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Analyze Box Office Data with Seaborn and Python
Snehan Kekre
Snehan Kekre
Welcome to this project-based course on Analyzing Box Office Data with Seaborn and Python. In this course, you will be working with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) in this project and the next. The statistical data visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs. By the end of this course, you will be able to produce data visualizations in Python with Seaborn, and apply graphical techniques used in exploratory data analysis (EDA).
Read more
Welcome to this project-based course on Analyzing Box Office Data with Seaborn and Python. In this course, you will be working with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) in this project and the next. The statistical data visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs. By the end of this course, you will be able to produce data visualizations in Python with Seaborn, and apply graphical techniques used in exploratory data analysis (EDA).
Read more
Welcome to this project-based course on Analyzing Box Office Data with Seaborn and Python. In this course, you will be working with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) in this project and the next. The statistical data visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs. By the end of this course, you will be able to produce data visualizations in Python with Seaborn, and apply graphical techniques used in exploratory data analysis (EDA).
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/y608t6/analyze
What's inside
Syllabus
Project: Analyze Box Office Data with Seaborn and Python
Welcome to this project-based course on Analyzing Worldwide Box Office Revenue with Seaborn and Python. In this project you will be working with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) in this project and the next. The statistical data visualization libraries Seaborn and Plotly will be our workhorses to generate interactive, publication-quality graphs.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides learners with hands-on labs and interactive materials, which can enhance learning
Uses Seaborn, a respected statistical data visualization library, for data analysis
Focuses on analyzing box office data, making it relevant for individuals interested in business or movie industry
Taught by Snehan Kekre, an experienced instructor in the field
Emphasizes exploratory data analysis (EDA), a valuable skill for data professionals
Limited access to the cloud desktop (five times), which could hinder learning for some students
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Reviews summary
Instruction-based seaborn training
According to students, this instruction-based course on visualizing box office data with Seaborn and Python is well received. Most learners largely appreciate the clear and helpful guidance from the instructor, Snehan Kekre. They report that the course is easy to follow and has engaging assignments. There are a handful of minor gripes about the user interface, and some students wish there was more supplemental material, but overall, the course is a hit.
Course is accessible to beginners.
"This course helped me gain hands-on experience using my knowledge of Python and its data visualisation libraries."
"The course is good for complete beginners"
"Snehan is a great instructor. With this course you'll learn how to use some Seaborn plots and how to do some EDA with pandas. "
Assignments help enforce learning.
"Easy to follow"
"Great course for practicing the application of seaborn, pyplot & panda."
"Good and helpful! Nice and clear instructions from the project guide helped me a lot!"
Instructor is clear and helpful.
"The instructor gave clear explanation."
"Nice Course, Thank you instructor Snehan Kekre"
"Mr. Kekre was elaborative, clear, neat, and direct in illustrating the project"
User interface can be frustrating.
"great idea, great course, the interface is not easy to work with."
"The lecture was great but the cloud desktop was too slow .It took too long to connect and sometimes it didn't so i had to exit."
More content would be beneficial.
"It's a good project"
"Great course, but please add more study material for the libraries that were used in the course"
"Need some more of content and wanted to download and try out in my applocation"
Activities
Coming soon
We're preparing activities for
Analyze Box Office Data with Seaborn and Python.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Analyze Box Office Data with Seaborn and Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to extract meaningful insights. By taking this course, you will gain valuable skills in data visualization with Seaborn, which is an important tool for effectively communicating data insights to stakeholders. Additionally, the course covers exploratory data analysis (EDA) techniques, which are essential for drawing meaningful conclusions from data.
Business Analyst
Business Analyst
A Business Analyst uses data to understand business processes and identify areas for improvement. This course will provide you with the skills to analyze box office data, which can be valuable for understanding consumer behavior and making informed business decisions. Additionally, the course covers data visualization techniques, which are essential for effectively communicating data insights to stakeholders.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand market trends and consumer behavior. This course will provide you with the skills to analyze box office data, which can be valuable for understanding consumer behavior and identifying market opportunities. Additionally, the course covers data visualization techniques, which are essential for effectively communicating data insights to stakeholders.
Data Scientist
Data Scientist
A Data Scientist is responsible for building and deploying machine learning models to extract insights from data. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Data Scientist. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to evaluate financial performance and make investment recommendations. This course will provide you with the skills to analyze box office data, which can be valuable for understanding the financial performance of movie studios and making informed investment decisions. Additionally, the course covers data visualization techniques, which are essential for effectively communicating data insights to stakeholders.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to understand marketing campaigns and consumer behavior. This course will provide you with the skills to analyze box office data, which can be valuable for understanding the effectiveness of marketing campaigns and identifying opportunities for improvement. Additionally, the course covers data visualization techniques, which are essential for effectively communicating data insights to stakeholders.
