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Muhammad Saad uddin
This is a hands-on project to give you an overview of how to use statistics in data science.
Register for this course and see more details by visiting:
OpenCourser.com/course/bkmwdk/statistics
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What's inside
Syllabus
Project Overview
Here you will describe what the project is about...give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps learners develop statistical analytical skills, which are essential for data science
Provides hands-on experience in using statistics for data analysis
Save this course
Save Statistics For Data Science to your list so you can find it easily later:
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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 Statistics For Data Science with these
activities:
Review Probability and Statistics
Show steps
Review foundational knowledge of probability and statistics to prepare for course material
Browse courses on
Probability
Show steps
-
Go over lecture notes on probability distributions and statistical modeling
-
Practice basic statistical calculations
-
Review statistical software
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Reinforce course concepts through a comprehensive review of machine learning techniques
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read selected chapters relevant to course material
-
Complete exercises and projects to apply concepts
-
Summarize key insights and connect them to course material
Pandas Tutorial for Data Manipulation
Show steps
Follow tutorials to strengthen proficiency in data manipulation using Pandas
Browse courses on
Python
Show steps
-
Find online tutorials or courses on Pandas
-
Practice using Pandas functions and methods
-
Apply Pandas to real-world datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Data Visualization Project
Show steps
Develop an in-depth project visualizing a dataset to reinforce data analysis and presentation skills
Browse courses on
Data Visualization
Show steps
-
Gather and prepare dataset
-
Clean and transform data
-
Create visualizations using data visualization software
-
Write a report summarizing insights gained
Discussion Forum Participation
Show steps
Engage in discussions with peers to broaden perspectives and understanding
Browse courses on
Machine Learning
Show steps
-
Read and respond to discussion prompts
-
Ask clarifying questions and provide insights
-
Summarize key points and takeaways
Statistical Analysis Practice Problems
Show steps
Sharpen statistical analysis skills through targeted practice
Browse courses on
Statistical Analysis
Show steps
-
Identify practice problems and questions
-
Solve problems and verify solutions
-
Identify areas for improvement and seek support
Data Science Blog Post
Show steps
Deepen understanding and showcase learning through creation and sharing of data science content
Browse courses on
Data Science
Show steps
-
Identify a specific data science topic
-
Research and gather relevant information
-
Write and edit blog post, ensuring clarity and accuracy
-
Publish and promote blog post
Contribute to Open Source Data Science Project
Show steps
Gain hands-on experience in real-world data science projects
Browse courses on
Data Science
Show steps
-
Identify an open-source data science project to contribute to
-
Review project documentation and codebase
-
Make contributions to the project
-
Seek feedback and collaborate with project maintainers
Review Probability and Statistics
Show steps
Review foundational knowledge of probability and statistics to prepare for course material
Browse courses on
Probability
Show steps
Show steps
Show steps
- Go over lecture notes on probability distributions and statistical modeling
- Practice basic statistical calculations
- Review statistical software
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Reinforce course concepts through a comprehensive review of machine learning techniques
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to course material
- Complete exercises and projects to apply concepts
- Summarize key insights and connect them to course material
Pandas Tutorial for Data Manipulation
Show steps
Follow tutorials to strengthen proficiency in data manipulation using Pandas
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online tutorials or courses on Pandas
- Practice using Pandas functions and methods
- Apply Pandas to real-world datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Data Visualization Project
Show steps
Develop an in-depth project visualizing a dataset to reinforce data analysis and presentation skills
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and prepare dataset
- Clean and transform data
- Create visualizations using data visualization software
- Write a report summarizing insights gained
Discussion Forum Participation
Show steps
Engage in discussions with peers to broaden perspectives and understanding
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read and respond to discussion prompts
- Ask clarifying questions and provide insights
- Summarize key points and takeaways
Statistical Analysis Practice Problems
Show steps
Sharpen statistical analysis skills through targeted practice
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Identify practice problems and questions
- Solve problems and verify solutions
- Identify areas for improvement and seek support
Data Science Blog Post
Show steps
Deepen understanding and showcase learning through creation and sharing of data science content
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify a specific data science topic
- Research and gather relevant information
- Write and edit blog post, ensuring clarity and accuracy
- Publish and promote blog post
Contribute to Open Source Data Science Project
Show steps
Gain hands-on experience in real-world data science projects
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify an open-source data science project to contribute to
- Review project documentation and codebase
- Make contributions to the project
- Seek feedback and collaborate with project maintainers
Career center
Learners who complete Statistics For Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists play a crucial role in the data science industry, utilizing statistical methods and analytical techniques to extract meaningful insights from complex datasets. This course on Statistics For Data Science provides a solid foundation for Data Scientists, equipping them with the skills to analyze, interpret, and communicate data-driven insights effectively.
Statistician
Statistician
Statisticians apply statistical principles to solve problems across various fields, including healthcare, finance, and research. This course helps build a foundation for Statisticians by providing a comprehensive overview of statistical concepts and techniques, enabling them to design and conduct statistical studies effectively.
