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Mehul Rangwala
This course in statistics will help you gain a solid understanding of the statistical concepts required to understand the topics in machine learning. While both R and Python contain many packages and libraries with machine learning functionalities, it is important to understand whether the outputs from the packages and libraries align with the data andcontext you have on hand. This is why having a solid foundation in statistics, probability, linear algebra, and calculus is so important.
Register for this course and see more details by visiting:
OpenCourser.com/course/a61zh9/statistics
What's inside
Learning objectives
- You will be able to define basic terminology in statistics and some common numerical techniques to explore data.
- You will be able to calculate sampling distributions and the central limit theorem, which are the core of inferential statistics.
- You will be able to use statistical inference – estimation and hypothesis testing.
- You will be able to calculate linear regression, the conditions that need to be satisfied and their violations, and cross validation.
- You will be able to define basic terminology in statistics and some common numerical techniques to explore data.
- You will be able to calculate sampling distributions and the central limit theorem, which are the core of inferential statistics.
- You will be able to use statistical inference – estimation and hypothesis testing.
- You will be able to calculate linear regression, the conditions that need to be satisfied and their violations, and cross validation.
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a strong foundation in statistical concepts, which is essential for understanding machine learning algorithms and interpreting their results
Covers sampling distributions and the central limit theorem, which are fundamental concepts in inferential statistics and crucial for data analysis
Explores linear regression, a widely used statistical technique in machine learning for modeling relationships between variables and making predictions
Taught by the University of California, Davis, which is known for its strong programs in statistics and data science
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Reviews summary
Foundational statistics for machine learning
According to learners, this course provides a solid foundation in the statistical concepts essential for machine learning. Many find the explanations clear and easy to follow, making complex topics accessible. Students particularly appreciate the course's relevance to ML applications, highlighting its practical value. While some note it covers foundational rather than advanced topics, the overall consensus is highly positive, positioning it as a valuable starting point for those needing statistical prerequisites for ML.
Focuses on introductory/intermediate stats.
"This course is definitely geared towards providing a foundation. Those with strong prior stats might find it basic."
"It covers the essentials well, but don't expect deep dives into very advanced statistical modeling techniques."
"Good for beginners needing stats for ML, maybe less so for those looking for highly advanced topics."
"The course serves as an excellent introduction to statistical prerequisites for machine learning."
Topics are explained in an accessible way.
"The explanations in this course are very clear and easy to understand, even for complex statistical ideas."
"I struggled with stats before, but the instructor's way of explaining things made it click for me."
"Content is presented logically, making it easy to follow along and grasp new concepts quickly."
"Everything was well-explained, from basic probability to inference."
Directly applicable to machine learning.
"The course does a great job of connecting statistical concepts directly to their use in machine learning."
"I appreciated how the course emphasized the 'why' behind stats for ML, not just the 'how'."
"Understanding things like sampling distributions and hypothesis testing is clearly shown to be crucial for ML validation."
"Helped me see how statistics is fundamental to understanding ML model outputs."
Builds essential stats knowledge for ML.
"This course provides a really solid foundation in statistics needed for machine learning. It covers all the key concepts effectively."
"I took this course to refresh my stats knowledge for ML, and it definitely delivered. It's a great starting point."
"It helped me understand the statistical underpinnings of ML models much better. A necessary prerequisite."
"I feel much more confident tackling ML topics after completing this course on statistical basics."
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 Machine Learning with these
activities:
Review Introductory Statistics
Show steps
Reinforce foundational statistical concepts to better understand the more advanced topics covered in the course.
Show steps
-
Review key statistical terms and definitions.
-
Work through practice problems on descriptive statistics.
-
Review probability distributions and hypothesis testing.
Read 'Naked Statistics: Stripping the Dread from the Data'
Show steps
Gain a more intuitive understanding of statistical concepts through real-world examples and engaging explanations.
View
Naked Statistics: Stripping the Dread from the...
on Amazon
Show steps
-
Read the book chapter by chapter.
-
Take notes on key concepts and examples.
-
Reflect on how the concepts relate to machine learning.
Practice Hypothesis Testing Problems
Show steps
Solidify your understanding of hypothesis testing by working through a variety of practice problems.
Show steps
-
Find online resources with hypothesis testing problems.
-
Work through the problems, showing your work.
-
Check your answers and review the solutions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Statistical Cheat Sheet
Show steps
Summarize key statistical formulas and concepts into a concise cheat sheet for quick reference.
Show steps
-
Identify the most important formulas and concepts.
-
Organize the information into a clear and concise format.
-
Review and refine the cheat sheet.
Analyze a Public Dataset
Show steps
Apply statistical techniques learned in the course to analyze a real-world dataset and draw meaningful conclusions.
Show steps
-
Choose a public dataset relevant to your interests.
