128 Statistics courses delivered Live Online

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)

Python Data Analytics with Python using Numpy, Pandas, Dataframes. Most attendees are in-work Data Professional. Private individuals are very welcome. Our Style: Hands-on, Practical Location: Online, Instructor-led

A workshop for independent schools that are inspected by Ofsted. Providing a proven strategy for ensuring compliance with the independent school standards.

About this course This practical course will help primary teachers to understand the purpose of assessment in history and consider current best practice. We will explore ways of continuing to improve the quality of teaching and learning in history through effective assessment strategies. We will go through practical ideas and resources to enliven teaching and learning in history and provide opportunities for assessment. This course will provide guidance and support to help develop the accuracy of teacher judgements. During the course, we will look at examples of writing and outcomes from different schools and consider how these outcomes demonstrate progress and attainment in history. Outcomes • understand the purpose of assessment in history • consider current best practice • explore ways of continuing to improve the quality of teaching and learning in history through effective assessment strategies • explore practical ideas and resources to enliven teaching and learning in history and provide opportunities for assessment • provide guidance and support to help develop the accuracy of teacher judgements Course leader The course is led by Steven Kenyon. Steven is a member of the Historical Association’s primary committee, having worked as a primary school teacher and then Deputy Head Teacher between 2004 and 2018. He joined Lancashire Professional Development Service in April 2018 as a Teaching and Learning Consultant for Primary History and English. He works closely with Lancashire Archives to promote and develop local history work in primary schools. This year he is a judge for the Historical Association's Young Quills Awards.

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

About this Virtual Instructor Led Training (VILT)  A decision to drill an exploration well with the objective to find a new oil or gas field must be based on sound assessment of the prospect risk and of the volumes. What is the chance that a well will find hydrocarbons, and how much could it be? Risk and volume assessments form the basis for decisions to drill a well or not, and as such form the link between subsurface evaluation and the business aspects of the petroleum industry. This Virtual Instructor Led Training (VILT) course explains how risks and volumes can be assessed in a realistic manner, based on a sound understanding of the geological details of the prospect as well as its regional geological setting and current play understanding. Participants of this VILT course will receive a softcopy of Risk and Volume Assessment Handbook which explains the concepts that are associated with probabilistic Risk & Volume (R & V) Assessment and contains many practical recommendations on how to translate geological understanding into meaningful inputs for probabilistic R &V assessments. The book is fully compatible with any probabilistic R & V tool in the industry. Training Objectives By the end of this VILT course, participants will be able to understand: The fundamentals of risk and volumes assessment; translating geological understanding into reasonable numbers and ranges. The difference between risk and uncertainty. Fundamentals of statistics; including explanation of distribution curves, understanding of expectation curves, do's and don'ts for adding risked volumes, and Bayes theorem. Uncertainty of trap, reservoir, seal and charge, illustrated by examples. Guidelines and exercises for estimating risks realistically and consistently. Calculating volume ranges for prospects and for portfolios of prospects; how to add prospect volumes for a correct representation of prospect portfolios. Incorporation of geophysical evidence (DHIs) in a realistic risk assessment. Target Audience This VILT course has been designed in the first place for geoscientists working in exploration, for prospect portfolio analysts and for their direct supervisors. It will also benefit staff from disciplines working closely with exploration staff, such as reservoir engineers, petrophysicists and geophysicists. Course Level Intermediate Training Methods Learning, methods and tools The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day. It is the intention to have at least 2 smaller exercises per day. Time will be reserved for recapitulation, questions and discussions. VILT will be conducted either via Zoom or Microsoft Teams. Presenting materials can easily be done on this platform. When participants need to ask a question, they can raise their hand, write notes or interrupt the Instructor by using their microphone. The presenter can switch to a screen where he/she can see all participants (also when each participant is sitting in another location e.g. at home). There is also a whiteboard functionality that can be used as one would use a flip chart. Exercises will be done on an online platform which provides each participant with a private work area that can be accessed by the Instructor to discuss the exercise in a similar manner as in a classroom course. Each topic is introduced by a lecture, and learning is re-enforced by practical exercises and discussions. Handout material in electronic format will be provided. Trainer Dr. Jan de Jager has a PhD in Geology from the University of Utrecht. He joined Shell in 1979 as an exploration geologist, and worked in several locations around the world such as Netherlands, Gabon, USA, Australia, Argentina, and Malaysia in technical and management positions. During the last 10 years of his career, he was responsible for the quality assurance of Shell's exploration prospects in many parts of the world and for upgrading and replenishing Shell's global exploration portfolio. During this period, he had also developed extensive expertise in Prospect Risk and Volume assessments for which he ran successful internal training programmes. Following his retirement from Shell in 2010, Dr Jan de Jager took on a position as part-time professor at the University of Amsterdam and also serves as a consultant exploration advisor for various E&P companies. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations

