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Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

IASSC lean six sigma green belt course. Online, 24/7 access to content and exam. Fee includes learning content, live webinars, tutor support, and official IASSC exam.

I Asked A Python Programmer For A Joke. He Said, 'Import Antigravity' | 10 QLS Endorsed Courses for Python Programmer | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support

[vc_row][vc_column][vc_column_text] Description: Want to learn the secrets of the stock market? If you're considering becoming a stock market trader, take the steps to fast and smart cash with this stock market trading course and learn how to trade stocks online. This Diploma in Stock Market Course is designed to explore the online stock market and financial markets in detail. You will learn all about stocks and shares investments, and their variable differences. In this stock market masterclass, you will get an introduction to market trends, understand currency conversion and statistics, and explore the risks and rewards of trading. Every stock trader should be aware of the risks and in this course, you will learn the basics of risk management. You will also gain an understanding of when to invest and review different options in trading. Throughout this stock trading course, you will familiarise with the role of a professional trader and trading strategies, as well as common mistakes to avoid in stock trading. Other topics covered include Forex trading, buzz words and technical analysis. Assessment: At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam. Certification: After you have successfully passed the test, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at a cost of £39 or in PDF format at a cost of £24. PDF certificate's turnaround time is 24 hours and for the hard copy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. [/vc_column_text][/vc_column][/vc_row] Stock Market Basics Introduction To Stocks Basics 00:30:00 About Share Basics 00:30:00 The Difference Between Stocks And Shares 00:30:00 Concept Of Fundamentals Of The Stock Market 00:30:00 How Exactly Do Stock Prices Get Determined? 00:30:00 Benefits Of Using Stocks And Shares 01:00:00 When To Get Out Of The Stock Market 00:30:00 Wrapping Up 00:15:00 Stock Market Statistics Understanding the stock market 01:00:00 Identifying trends 01:00:00 What is Forex? 01:00:00 Basics of currency conversion 01:00:00 Understanding Statistics 01:00:00 Forex Volatility And Market Expectation 01:00:00 Aspects Of The Trade 01:00:00 Risk Management 01:00:00 'Buzz' Words 01:00:00 Expert Trading Options 01:00:00 Other Trading Options 01:00:00 In Review 01:00:00 One Final Option 00:30:00 Refer A Friend Refer A Friend 00:00:00 Mock Exam Mock Exam- Diploma in Stock Market 00:30:00 Final Exam Final Exam- Diploma in Stock Market 00:30:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

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The Horticulture Diploma & Organic Gardening Certification Online course offers comprehensive training in horticulture, covering topics such as botany, soil science, plant nutrition, plant pests and diseases, organic gardening concepts, and landscaping. Participants will also learn about plant propagation, indoor plants, ornamental horticulture, and permaculture, preparing them for a rewarding career in horticulture and organic gardening. Learning Outcomes: Understand the principles of botany and plant classification. Gain knowledge of soil science and the nutritional requirements of plants. Identify and manage plant pests and diseases in an organic gardening context. Learn techniques for weed control, plant propagation, and selecting native and exotic plants. Acquire skills in lawn care, indoor plant maintenance, and plant protection in different settings. Discover the essentials of landscaping, garden design, and ornamental horticulture. Master the principles and practices of permaculture and arboriculture in horticulture. Gain insights into horticulture statistics and the latest developments in the industry. Who is this course for? This Horticulture & Organic Gardening does not require you to have any prior qualifications or experience. You can just enrol and start learning. The course is for anyone who wants to learn more about organic gardening and horticulture or start their own organic garden. Prerequisites This Horticulture & Organic Gardening was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Horticulture & Organic Gardening is a great way for you to gain multiple skills from the comfort of your home. This course is ideal for  Gardeners Garden Associates Research Scientists Horticulture Consultants Horticulture Inspectors Horticulture Technicians Plant Pathologists Groundskeepers Garden Maintenance Officers Landscape Assistants Course Curriculum Module 01: Basic Botany Basic Botany 00:28:00 Module 02: Plant Classification Plant Classification 00:11:00 Module 03: Soil Science Soil Science 00:23:00 Module 04: Plant Nutrition Requirements Plant Nutrition Requirements 00:22:00 Module 05: Plant Pests and Diseases Plant Pests and Diseases 00:41:00 Module 06: Basic Concepts of Organic Gardening Basic Concepts of Organic Gardening 00:21:00 Module 07: Weed Control Weed Control 00:20:00 Module 08: Plant Propagation Plant Propagation part 01 00:31:00 Plant Propagation Part 02 00:42:00 Module 09: Amenity Horticulture, Plant Selection and Native Plants Amenity Horticulture, Plant Selection and Native Plants 00:36:00 Module 10: Exotic Plants Exotic Plants 00:35:00 Module 11: Indoor Plants Indoor Plants 00:30:00 Module 12: Lawns Lawns 00:13:00 Module 13: Planting and Plant Care Planting and Plant Care 00:10:00 Module 14: Plant Nodes and Indigenous Plants Plant Nodes and Indigenous Plants 00:21:00 Module 15: Plant Protection, Landscaping and Garden Design Plant Protection, Landscaping and Garden Design 00:23:00 Module 16: Ornamental Horticulture Ornamental Horticulture 00:20:00 Module 17: Permaculture Permaculture 00:16:00 Module 18: Arboriculture Arboriculture 00:11:00 Module 19: Horticulture Statistics Horticulture Statistics 00:33:00 Module 20: Results from the 2018 Seasonal Labour in Horticulture End of Year Results from the 2018 Seasonal Labour in Horticulture End of Year 00:35:00

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Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

There is a common misconception that sharps injuries are only of concern to medical and care staff. However it is not uncommon for people in other industries such as waste disposal to come into contact with used sharps. In this course we'll start by looking at some statistics relating to discarded needles, then define 'sharps' and look at the different types that can be found in 'sharps litter', ranging from needles, syringes and scalpel blades to broken glass, knives, scissors and nails. It then covers the primary and secondary risks from sharps. It'll look at the responsibilities of employers and how they must use risk assessment to ensure workers safety, as much as possible. Then, the course will take a detailed look at Hepatitis B, Hepatitis C, HIV and Tetanus There'll be a section on the Chain of Infection, helping you to understand how an infection gets passed on, and what steps can be taken to break the chain and stop the process. It'll look at the probability of picking up an infection from a needlestick injury, and the factors that can affect this. It's important to always handle discarded sharps safely so the course will take you through the equipment you need, including litter pickers, forceps, disinfectant sprays and gloves, along with the correct techniques you should employ to avoid injury. This subject will be further expanded on by detailing the correct sharps handling procedures, including how to dispose of discarded sharps safely, how to remove disposable gloves to minimise cross-contamination, and correct hand washing procedures. Finally, it'll cover how to report discarded sharps and the correct procedures to follow if you're unlucky enough to receive a sharps injury.