Product Manager
Product Manager
A Product Manager is responsible for developing and managing products. This course will provide you with the skills to analyze data, which is essential for understanding customer needs and making informed product decisions. Additionally, the course covers data visualization techniques, which are essential for effectively communicating data insights to stakeholders.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Software Engineer. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing and maintaining data infrastructure. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Data Engineer. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to provide insights and make predictions. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Statistician. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Economist
Economist
An Economist analyzes economic data to understand economic trends and make policy recommendations. This course will provide you with a foundation in data analysis and visualization, which are essential skills for an Economist. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and spread of diseases. This course will provide you with a foundation in data analysis and visualization, which are essential skills for an Epidemiologist. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to medical data to improve healthcare outcomes. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Biostatistician. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Actuary
Actuary
An Actuary uses statistical methods to assess risk and uncertainty. This course will provide you with a foundation in data analysis and visualization, which are essential skills for an Actuary. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze financial data. This course will provide you with a foundation in data analysis and visualization, which are essential skills for a Quantitative Analyst. Additionally, the course covers exploratory data analysis (EDA) techniques, which are important for understanding data and identifying patterns.
For more career information including salaries, visit:
OpenCourser.com/course/y608t6/analyze
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
Analyze Box Office Data with Seaborn and Python.
Comprehensive reference on speech and language processing and covers topics such as speech recognition, natural language understanding, and speech synthesis. It is written in a clear and engaging style and provides deep insights into the theoretical foundations and practical applications of speech and language processing.
Provides a comprehensive overview of exploratory data analysis (EDA) techniques using Python. It covers topics such as data cleaning, data exploration, and statistical analysis, and provides practical examples and exercises to help learners apply EDA to real-world datasets.
Save
Comprehensive introduction to machine learning and covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering. It is written in a clear and engaging style and provides intuitive explanations of complex machine learning concepts.
Comprehensive reference on data science with Python and covers topics such as data manipulation, data visualization, and machine learning. It provides detailed explanations and practical examples for working with Python data science libraries such as NumPy, Pandas, and scikit-learn.
Comprehensive introduction to machine learning with Python and covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering. It is written in a clear and engaging style and provides intuitive explanations of complex machine learning concepts.
Comprehensive introduction to deep learning with Python and covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and engaging style and provides intuitive explanations of complex deep learning concepts.
Save
Provides a comprehensive introduction to data visualization with Seaborn, covering topics such as creating different types of charts, customizing visualizations, and working with large datasets. It valuable resource for learners who want to gain a deeper understanding of Seaborn and its capabilities.
Practical guide to machine learning with Python and covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering. It is written in a clear and concise style and provides step-by-step instructions for building and deploying machine learning models.
Provides a practical introduction to machine learning and covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering. It is written in a clear and concise style and provides step-by-step instructions for building and deploying machine learning models.
Save
Comprehensive introduction to computer vision and covers topics such as image processing, object detection, and image classification. It is written in a clear and concise style and provides practical examples for working with computer vision tasks.
Provides a comprehensive introduction to machine learning using Python libraries such as scikit-learn, Keras, and TensorFlow. It covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering, and provides practical examples and exercises to help learners build and deploy machine learning models.
Provides a hands-on introduction to data science and covers topics such as data cleaning, data exploration, and machine learning. It is written in a practical and accessible style and provides step-by-step instructions for working with real-world datasets.
Provides a comprehensive introduction to natural language processing (NLP) with Python and covers topics such as text preprocessing, text classification, and text generation. It is written in a clear and concise style and provides practical examples for working with NLP tasks.
Classic reference on statistical learning and provides a comprehensive overview of machine learning algorithms. It covers topics such as linear regression, logistic regression, decision trees, and support vector machines, and provides mathematical foundations and theoretical insights for understanding machine learning concepts.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y608t6/analyze
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Provides learners with hands-on labs and interactive materials, which can enhance learning
Uses Seaborn, a respected statistical data visualization library, for data analysis
Focuses on analyzing box office data, making it relevant for individuals interested in business or movie industry
Taught by Snehan Kekre, an experienced instructor in the field
Emphasizes exploratory data analysis (EDA), a valuable skill for data professionals
Limited access to the cloud desktop (five times), which could hinder learning for some students
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