a Real-World Data Analysis Project
Consolidate your learning by applying Pandas techniques to a real-world data analysis project. This hands-on experience will enhance your critical thinking and problem-solving skills.
Browse courses on Data Analysis
Show steps
  • Identify a suitable dataset and problem statement.
  • Plan your analysis approach and data manipulation strategy.
  • Implement your analysis using Pandas and Python.
  • Present your findings and insights.

Career center

Learners who complete Mastering Data Analysis with Pandas: Learning Path Part 2 will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst uses data to solve business problems and improve decision making with the use of software tools. They often gather data, analyze it, and create reports based on the findings. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather data from multiple sources, clean and prepare it, and perform statistical analysis. This course can help Data Analysts develop the skills needed to succeed in their role.
Data Scientist
A Data Scientist has skills that combine programming, mathematics, and statistics to extract knowledge and insights from data. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are foundational skills for Data Scientists. This course can help Data Scientists build a strong foundation in data analysis.
Business Analyst
A Business Analyst helps businesses improve their performance by analyzing data and making recommendations. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Business Analysts. This course can help Business Analysts develop the skills needed to succeed in their role.
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data and make predictions. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Quantitative Analysts. This course can help Quantitative Analysts develop the skills needed to succeed in their role.
Market Researcher
A Market Researcher gathers and analyzes data to understand consumer behavior and market trends. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Market Researchers. This course can help Market Researchers develop the skills needed to succeed in their role.
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Financial Analysts. This course can help Financial Analysts develop the skills needed to succeed in their role.
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to improve the efficiency of business operations. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Operations Research Analysts. This course can help Operations Research Analysts develop the skills needed to succeed in their role.
Data Engineer
A Data Engineer designs and builds systems to store, manage, and process data. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Data Engineers. This course can help Data Engineers develop the skills needed to succeed in their role.
Software Engineer
A Software Engineer designs, develops, and maintains software applications. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Software Engineers. This course can help Software Engineers develop the skills needed to succeed in their role.
Statistician
A Statistician gathers, analyzes, interprets, and presents data to help solve problems. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Statisticians. This course can help Statisticians develop the skills needed to succeed in their role.
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and maintains machine learning models. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Machine Learning Engineers. This course can help Machine Learning Engineers develop the skills needed to succeed in their role.
Data Architect
A Data Architect designs and manages the architecture of data systems. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Data Architects. This course can help Data Architects develop the skills needed to succeed in their role.
Database Administrator
A Database Administrator manages and maintains databases. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Database Administrators. This course can help Database Administrators develop the skills needed to succeed in their role.
Information Security Analyst
An Information Security Analyst protects an organization's data from unauthorized access. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Information Security Analysts. This course can help Information Security Analysts develop the skills needed to succeed in their role.
Computer Network Architect
A Computer Network Architect designs and manages the architecture of computer networks. The course, Mastering Data Analysis with Pandas: Learning Path Part 2, teaches the fundamentals of data analysis, including how to gather, clean, and analyze data, which are essential skills for Computer Network Architects. This course can help Computer Network Architects develop the skills needed to succeed in their role.

Reading list

We've selected ten 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 Mastering Data Analysis with Pandas: Learning Path Part 2.
Comprehensive guide to using Python for data analysis. It covers a wide range of topics, including data cleaning, data manipulation, and data visualization. It good choice if you want to learn more about Python or if you are new to data analysis.
Gentle introduction to data science. It covers a wide range of topics, including data cleaning, data analysis, and machine learning. It is suitable for a range of readers, from beginners to experienced data scientists.
Comprehensive guide to deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It is suitable for a range of readers, from beginners to experienced deep learning practitioners.
Comprehensive guide to natural language processing. It covers a wide range of topics, including text classification, text clustering, and machine translation. It is suitable for a range of readers, from beginners to experienced natural language processing practitioners.
Comprehensive guide to computer vision. It covers a wide range of topics, including image processing, feature detection, and object recognition. It is suitable for a range of readers, from beginners to experienced computer vision practitioners.
Comprehensive guide to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It is suitable for a range of readers, from beginners to experienced machine learners.
Comprehensive guide to deep learning for natural language processing. It covers a wide range of topics, including word embeddings, neural machine translation, and question answering. It is suitable for a range of readers, from beginners to experienced deep learning practitioners.
Comprehensive guide to pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It is suitable for a range of readers, from beginners to experienced machine learners.
Comprehensive guide to reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy optimization. It is suitable for a range of readers, from beginners to experienced reinforcement learning practitioners.
Comprehensive guide to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It is suitable for a range of readers, from beginners to experienced deep learning practitioners.

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