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Updated in [March 06th, 2023]

This course provides an overview of regression analysis for Six Sigma Black Belt aspirants. It covers topics such as building predictive models based on multiple linear and logistic regression, analyzing and interpreting results of regression models, correlation and scatter diagrams, single linear regression using line of best fit, multiple linear regression with best sub-set method, residual analysis, various statistics such as R-sq, R-sq(Adj), R-sq(Pred), S Value, Mallow's Cp, VIF, multi-collinearity, Spearman's coefficient, logistic regression using logit function, and predictive analytics. Participants will gain the knowledge and skills necessary to answer questions related to regression analysis for ASQ and IASSC certification tests, as well as gain a primer on predictive modeling.

[Applications]
After taking this course, Six Sigma Black Belt Aspirants will be able to apply the concepts of Regression Analysis to answer questions in ASQ and IASSC Certification Tests. Machine Learning enthusiasts will be able to use the concepts of Regression Analysis as a foundation for Predictive Modeling. The course will also provide an understanding of Correlation & Scatter Diagram, Single Linear Regression, Multiple Linear Regression, Residual Analysis, Various Statistics, Multi-collinearity, Spearman's Coefficient, Logistic Regression, and Predictive Analytics.

[Career Paths]
Recommended Career Paths:
1. Data Scientist: Data Scientists use predictive modeling and regression analysis to analyze large datasets and develop insights that can be used to inform business decisions. They are responsible for creating and maintaining data models, developing algorithms, and creating visualizations to present their findings. As the demand for data-driven insights continues to grow, the demand for Data Scientists is expected to increase.

2. Business Analyst: Business Analysts use regression analysis to identify trends and patterns in data that can be used to inform business decisions. They are responsible for gathering and analyzing data, developing reports, and presenting their findings to stakeholders. As businesses become increasingly data-driven, the demand for Business Analysts is expected to grow.

3. Machine Learning Engineer: Machine Learning Engineers use regression analysis to develop and deploy machine learning models. They are responsible for designing, building, and testing machine learning models, as well as deploying them in production. As machine learning becomes more widely used, the demand for Machine Learning Engineers is expected to increase.

4. Data Engineer: Data Engineers use regression analysis to develop and maintain data pipelines. They are responsible for designing, building, and maintaining data pipelines, as well as ensuring that data is secure and accessible. As businesses become increasingly reliant on data, the demand for Data Engineers is expected to grow.

[Education Paths]
Recommended Degree Paths:
1. Bachelor of Science in Statistics: This degree program provides students with a comprehensive understanding of statistical methods and techniques, including data analysis, probability, and regression analysis. Students will learn how to use statistical software to analyze data and develop predictive models. This degree is ideal for those interested in pursuing a career in data science, analytics, or research.

2. Master of Science in Data Science: This degree program provides students with a comprehensive understanding of data science principles and techniques, including machine learning, data mining, and predictive analytics. Students will learn how to use data to develop models and algorithms that can be used to make decisions and solve problems. This degree is ideal for those interested in pursuing a career in data science, analytics, or research.

3. Master of Science in Artificial Intelligence: This degree program provides students with a comprehensive understanding of artificial intelligence principles and techniques, including machine learning, natural language processing, and computer vision. Students will learn how to use AI to develop models and algorithms that can be used to make decisions and solve problems. This degree is ideal for those interested in pursuing a career in AI, robotics, or computer science.

4. Doctor of Philosophy in Machine Learning: This degree program provides students with a comprehensive understanding of machine learning principles and techniques, including deep learning, reinforcement learning, and unsupervised learning. Students will learn how to use machine learning to develop models and algorithms that can be used to make decisions and solve problems. This degree is ideal for those interested in pursuing a career in AI, robotics, or computer science.

Developing Trends:
1. Automation: Automation is becoming increasingly important in the field of data science and analytics. Automation tools are being used to automate data collection, analysis, and modeling processes, allowing data scientists to focus on more complex tasks.

2. Big Data: Big data is becoming increasingly important in the field of data science and analytics. Big data technologies are being used to collect, store, and analyze large amounts of data, allowing data scientists to uncover insights and make better decisions.

3. Cloud Computing: Cloud computing is becoming increasingly important in the field of data science and analytics. Cloud computing technologies are being used to store and process large amounts of data, allowing data scientists to access data from anywhere in the world.

4. Machine Learning: Machine learning is becoming increasingly important in the field of data science and analytics. Machine learning algorithms are being used to develop models and algorithms that can be used to make decisions and solve problems.