DSCI 602: Statistical Methods for Data Science (2024) from Coursera

Welcome to the Ball State University course “Statistical Methods for Data Science.” This course is about Statistical Methods for data scientists. To make good sense of data, you will need the right tools and analytics methods. We are going to take a systematic approach to learn about the right tools and methods you can use. Note that as data scientists it is important for us to be able to connect data and learn how the world around us works. To accomplish this challenging task, we will learn how we can connect data through probability theory and statistical models and take actionable decisions, confirm a hypothesis, or make predictions.

After completing the course, you will be able to:

1) Apply probability and distribution theory to address real world problems related to the data science field; 2) Classify the type of random variables and their probability distributions used to model various types of data in practice; 3) Outline the properties of discrete and continuous random variables;

4) Explain the sampling distributions of sample statistics such as the sample mean and the sample proportion; 5) Explain the Laws for Large numbers for the sample mean and the sample proportion;

6) Choose and use appropriate inference strategies such as the right estimation method or the hypothesis test to make inferences on unknown population parameters; 7) Illustrate the estimation process and hypothesis testing as the mode of statistical inference; 8) Outline multivariate discrete and continuous distributions to understand the joint behavior of several correlated discrete and continuous variables, respectively; 9) Relate multivariate analysis techniques to dimension reduction problems; 10) Utilize the R computational environment for probability simulation and other statistical computing in this course.

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Syllabus

Probability Theory: A Review

Welcome! In part 1 of this module you will complete a recommended reading about the course and post on a discussion board entry to introduce yourself to your classmates. In part 2 of this module, we will review probability theory and its applications to real-world problem-solving. Probability is a measure of the chance of occurrence of a future event. For example, what is the probability that you will see two heads when you toss two coins? It is ¼, right? Why do you care about learning probability? Here is a quote by the ancient Greek philosopher Democritus “Everything existing in the universe is the fruit of chance”. Thus, it is important for us to have basic probability knowledge. In data science, probability helps us understand how data is generated and plays a major role in inference and prediction.In this module, we will review three definitions of probability, probability laws, conditional probability, and Bayes' rule. Knowledge of conditional probability is essential in most practical problems. Bayes' rule provides a mechanism for determining conditional probabilities when prior probabilities are given.

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Utilizes the R computational environment, which is a standard tool for statistical computing and data analysis in the field

Explores probability theory, which is essential for understanding data generation, inference, and prediction in data science

Examines both discrete and continuous probability distributions, providing a comprehensive understanding of random variables

Covers multivariate analysis techniques, which are relevant to dimension reduction problems encountered in data science

Reviews probability theory, which may be helpful for learners who need a refresher on fundamental concepts

Reviews summary

Statistical methods for data science foundation

According to learners, DSCI 602 provides a strong theoretical foundation in statistical methods crucial for data science. Many appreciate the depth of coverage on topics like probability, distributions, and inference. However, some students found the course challenging, particularly if they lacked a strong prior background in statistics or R. There's a perception among some that the course leans heavily towards theory with R integration being somewhat basic, suggesting a need to supplement learning with more practical, real-world data science applications outside the course.

Provides a strong statistical base.

"Excellent course! Covers all the fundamental statistical methods needed for data science deeply."

"As a stats major transitioning to data science, this course was perfect. Bridge the gap between theory and application..."

"Gives a strong theoretical base... The material is dense but presented logically."

"Provides a robust statistical foundation... Covers classic topics well."

R examples are useful but limited.

"The R labs were helpful, though sometimes felt a bit rushed."

"R usage was minimal compared to the theory."

"The R component is useful but basic. Would benefit from more case studies..."

"R examples were good but limited."

More theoretical than applied data science.

"Needed to supplement with other resources for practical implementation..."

"Some parts felt overly academic rather than focused on practical data science problems."

"This course was not what I expected. Very theoretical statistics, not enough data science application."

"Needed extra practice outside the course to fully grasp applying them in real-world scenarios."

Challenging, especially for beginners.

"Found this course very difficult. Assumes too much prior knowledge..."

"The pace was too fast for me as a beginner."

"Requires significant effort to fully grasp the concepts."

"Too much math, not enough practical coding..."

