Advanced Statistical Techniques for Data Science

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless. This course is part of the Performance Based Admission courses for the Data Science program. In this course, we will learn what happens to our regression model when these assumptions have not been met. How can we detect these discrepancies in model assumptions and how do we remediate the problems will be addressed in this course. Upon successful completion of this course, you will be able to: -describe the assumptions of the linear regression models. -use diagnostic plots to detect violations of the assumptions of a linear regression model. -perform a transformation of variables in building regression models. -use suitable tools to detect and remove heteroscedastic errors. -use suitable tools to remediate autocorrelation. -use suitable tools to remediate collinear data. -perform variable selections and model validations.

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless. This course is part of the Performance Based Admission courses for the Data Science program. This course will focus on getting you acquainted with the basic ideas behind regression, it provides you with an overview of the basic techniques in regression such as simple and multiple linear regression, and the use of categorical variables. Software Requirements: R Upon successful completion of this course, you will be able to: - Describe the assumptions of the linear regression models. - Compute the least squares estimators using R. - Describe the properties of the least squares estimators. - Use R to fit a linear regression model to a given data set. - Interpret and draw conclusions on the linear regression model. - Use R to perform statistical inference based on the regression models.

If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless. This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique that is robust to having outliers in the data. Finally, we will learn how to perform model validation involving GLM. After this course, students will be able to: - Determine which regression models to use based on the nature of the response variable. - Use regression technique which is robust to the presence of outliers. - Perform generalized linear regression using R by identifying the correct link function. - Interpret and draw conclusions on the regression model. - Use R to perform statistical inference based on the regression models.

This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results. This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary. This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders. Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained. Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading) Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries. To succeed in this course, learners should possess a basic knowledge of linear algebra and statistics, basic set theory and probability theory, and have basic Python and SQL skills. A few courses that can help equip you with the database knowledge needed for this course are: Introduction to Relational Databases, Relational Database Design, and Relational Database Implementation and Applications.