Lean Six Sigma Black Belt Certification Program: Virtual 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)

Some 60% of injuries at work are caused by lifting heavy objects. This powerful, practical programme is designed to help stop any of your staff from becoming the next statistic. 1 Introduction and objectives 2 Overview of Health and Safety Legislation and HSE Injury Statistics Health and Safety at Work Act 1974 Management of Health and Safety at Work Regulations (MHSWR) 1992 MHSWR 1999 specific duties to risk assess Manual Handling Operations Regulations (MHOR) 1992 Breakdown of injury statistics and costs of poor manual handling 3 The musculoskeletal system explained Prevention and ill-health Ergonomics RSI The spine in detail 4 Risk assessment General principles The TILE method Employees' duties Workplace scenarios

Course Objectives The goal of this course is to provide you with the knowledge required to use more advanced functions and formulas and work with various tools to analyse and present data in spreadsheets, such as sorting, filtering, applying conditional formatting and charting the data. ' Customer Feedback Really useful and engaging course. Learnt a lot that will be very beneficial in my job. Trainer was great. Kelly Moreley - TACT Very happy with the course. Worked as a good refresher from what I knew already and enhanced my knowledge further in formulas + vlookup and shortcut keys. Jenny Price - Acer 1 year email support service Take a look at the consistent excellent feedback from our corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke courses. Tailored training courses: In, in company training, you can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Recap on Excel formulas and calculations Overview of formulas in Excel Relative, Absolute and Mixed cell references Group editing worksheets Autofill and Flash Fill Changing Excel’s environment Options Changing the default number of sheets Creating an Autofill Custom List Adding tools to the Quick Access Toolbar Mastering Excel Tables Introducing Excel Tables Formatting a Table Creating Calculated Columns Using Slicers to filter your data Using Totals to get statistics out of your data Removing duplicates Converting Tables back to normal Ranges Using names Ranges In Excel formulas As a way of navigating through the workbook Advanced Formulas Simple IF examples Using IF to check if a cell is blank Nested IFs VLOOKUP HLOOKUP Text Functions Date Functions Conditional formatting Apply Conditional Formatting Customising Conditional Formatting Using Icons in Conditional Formatting Using Formulas to conditionally format cells Linking spreadsheets and workbooks Making a reference to another worksheet Making a reference to another workbook Editing links Troubleshooting links Analysing databases Quick analysis Sorting a database Apply filters to a database Advance filter Sorting and Filtering by Conditional Formats Charts Analyse trends in data using Sparklines Creating charts from start to finish Exploring the different Chart Types Apply Chart Styles Formatting Chart Elements Filtering Charts by Series or Categories Adding a Trendline to a Chart Create a Chart Template Attaching security to a spreadsheet and workbook Protect Cells Protect Structure of worksheets Protect a Workbook by adding passwords Introduction to Pivot Tables What are Pivot Tables? Using recommended pivot tables to analyse your data Who is this course for? Who is this course for? For those who want to explore in more detail formulas and functions, data analysis and data presentation. Requirements Requirements Preferably, delegates would have attended the Excel Introduction course. Career path Career path Excel know-how can instantly increase your job prospects as well as your salary. 80 percent of job openings require spreadsheet and word-processing software skills Certificates Certificates Certificate of completion Digital certificate - Included

Practical MySQL Course Intermediate, to leave you fully conversant with queries, DML and DDL statements. Hands-on, Practical MySQL Course Intermediate. PCWorkshops MySQL Course Intermediate Certificate. Max 4 people per course, we keep it personalised.