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 DSCI 602: Statistical Methods for Data Science (2024) with these activities:

Review Introductory Statistics

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Solidify your understanding of fundamental statistical concepts to better grasp the more advanced methods covered in this course.

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Review key concepts like mean, median, mode, and standard deviation.
Practice calculating probabilities and interpreting statistical results.
Familiarize yourself with different types of data and their appropriate statistical analyses.

Read 'Naked Statistics: Stripping the Dread from the Data'

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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

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Read the book, focusing on chapters related to probability, distributions, and inference.
Take notes on key concepts and examples.
Reflect on how the concepts relate to data science applications.

Probability Calculation Exercises

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Reinforce your understanding of probability theory by working through a variety of calculation exercises.

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Find online resources or textbooks with probability calculation problems.
Work through the problems, showing your work and checking your answers.
Focus on problems related to conditional probability and Bayes' rule.

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Create a Probability Distribution Visualization

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Deepen your understanding of probability distributions by creating a visual representation of a distribution of your choice.

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Choose a probability distribution (e.g., normal, binomial, Poisson).
Use R or another tool to generate random samples from the distribution.
Create a histogram or other visualization to display the distribution.
Write a short explanation of the distribution and its properties.

Analyze a Real-World Dataset

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Apply the statistical methods learned in the course to analyze a real-world dataset and draw meaningful conclusions.

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Find a publicly available dataset related to a topic of interest.
Clean and preprocess the data.
Apply appropriate statistical methods to analyze the data.
Interpret the results and draw conclusions.
Write a report summarizing your findings.

Build a Predictive Model

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Solidify your understanding of statistical modeling by building a predictive model using the techniques learned in the course.

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Choose a prediction problem and find a relevant dataset.
Select appropriate features and build a statistical model.
Evaluate the model's performance using appropriate metrics.
Refine the model to improve its accuracy.
Document your model and its performance.

Read 'All of Statistics: A Concise Course in Statistical Inference'

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Expand your knowledge of statistical inference with a comprehensive and rigorous treatment of the subject.

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Work through the examples and exercises.
Read the book, focusing on chapters related to inference, modeling, and multivariate analysis.
Relate the concepts to real-world data science problems.

Career center

Learners who complete DSCI 602: Statistical Methods for Data Science (2024) will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist works with large datasets to extract insights and develop predictive models. This course is designed to provide a foundation in statistical methods for data science. The course's emphasis on probability and distribution theory, random variables, statistical inference, and multivariate analysis are vital to a data scientist. This course is especially relevant for those wishing to advance in or become a data scientist, as it provides instruction directly applicable to the field.

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Statistician

A statistician utilizes statistical methods to analyze data and draw meaningful conclusions, often working in research, government, or the private sector. This course builds a strong understanding of probability theory, random variables, and various distributions—all of which are foundational for a statistician. The ability to apply inference strategies, estimation methods, and hypothesis testing, also covered in this course, is crucial for this role. The course's focus on connecting data through probability and statistical models is particularly relevant for a statistician wishing to understand the underlying processes driving data. The usage of R computational software in this course may also be valuable for this role.

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Quantitative Analyst

A quantitative analyst, often working in finance or economics, develops and applies mathematical and statistical models to price assets or make investment decisions. This course introduces several methods crucial for a quantitative analyst, such as probability theory, statistical modeling, and inference strategies. This course's emphasis on connecting data through probability and statistical models may also be especially relevant. The use of R for computational probability will also be useful to a quantitative analyst. Many quantitative analyst roles require an advanced degree.

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Biostatistician

A biostatistician applies statistical methods to biological and health-related data, often in clinical trials, epidemiological studies, or genomics research. This course provides the essential statistical foundation required for this role. The course’s focus on probability and distribution theory, random variables, and inference strategies helps build a biostatistician's core skill set. Moreover, the course’s coverage of multivariate distributions is invaluable for analyzing complex biological datasets. This course may be particularly beneficial for biostatisticians.