Data Analyst
Data Analyst
Data Analysts play a key role in analyzing and interpreting data to drive decision-making. This course on Statistics For Data Science provides a valuable foundation for Data Analysts, as it equips them with the skills to gather, clean, and analyze data, as well as communicate their findings effectively.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex problems. This course on Statistics For Data Science may be useful for Machine Learning Engineers, as it provides a foundation in statistical concepts that are essential for understanding and implementing machine learning algorithms.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical models and analytical techniques to assess financial risk and make investment decisions. This course on Statistics For Data Science may be useful for Quantitative Analysts, as it provides a foundation in statistical methods and data analysis techniques that are widely used in the financial industry.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to analyze and interpret data in the field of healthcare. This course on Statistics For Data Science may be useful for Biostatisticians, as it provides a foundation in statistical concepts and techniques that are essential for understanding and analyzing biomedical data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to optimize decision-making in various industries. This course on Statistics For Data Science may be useful for Operations Research Analysts, as it provides a foundation in statistical modeling and optimization techniques that are widely used in this field.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct surveys, analyze data, and interpret results to understand consumer behavior and market trends. This course on Statistics For Data Science may be useful for Market Research Analysts, as it provides a foundation in statistical methods and data analysis techniques that are essential for understanding and interpreting market research data.
Actuary
Actuary
Actuaries use statistical and mathematical models to assess and manage financial risk. This course on Statistics For Data Science may be useful for Actuaries, as it provides a foundation in statistical methods and modeling techniques that are widely used in the insurance industry.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. This course on Statistics For Data Science may be useful for Epidemiologists, as it provides a foundation in statistical methods and data analysis techniques that are essential for understanding and analyzing epidemiological data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course on Statistics For Data Science may be useful for Software Engineers specializing in data-driven applications, as it provides a foundation in statistical concepts and data analysis techniques that are becoming increasingly important in software development.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. This course on Statistics For Data Science may be useful for Financial Analysts, as it provides a foundation in statistical methods and data analysis techniques that are widely used in the financial industry.
Business Analyst
Business Analyst
Business Analysts use data and analysis to improve business processes and make better decisions. This course on Statistics For Data Science may be useful for Business Analysts, as it provides a foundation in statistical methods and data analysis techniques that are essential for understanding and analyzing business data.
Data Engineer
Data Engineer
Data Engineers design and build systems to store and process large amounts of data. This course on Statistics For Data Science may be useful for Data Engineers, as it provides a foundation in statistical concepts and data analysis techniques that are becoming increasingly important in data engineering.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. This course on Statistics For Data Science may be useful for User Experience Researchers, as it provides a foundation in statistical methods and data analysis techniques that are essential for understanding and analyzing user behavior.
For more career information including salaries, visit:
OpenCourser.com/course/bkmwdk/statistics
Reading list
We've selected 13 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
Statistics For Data Science.
More advanced textbook on statistical learning, covering a wide range of topics from linear regression to Bayesian methods. It valuable reference for anyone looking to learn more about the theory and practice of statistical learning.
Comprehensive guide to Bayesian data analysis, covering a wide range of topics from Bayesian inference to Bayesian modeling. It valuable resource for anyone looking to learn more about the theory and practice of Bayesian data analysis.
Save
Comprehensive overview of deep learning, covering a wide range of topics from neural networks to convolutional neural networks. It valuable resource for anyone looking to learn more about the theory and practice of deep learning.
Comprehensive guide to Bayesian statistics, covering a wide range of topics from Bayesian inference to Bayesian modeling. It valuable resource for anyone looking to learn more about the theory and practice of Bayesian statistics.
Widely used textbook on statistical learning, covering a wide range of topics from linear regression to unsupervised learning. It valuable reference for anyone looking to learn more about the theory and practice of statistical learning.
Comprehensive guide to causal inference, covering a wide range of topics from causal models to causal inference methods. It valuable resource for anyone looking to learn more about the theory and practice of causal inference.
Save
Concise introduction to econometrics, covering a wide range of topics from econometric models to econometric estimation methods. It valuable resource for anyone looking to learn more about the theory and practice of econometrics.
Practical guide to machine learning, covering a wide range of topics from data preprocessing to model evaluation. It valuable resource for anyone looking to learn more about the practical aspects of machine learning.
Comprehensive guide to Python for data analysis, covering a wide range of topics from data manipulation to data visualization. It valuable resource for anyone looking to learn more about the practical aspects of data analysis using Python.
Save
Comprehensive guide to R for data science, covering a wide range of topics from data manipulation to data visualization. It valuable resource for anyone looking to learn more about the practical aspects of data science using R.
Concise introduction to causal inference, covering a wide range of topics from causal models to causal inference methods. It valuable resource for anyone looking to learn more about the theory and practice of causal inference.
Provides a comprehensive overview of data science, including the key concepts, techniques, and tools used in the field. It valuable resource for anyone looking to gain a deeper understanding of data science and its applications in business.
Practical guide to data science, covering a wide range of topics from data cleaning to model building. It valuable resource for anyone looking to learn more about the practical aspects of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bkmwdk/statistics
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Effort
2 Hour
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Language
English
Good to know
Helps learners develop statistical analytical skills, which are essential for data science
Provides hands-on experience in using statistics for data analysis
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