Statistical Fundamentals of Data Science Part-1 (SFDS-01)
Learn to analyze and visualize data in R and gain proficiency in descriptive statistics, probability, hypothesis testing, and regression.
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Statistical Fundamentals of Data Science Part-2 (SFDS-02)
The course covers exploratory data analysis techniques and an introduction to statistical regressions.
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Statistics for Non Statisticians (SFNS)
Do you feel utterly lost when you see numbers? ‘Statistics For Non-Statisticians’ helps you build a steady foundation grasp essential concepts of statistical thinking, and...
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Design of Experiments Part-1 (DOE-01): Basic DOE with R
This course covers the fundamentals of the design and analysis of experiments (DoE). Using these principles, you will learn to critically analyze experimental data and...
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Design of Experiments Part-2 (DOE-02): Mixture DOE with R
In this course, you will learn understand the differences between factorial designs and mixture designs.
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Sample Size and Power (SSP)
"Statistical power analysis addresses the question “How large a sample do I need?
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Design of Experiments Part-3 (DOE-03): Advanced DOE with R
"This course will cover the basic concepts behind the Response Surface Methodology and Experimental Designs for maximising or minimising response variables.
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Principal Components Analysis (ML-01)
The course delves into a critical aspect of machine learning - Principal Component Analysis (PCA).
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Time Series Analysis Using R (ML-02)
Currently, R is the leading open source software for time series analysis and forecasting.
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Statistical Quality Control (SQC-01)
Statistical process control (SPC) or statistical quality control (SQC) is the application of statistical methods to monitor and control the quality of a product or...
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Statistical Quality Control Advanced (SQC-02)
Statistical process control (SPC) or statistical quality control (SQC) is the application of statistical methods to monitor and control the quality of a product or...
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Monte Carlo Simulation (MCS) using R
In this course, you will learn to generate Continuous, Discrete and Categorical Data (Xs) Using Statistical Distributions, create transfer functions, and use R software to...
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Machine Learning Techniques (ML-03) onwards
Delve deeper into Machine Learning. Handle advanced techniques land know which Machine Learning model to choose for each type of problem
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Reliability Engineering (REL)
Reliability is often referred to as "quality over time". The REL course will give you a solid foundation in core concepts like strength/load analysis, normal,...
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Seven QC Tools: Classical (7QCT-01)
An in-depth guide to the seven traditional Quality Control (QC) tools. These quality control tools help refine performance and improve productivity.
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Seven QC Tools: Modern (7QCT-02)
An in-depth guide to the seven modern Quality Control (QC) tools. The modern toolset aids in promoting innovation, communicating information efficiently, and successfully planning major...
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Process Mapping (PMAP)
This program dives deep into the rationale behind process mapping, and gives you a practical perspective on how we can use it to improve productivity....
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Qualitative Tools and Techniques (QTT)
Qualitative tools allow you to deep dive into an issue to discover areas for growth, development, and improvement. This program takes you through the essential...
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