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Research Scientist

Research scientists often employ statistical methods to analyze experimental data and test hypotheses, particularly in fields like biology, psychology, and medicine. The topics covered in this course, such as probability and distribution theory and hypothesis testing, are essential capabilities for a research scientist. A research scientist may also benefit from learning about multivariate discrete and continuous distributions to understand correlated variables, as this course provides. Furthermore, the computational skills and the use of R, which are part of the course, may be useful in the day to day work of a research scientist.

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Bioinformatician

A bioinformatician applies computational and statistical methods to analyze biological data, particularly genetic data. This course helps build a foundation in statistics and probability, which is essential for this role. The course’s material on random variables, probability distributions, and multivariate analysis are directly relevant. This includes learning about inference and hypothesis testing. The use of R, taught in this course, is also a valuable skill for a bioinformatician’s day to day work.

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Epidemiologist

Epidemiologists study the distribution and determinants of health-related states or events, often using statistical methods to identify disease patterns and risk factors. This course provides an introduction to the statistical methods that an epidemiologist may use in their work. The course material on probability and distribution theory, random variables, and inference strategies are highly relevant to an epidemiologist. The course's exploration of the joint behavior of correlated variables will also be useful in this role. The course's focus on applied methods in the real world may be particularly useful.

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Data Analyst

The role of a data analyst involves interpreting data to identify trends and insights that can inform business decisions. This course will help you to develop skills in probability and distribution theory, which are fundamental for this role. For example, understanding random variables and their distributions is critical for this role. The course's coverage of inference strategies and hypothesis testing will help the data analyst to draw conclusions from data. The course emphasizes a systematic approach to learning tools and methods, which will be helpful to a data analyst.

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Operations Research Analyst

An operations research analyst applies mathematical and statistical methods to solve complex problems and improve operational efficiency. This course may provide a foundation in statistical techniques used by operations research analysts. The course's coverage of probability theory, inference strategies, hypothesis testing, and multivariate analysis may be specifically useful to an operations research analyst. The use of R could be relevant for statistical computation and simulation.

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Actuary

An actuary assesses and manages financial risk, often in insurance and finance, using statistical models. The methods learned in this course, such as probability theory, random variables, and distributions, are an important part of an actuary's toolbox. The course's coverage of statistical models, inference, and hypothesis testing will also help the actuary in their day to day work. This course may be helpful for those hoping to begin a career as an actuary.

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Machine Learning Engineer

Machine learning engineers build and develop machine learning models. This course helps build a foundation in probability and distribution theory, which is an important part of this work. Understanding random variables, probability distributions, and multivariate analysis, as covered in this course, is important for a machine learning engineer, especially when understanding the models they are building. This course's focus on using R for statistical computation could help with data analysis that serves as a precursor to model building.

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Social Science Researcher

Social science researchers use quantitative and qualitative methods to study human behavior and social phenomena. This course gives researchers a foundation in statistical analysis. The course's coverage of probability, random variables, statistical inference, and multivariate distributions can help a social science researcher analyze quantitative data. The course's use of R for computation could be particularly useful for data analysis and simulation within social science research.

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Market Research Analyst

Market research analysts study consumer behavior and market trends to advise companies on product development and marketing strategies. This course provides a foundation in probability and distribution theory, inference strategies, and hypothesis testing which can be useful for analyzing survey data and making informed recommendations. The course's instruction on connecting data through probability may be helpful for understanding consumer data. Also, the use of R for statistical computing could aid in data analysis.

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Business Intelligence Analyst

A business intelligence analyst works with data, creating reports and dashboards to inform business decisions. This course helps build a foundation in probability and distribution theory, which is relevant for this work. The course's coverage of inference strategies, estimation methods, and hypothesis testing helps draw conclusions from data, which is particularly valuable for a business intelligence analyst. The course’s focus on the systematic learning of tools and methods may be helpful in their day to day work.

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Financial Analyst

A financial analyst analyzes financial data, develops forecasts, and provides recommendations to clients or companies. The statistical methods covered in this course, such as probability theory, random variables, and statistical modeling, could be a useful foundation for a financial analyst. The course's focus on inference strategies and hypothesis testing could also help a financial analyst when trying to understand different financial markets or investment decisions. While not a primary skill, the statistical background provided in this course may be beneficial for a financial analyst.

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DSCI 602

Statistical Methods for Data Science (2024)

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Statistical methods for data science foundation

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