Training Data analysis e-learning or face-to-face
Search for cause-and-effect relationships & correlations in data
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Data analysis training
Duration
14h
Rate
250 €Ht/pers
Using statistical tools to understand your data
The Data Analysis training course will help you understand the main statistical tools used to analyze data. Graphical analysis, statistical tests, laws of probability: all these tools will enable you to go further in the analysis of your data and understand what's going on.
At the end of this course, you'll be able to make decisions based on sound analysis. By understanding and exploiting data rigorously, you'll be able to reduce uncertainties, quickly identify anomalies or inefficiencies, and better anticipate trends or adjustment needs. This will lead to better overall performance, increased competitiveness, and improved risk management.
At the end of this course, you'll be able to make decisions based on sound analysis. By understanding and exploiting data rigorously, you'll be able to reduce uncertainties, quickly identify anomalies or inefficiencies, and better anticipate trends or adjustment needs. This will lead to better overall performance, increased competitiveness, and improved risk management.
Competitive benefits
Data Analysis training is lively and interactive. Numerous exercises on Ellistat's Data Analysis software will enable you to put theory into practice. The resources and assistance provided by our teams will make it easier for you to assimilate complex concepts.
100%
From a satisfied Ellistat trainee
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Training available in e-learning, mix-learning or face-to-face.
99%
Trainees recommend our training courses.
*September 2021 to September 2024
Objectives
- Prove the validity of a hypothesis with a statistical test and interpret the result.
- Understand the use of descriptive and inferential statistics.
- Know how to use the right statistical test.
- Understand the difference between simple regression and multiple regression (linear and non-linear).
For whom
Data analysis training is designed for engineers, supervisors or technicians who have production or test results to interpret, or who are looking for cause-effect relationships or correlations in a data table.
Prerequisites
- Basic use of the Internet and a web browser.
- Level 4 diploma and/or 2 years' professional experience.
Duration
14 hours of animated 100% e-Learning, certification practice quizzes, implementation of theoretical points on industrial simulators. The e-Learning is available 24/7 for 1 month for this training course.
Teaching and technical resources
- 100% E-Learning training on a dedicated platform
- Our teaching methods are mediatized (voice, text, exercises), fun and multimodal, with tests at every lesson.
- Theoretical presentations
- Case studies
- Online availability of PDF and Excel support documents
Teaching team
With over 30 years' experience, rich in teaching and practical experience, our industrial quality training organization provides you with training and consulting services to develop and improve your performance and know-how. All our consultants are Master Black Belt Lean Six Sigma and have at least 10 years' experience in applying Lean and Six Sigma tools in industrial environments.
Accessibility
The Data Analysis training course is accessible to people with disabilities. Please contact us for specific accommodation options. We will do our utmost to accommodate you.
Evaluation methods
- Attendance sheets.
- Oral or written questions (MCQs).
- Case studies.
- Practical work
Program
Descriptive statistics, Graphs
- Identify the benefits of graphical representation
- Quantitative variables
- Qualitative variables
- Mixed quantitative/qualitative case
Descriptive statistics, discrete distributions
- Probability basics
- Binomial distribution, Hypergeometric distribution and Poisson distribution
- Single sampling control
Descriptive statistics, continuous distributions
- Origin of the Gaussian distribution
- The parameters of a Gaussian distribution
- Validate the normality hypothesis
- Testing for outliers
- Student's distribution
- Normality analysis Skewness and Kurtosis
- Distribution of averages and confidence intervals
- Distribution of variances and confidence intervals
Inferential Statistics
- Hypothesis testing
- Alpha and beta risks
- Power of a test
Frequency comparison
- Various tests
- Compare a frequency with a theoretical frequency (1P)
- Comparing two frequencies (2P)
- Compare more than two frequencies (independence table)
Comparison of averages
- Compare an Average to a theoretical Average (z and theoretical t)
- Compare two Averages (t)
- Compare more than two Averages (ANAVAR)
- Dissociating matched cases
Variance comparison
- Comparing a Variance to a Theoretical Variance
- Comparing two Variances
- Compare more than two Variances
Non-parametric tests
- Understanding the benefits of non-parametric tests
- Principle of the main non-parametric tests
- Simple examples of non-parametric tests (signs and B to C)
- Application to sensory measurement
- Theoretical and matched comparison: Wilcoxon test
- Comparison of two populations: Mann Whitney test
- Comparison of more than two populations Krustal-Wallis, Mood, Friedman, Page test
Simple regression
- Principles and calculations
- Hypothesis testing of coefficients
- R² interpretation
- Non-linear regression
Multiple regression
- Principles, calculations and interpretation
- The benefits of multiple regression
- Non-linear responses
- Qualitative factors
Your feedback
Two modes learning
- E-learningSelf-paced learning250€
- 14h e-learning courses
- Online support
- PresentialIn-company training2
- 2-day statistical testing course
- 4-day statistical analysis course