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Ahmad Varasteh

This Guided Project, Exploratory and Confirmatory Data Analysis using python, is for those who want to learn about different methods of data analysis. In this 2-hour-long project-based course, you will understand and apply Exploratory Data Analysis, build different Data visualizations, apply different exploration techniques based on the data at hand and define and understand the concept of Confirmatory Data Analysis.

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This Guided Project, Exploratory and Confirmatory Data Analysis using python, is for those who want to learn about different methods of data analysis. In this 2-hour-long project-based course, you will understand and apply Exploratory Data Analysis, build different Data visualizations, apply different exploration techniques based on the data at hand and define and understand the concept of Confirmatory Data Analysis.

This project is unique because you will learn how and where to start your data exploration. You will also learn how to implement different data visualizations using python and when to use them. To be successful in this project, you will need to be experienced in python programming language and working with jupyter notebook environment.

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Syllabus

Project Overview
This Guided Project, Exploratory and Confirmatory Data Analysis using python are for those who want to learn about different methods of data analysis. In this 2-hour-long project-based course, you will understand and apply Exploratory Data Analysis, build different Data visualizations, apply different exploration techniques based on the data at hand and define and apply the concept of Confirmatory Data Analysis. This project is unique because you will learn how and where to start your data exploration. You will also learn how to implement different data visualizations using python and when to use them. To be successful in this project, you will need to be experienced working with Pandas and Plotly module in python and jupyter notebook environment. Let's get started!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops foundational concepts and skills necessary for identifying skills gaps and solving analytical problems using python
Teaches data visualization techniques using plotly, which are essential for presenting and understanding data insights
Provides hands-on experience through a project-based approach, allowing learners to apply concepts and techniques
Suitable for individuals with prior experience in python programming and Jupyter Notebook environment

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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 Exploratory vs Confirmatory data analysis using Python with these activities:
Book Review: Python Data Science Handbook
Reading this book will provide you with a comprehensive overview of the Python data science ecosystem and best practices for data analysis.
Show steps
  • Read the book and take notes.
  • Discuss the book with classmates or colleagues.
  • Apply the concepts from the book to your own data analysis projects.
Tutorial: Exploratory Data Analysis Techniques
By following this tutorial, you will learn various exploratory data analysis techniques that will help you understand your data better.
Browse courses on Exploratory Data Analysis
Show steps
  • Watch the tutorial videos.
  • Complete the practice exercises.
  • Apply the techniques to your own data analysis projects.
Tutorial: Pandas and Jupyter Notebook Environment
Following this tutorial will give you a solid foundation in using Pandas and Jupyter Notebook, which are essential tools for data analysis in Python.
Browse courses on Pandas
Show steps
  • Watch the tutorial videos.
  • Complete the practice exercises.
  • Install Jupyter Notebook and Pandas on your computer.
  • Try out the examples in the tutorials.
Four other activities
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Workshop: Machine Learning for Beginners
Attending this workshop will provide you with a solid foundation in the basics of machine learning, which is a useful skill for data analysts.
Browse courses on Supervised Learning
Show steps
  • Attend the workshop and listen to the lectures.
  • Complete the practice exercises.
  • Ask questions and get help from the instructor.
Practice Data Visualization with Plotly
Completing these practice drills will improve your ability to effectively visualize data using Plotly, which is a valuable skill for data analysts.
Browse courses on Plotly
Show steps
  • Complete the practice exercises in the Plotly documentation.
  • Find a dataset and create several different types of visualizations.
  • Share your visualizations with others and get feedback.
Attend a Data Science Meetup
Attending a data science meetup will allow you to connect with other professionals in the field and learn about new trends and technologies.
Show steps
  • Find a data science meetup in your area.
  • Attend the meetup and introduce yourself to others.
  • Join in on the discussions and ask questions.
Volunteer at a Data Science Organization
Volunteering at a data science organization will give you practical experience and allow you to contribute to the community.
Show steps
  • Find a data science organization that you are interested in volunteering for.
  • Contact the organization and inquire about volunteer opportunities.
  • Attend volunteer orientation and training.
  • Complete your volunteer hours.

Career center

Learners who complete Exploratory vs Confirmatory data analysis using Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help Data Scientists build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Scientists uncover trends, identify patterns, and make more informed decisions.
Machine Learning Engineer
Machine Learning Engineers use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Machine Learning Engineers build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Machine Learning Engineers uncover trends, identify patterns, and make more informed decisions.
Business Analyst
Business Analysts use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Business Analysts build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Business Analysts uncover trends, identify patterns, and make more informed decisions.
Statistician
Statisticians use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Statisticians build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Statisticians uncover trends, identify patterns, and make more informed decisions.
Data Architect
Data Architects use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Data Architects build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Architects uncover trends, identify patterns, and make more informed decisions.
Data Engineer
Data Engineers use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Data Engineers build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Engineers uncover trends, identify patterns, and make more informed decisions.
Data Visualization Specialist
Data Visualization Specialists use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Data Visualization Specialists build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Visualization Specialists uncover trends, identify patterns, and make more informed decisions.
Quantitative Analyst
Quantitative Analysts use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Quantitative Analysts build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Quantitative Analysts uncover trends, identify patterns, and make more informed decisions.
Data Science Manager
Data Science Managers use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Data Science Managers build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Science Managers uncover trends, identify patterns, and make more informed decisions.
Data Analyst
Data Analysts use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Data Analysts build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Data Analysts uncover trends, identify patterns, and make more informed decisions.
Risk Analyst
Risk Analysts use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Risk Analysts build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Risk Analysts uncover trends, identify patterns, and make more informed decisions.
Software Engineer
Software Engineers use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help aspiring Software Engineers build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Software Engineers uncover trends, identify patterns, and make more informed decisions.
Product Manager
Product Managers use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help aspiring Product Managers build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Product Managers uncover trends, identify patterns, and make more informed decisions.
Consultant
Consultants use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help aspiring Consultants build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Consultants uncover trends, identify patterns, and make more informed decisions.
Financial Analyst
Financial Analysts use their programming skills to analyze data, develop and implement data-driven solutions, and draw meaningful conclusions from large amounts of data. This course can help budding Financial Analysts build a foundation in data exploration and visualization techniques. Understanding exploratory and confirmatory data analysis can help Financial Analysts uncover trends, identify patterns, and make more informed decisions.

Reading list

We've selected 14 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 Exploratory vs Confirmatory data analysis using Python.
Provides a comprehensive overview of Python for data analysis. It covers topics such as data structures, data manipulation, and data visualization.
Provides a comprehensive overview of data science for business. It covers topics such as data mining, machine learning, and data visualization.
Provides a comprehensive overview of statistical modeling. It covers topics such as Bayesian statistics, frequentist statistics, and model selection.
Provides a comprehensive overview of statistical learning. It covers topics such as supervised learning, unsupervised learning, and ensemble methods.
Provides a comprehensive overview of data mining techniques. It covers topics such as data preprocessing, feature selection, and classification.
Provides a comprehensive overview of machine learning techniques. It covers topics such as supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive overview of deep learning techniques. It covers topics such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Provides a comprehensive overview of natural language processing techniques. It covers topics such as text preprocessing, text classification, and text generation.
Provides a comprehensive overview of computer vision techniques. It covers topics such as image processing, object detection, and image segmentation.
Provides a comprehensive overview of reinforcement learning techniques. It covers topics such as Markov decision processes, value functions, and policy optimization.
Provides a comprehensive overview of data-intensive text processing techniques using MapReduce. It covers topics such as text preprocessing, text classification, and text clustering.

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