-
Clean and prepare the data for analysis.
-
Perform statistical analysis using appropriate techniques.
-
Interpret the results and draw conclusions.
Read 'The Elements of Statistical Learning'
Show steps
Deepen your understanding of the statistical foundations of machine learning with a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read the book chapter by chapter.
-
Work through the exercises at the end of each chapter.
-
Implement the algorithms in code.
Help Others in Online Forums
Show steps
Reinforce your understanding of statistical concepts by helping other students in online forums.
Show steps
-
Find online forums related to statistics and machine learning.
-
Answer questions and provide helpful explanations.
-
Learn from the questions and perspectives of others.
Review Introductory Statistics
Show steps
Reinforce foundational statistical concepts to better understand the more advanced topics covered in the course.
Show steps
Show steps
Show steps
- Review key statistical terms and definitions.
- Work through practice problems on descriptive statistics.
- Review probability distributions and hypothesis testing.
Read 'Naked Statistics: Stripping the Dread from the Data'
Show steps
Gain a more intuitive understanding of statistical concepts through real-world examples and engaging explanations.
View
Naked Statistics: Stripping the Dread from the...
on Amazon
Show steps
Show steps
Show steps
- Read the book chapter by chapter.
- Take notes on key concepts and examples.
- Reflect on how the concepts relate to machine learning.
Practice Hypothesis Testing Problems
Show steps
Solidify your understanding of hypothesis testing by working through a variety of practice problems.
Show steps
Show steps
Show steps
- Find online resources with hypothesis testing problems.
- Work through the problems, showing your work.
- Check your answers and review the solutions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Statistical Cheat Sheet
Show steps
Summarize key statistical formulas and concepts into a concise cheat sheet for quick reference.
Show steps
Show steps
Show steps
- Identify the most important formulas and concepts.
- Organize the information into a clear and concise format.
- Review and refine the cheat sheet.
Analyze a Public Dataset
Show steps
Apply statistical techniques learned in the course to analyze a real-world dataset and draw meaningful conclusions.
Show steps
Show steps
Show steps
- Choose a public dataset relevant to your interests.
- Clean and prepare the data for analysis.
- Perform statistical analysis using appropriate techniques.
- Interpret the results and draw conclusions.
Read 'The Elements of Statistical Learning'
Show steps
Deepen your understanding of the statistical foundations of machine learning with a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read the book chapter by chapter.
- Work through the exercises at the end of each chapter.
- Implement the algorithms in code.
Help Others in Online Forums
Show steps
Reinforce your understanding of statistical concepts by helping other students in online forums.
Show steps
Show steps
Show steps
- Find online forums related to statistics and machine learning.
- Answer questions and provide helpful explanations.
- Learn from the questions and perspectives of others.
Career center
Learners who complete Statistics for Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a machine learning engineer, you will design, develop, and deploy machine learning models. A key aspect of this role involves understanding the statistical underpinnings of these models. This course is designed to provide a solid grasp of statistical concepts crucial for understanding machine learning topics. By focusing on statistical concepts, it helps ensure that you can effectively interpret the results and outputs of machine learning models, and that packages and libraries align with the data and context at hand. It builds a comprehensive understanding of the statistical techniques needed to excel as a machine learning engineer.
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and insights that inform business decisions. This course provides a strong foundation in the statistical concepts required to effectively analyze data. It teaches fundamental statistical terminology, sampling distributions, and statistical inference. These concepts are essential for data analysts who need to derive meaningful conclusions from data and communicate findings to stakeholders.
Research Scientist
Research Scientist
Research scientists design and conduct experiments and analyze data to investigate phenomena and test hypotheses. This course provides a solid understanding of statistical concepts, directly supporting the analytical skills required for research. The course's coverage of statistical inference and linear regression helps you analyze experimental data and draw statistically sound conclusions. As a research scientist, you will find that this course strengthens your ability to design rigorous studies and interpret findings accurately.
Data Scientist
Data Scientist
The role of a data scientist involves extracting knowledge and insights from data using various statistical and machine learning techniques. This course may be useful because the statistical concepts you learn here are essential for effectively analyzing data and building predictive models. It directly addresses the need to understand the statistical foundations that drive many machine learning algorithms. This course helps you with the statistical rigor needed to validate findings and make informed decisions as a data scientist.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data, test economic theories, and forecast economic trends. The skills taught in this course helps econometricians estimate economic relationships, assess the impact of policies, and make predictions about future economic conditions. As the course focuses on statistical inference and linear regression, it aligns well with addressing the needs of an econometrician who must critically evaluate parameters and make data driven models.
