Data Science

Training series includes Basic Statistics, Intermediate Statistics, Introduction to Design of Experiments and Introduction to Reliability and Survival Analysis, which can be delivered as a complete program or as individual stand-alone classes. Always tailored to company needs—implementation and follow-up small group coaching highly recommended for learning transferability and sustainability.

Statistics is one of the three pillars of modern data science. A key component of data science, statistics addresses uncertainty and how to make data-based, informed decisions in the face of uncertainty. This course explores the practical underpinnings of modern statistics.

LEARNING OBJECTIVES

• Learn how to characterize data
• Learn measures of center, spread and shape: the normal distribution
• Practice elementary graphical analysis methods
• Learn about hypothesis testing
• Learn about simple statistical comparisons of mean and variation

Once the subject of basic statistics is well understood, it's time to shift into 2^nd gear. In statistics, it's all about using inference—how we know data behaves—to encapsulate uncertainty within specified boundaries. In doing so, we can assess risks of making incorrect decisions.

LEARNING OBJECTIVES

• Perform graphical analysis: the procedure and application of graphical tools
• Perform a Measurement System Analysis (MSA, aka Gage Repeatability and Reproducibility)
• Learn how to deal with non-normal data
• Learn the basics of hypothesis testing: types I and II error, p-values, confidence and power
• Perform power and sample-size calculations
• Perform simple comparisons: confidence intervals, testing means and variances
• Perform correlation and simple linear regression
• Learn the basics of Analysis of Variance (ANOVA) and perform an ANOVA analysis
• Learn about process capability and apply to a company-specific process
• Perform statistical tolerancing for a company-specific product

This course goes well beyond traditional trial-and-error or one-factor-at-a-time experimentation. Through theory, example, and company-specific application, the participant will experience the wonderful world of Design of Experiments (DoE). The intent of the course is to familiarize the participant with the terminology, process, and possibilities provided by modern experimentation through lecture, example, and company-oriented exercises. 

LEARNING OBJECTIVES

• Review Analysis of Variance method through randomized block designs
• Design and analyze General Full-Factorial
• Design and analyze 2^k Full-Factorial designs
• Understand the use of Center-Points and review Blocking in experimental designs
• Design and analyze 2^k Fractional-Factorial designs
• Perform DoE Planning and Sample-size calculations
• Design and analyze Response Surface Methodology (RSM) optimization designs

This course is focused on the application of reliability concepts to evaluate product lifetime and survival. The course moves from the basics of product reliability and its importance to simple case studies illustrating how survival may be evaluated. 

LEARNING OBJECTIVES

• Understand reliability distributions, the Survival and Hazard functions, and the “bathtub” lifetime curve
• Understand left-, right-, and interval-Censoring
• Perform reliability estimates via parametric and nonparametric methods
• Perform Warranty prediction
• Perform Bayesian reliability analysis
• Create reliability test plans
• Perform reliability estimates for multiple failure modes

This course explores distributed computing and practical tools used to store and process data before it can be analyzed. Class works with data stacks to gain experience with the kinds of data flow situations commonly used to inform key business decisions. 

Series options include: Big Data Core, Data Science Foundations, Applied Data Science with R, Applied Data Science with Python, Deep Learning, Blockchain and more.

LEARNING OBJECTIVES

• Learn concepts of Big Data, including distributed data storage and processing for analytical and streaming applications
• Understand how to apply critical data engineering tools and techniques
• Learn how to use emerging technologies to solve current data challenges
• Work with tools including Hadoop and Hive