Quantitative Analyst
Quantitative Analyst
A quantitative analyst, often working in the financial sector, develops and implements mathematical models for pricing, hedging, and risk management. Given that these models rely heavily on statistical analysis and probability theory, this course may be useful because it may help you understand the statistical concepts necessary for building and validating quantitative models. The course's focus on statistical inference, estimation, and hypothesis testing helps in the rigorous analysis required for this role. As a quantitative analyst, you need a deep understanding of statistical principles.
Epidemiologist
Epidemiologist
Epidemiologists investigate the patterns and causes of diseases in populations. This course may be useful because it offers a solid understanding of statistical concepts for analyzing health data, identifying risk factors, and designing public health interventions. The course emphasizes statistical inference and hypothesis testing. Knowledge of these topics are crucial for epidemiologists who must evaluate the effectiveness of interventions and policies.
Bioinformatician
Bioinformatician
Bioinformaticians analyze biological data using computational tools and statistical methods to understand complex biological processes. Given the vast amount of data generated in biological research, statistical expertise is vital. This course may be useful because it helps you with the statistical concepts required to analyze biological data effectively. The course's coverage of statistical inference and linear regression aligns well with the needs of a bioinformatician who must interpret experimental results and build predictive models.
Actuary
Actuary
Actuaries assess and manage financial risks, particularly in the insurance and pensions industries. This course may be useful because it builds a foundation in statistical concepts, which is essential for building actuarial models and assessing risk. The course’s coverage of statistical inference and probability helps you in making accurate predictions and informed decisions. Foundational knowledge of actuarial science is strengthened by this course.
Credit Risk Analyst
Credit Risk Analyst
Credit risk analysts assess the creditworthiness of individuals or businesses to determine the risk of lending money. This course may be useful because it provides a foundation in statistical concepts for analyzing financial data and predicting the likelihood of default. Its focus on hypothesis testing and linear regression helps you build and validate credit scoring models, supporting your role as a credit risk analyst by making informed decisions about lending risks.
Statistician
Statistician
Statisticians collect, analyze, and interpret numerical data to identify trends and relationships. This course may be useful because it directly aligns with the core responsibilities of a statistician by building a strong foundation in statistical concepts and techniques. The course may help you define basic statistical terminology, calculate sampling distributions, and apply statistical inference. The knowledge offered here is essential for anyone pursuing a career as a statistician, where a deep understanding of these principles is fundamental.
Marketing Analyst
Marketing Analyst
Marketing analysts analyze market trends, consumer behavior, and the effectiveness of marketing campaigns to optimize strategies and improve results. This course may be useful because it provides a strong understanding of statistical concepts applicable to analyzing marketing data. The skills acquired in this course helps you in conducting A/B testing, analyzing survey data, and drawing meaningful conclusions about consumer preferences and campaign performance. As a marketing analyst, these statistical skills are invaluable for data-driven decision-making.
Financial Analyst
Financial Analyst
A financial analyst provides guidance to businesses and individuals in making investment decisions. While often focused on market trends and financial statements, understanding statistical analysis is crucial for assessing risk and predicting outcomes. This course may be useful because it provides a foundation in statistical concepts, especially linear regression and hypothesis testing, which are valuable for analyzing financial data and making informed predictions. As a financial analyst, these skills enhance your ability to evaluate investment opportunities.
Business Analyst
Business Analyst
A business analyst identifies business needs and determines solutions to business problems. While often focused on business processes, understanding data and its statistical properties is increasingly important. This course may be useful because it helps you understand how to explore data, calculate sampling distributions, and apply statistical inference. The skills gained prepares you to analyze business data more effectively and contributes to data-driven decision-making processes as a business analyst.
Data Architect
Data Architect
Data architects design and manage the data infrastructure for organizations, ensuring that data is stored, processed, and accessible efficiently and securely. While a data architect is focused on infrastructure, this course may be useful because it can help gain a deeper understanding of the statistical characteristics of the data being managed. Concepts in this course, such as terminology in statistics, help improve the performance and reliability of data systems. This improves their effectiveness in supporting data analysis and machine learning initiatives.
For more career information including salaries, visit:
OpenCourser.com/course/a61zh9/statistics
Reading list
We've selected two 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 Machine Learning.
Provides a comprehensive overview of statistical learning techniques. It covers a wide range of topics, including linear regression, classification, and unsupervised learning. It valuable resource for students who want to delve deeper into the theoretical foundations of machine learning. This book is commonly used as a textbook at academic institutions.
Provides an accessible and engaging introduction to statistical concepts. It explains complex ideas in a clear and intuitive way, making it a valuable resource for students who want to build a strong foundation in statistics. It is particularly helpful for understanding the real-world applications of statistical methods. This book serves as excellent additional reading.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a61zh9/statistics
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Effort
3 to 6 hours per week for 4 weeks
Level
Intermediate
Via
edX
Institution
University of California, Davis
Instructor
Mehul Rangwala
Language
English
Transcript
English
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