57 Cleaning courses in Forfar delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This course is intended for students new to computer programming or experienced programmers who are new to client-side web development. Overview After completing this course, you will be able to: Describe the technologies involved in web development. Create HTML pages with links and images. Explain the benefits of CSS. Style HTML pages with CSS. Explain the concepts of objects, methods, and properties. Work with JavaScript variables. Create their own custom functions in JavaScript. Write flow control logic in JavaScript. Write JavaScript code that listens for and handles events, such as mouse clicks and page loads. Create forms with HTML and validate them with JavaScript. Use regular expressions in JavaScript for advanced form validation. This course is intended for IT professionals interested in becoming client-side web developers. who need to get a jump start on all three technologies. The course includes more than 30 exercises, providing a rapid hands-on introduction to the three major client-side languages: HTML, CSS, and JavaScript. Students should be prepared to cover a lot of ground quickly.This material updates and replaces course Microsoft course 20480 which was previously published under the title Programming in HTML5 with JavaScript and CSS3. Module 1: A Quick Overview of Web Development HTML is Part of a Team Client-side Programming Server-side Programming Web Development Technologies Module 2: Introduction to HTML Exercise: A Simple HTML Document Getting Started with a Simple HTML Document HTML Elements, Attributes, and Comments The HTML Skeleton Viewing the Page Source Special Characters HTML Elements and Special Characters History of HTML The lang Attribute Module 3: Paragraphs, Headings, and Text Paragraphs Heading Levels Breaks and Horizontal Rules Exercise: Paragraphs, Headings, and Text The div Tag Creating an HTML Page Quoted Text Preformatted Text Inline Semantic Elements Exercise: Adding Inline Elements Module 4: HTML Links Links Introduction Text Links Absolute vs. Relative Paths Targeting New Tabs Email Links Exercise: Adding Links Lorem Ipsum The title Attribute Linking to a Specific Location on the Page Targeting a Specific Location on the Page Module 5: HTML Images Inserting Images Image Links Adding Images to the Document Exercise: Adding Images to the Page Providing Alternative Images Module 6: HTML Lists Unordered Lists Ordered Lists Definition Lists Exercise: Creating Lists Module 7: Crash Course in CSS Benefits of Cascading Style Sheets CSS Rules Selectors Combinators Precedence of Selectors How Browsers Style Pages CSS Resets CSS Normalizers External Stylesheets, Embedded Stylesheets, and Inline Styles Exercise: Creating an External Stylesheet Exercise: Creating an Embedded Stylesheet Exercise: Adding Inline Styles div and span Exercise: Styling div and span Media Types Units of Measurement Inheritance Module 8: CSS Fonts font-family @font-face font-size font-style font-variant font-weight line-height font shorthand Exercise: Styling Fonts Module 9: Color and Opacity About Color and Opacity Color and Opacity Values Color Opacity Exercise: Adding Color and Opacity to Text Module 10: CSS Text letter-spacing text-align text-decoration text-indent text-shadow text-transform white-space word-break word-spacing Exercise: Text Properties Module 11: JavaScript Basics JavaScript vs. EcmaScript The HTML DOM JavaScript Syntax Accessing Elements Where Is JavaScript Code Written? JavaScript Objects, Methods, and Properties Exercise: Alerts, Writing, and Changing Background Color Module 12: Variables, Arrays, and Operators JavaScript Variables A Loosely Typed Language Google Chrome DevTools Storing User-Entered Data Exercise: Using Variables Constants Arrays Exercise: Working with Arrays Associative Arrays Playing with Array Methods JavaScript Operators The Modulus Operator Playing with Operators The Default Operator Exercise: Working with Operators Module 13: JavaScript Functions Global Objects and Functions Exercise: Working with Global Functions User-defined Functions Exercise: Writing a JavaScript Function Returning Values from Functions Module 14: Built-In JavaScript Objects Strings Math Date Helper Functions Exercise: Returning the Day of the Week as a String Module 15: Conditionals and Loops Conditionals Short-circuiting Switch / Case Ternary Operator Truthy and Falsy Exercise: Conditional Processing Loops while and do?while Loops for Loops break and continue Exercise: Working with Loops Array: forEach() Module 16: Event Handlers and Listeners On-event Handlers Exercise: Using On-event Handlers The addEventListener() Method Anonymous Functions Capturing Key Events Exercise: Adding Event Listeners Benefits of Event Listeners Timers Typing Test Module 17: The HTML Document Object Model CSS Selectors The innerHTML Property Nodes, NodeLists, and HTMLCollections Accessing Element Nodes Exercise: Accessing Elements Dot Notation and Square Bracket Notation Accessing Elements Hierarchically Exercise: Working with Hierarchical Elements Accessing Attributes Creating New Nodes Focusing on a Field Shopping List Application Exercise: Logging Exercise: Adding EventListeners Exercise: Adding Items to the List Exercise: Dynamically Adding Remove Buttons to the List Items Exercise: Removing List Items Exercise: Preventing Duplicates and Zero-length Product Names Manipulating Tables Module 18: HTML Forms How HTML Forms Work The form Element Form Elements Buttons Exercise: Creating a Registration Form Checkboxes Radio Buttons Exercise: Adding Checkboxes and Radio Buttons Fieldsets Select Menus Textareas Exercise: Adding a Select Menu and a Textarea HTML Forms and CSS Module 19: JavaScript Form Validation Server-side Form Validation HTML Form Validation Accessing Form Data Form Validation with JavaScript Exercise: Checking the Validity of the Email and URL Fields Checking Validity on Input and Submit Events Adding Error Messages Validating Textareas Validating Checkboxes Validating Radio Buttons Validating Select Menus Exercise: Validating the Ice Cream Order Form Giving the User a Chance Module 20: Regular Expressions Getting Started Regular Expression Syntax Backreferences Form Validation with Regular Expressions Cleaning Up Form Entries Exercise: Cleaning Up Form Entries A Slightly More Complex Example

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

If your organisation manages contractors then your staff need to understand the health and safety issues. This course is the answer. The expert trainer will set out clearly the legal responsibilities of all relevant parties and explore the practical application of these responsibilities with the course participants. The course will then examine the issues associated with the planning of work to be contracted out and the evaluation, selection, control and monitoring of contractors engaged to undertake the work. Although the main focus is on health and safety, the course will also explain how health and safety issues need to be integrated into your organisation's functional management processes to ensure effective control of contractors. The course will consider all types of contracted activities, including construction and maintenance, cleaning, security, plant installation, etc. This programme will give participants: A clear understanding of the organisation's legal responsibilities for managing contractors The information they need to assess the competence of contractors A practical understanding of risk assessment principles and the transfer of risk to contractors A step-by-step guide to the key aspects of managing contractors in practice, covering:Planning of the workSelecting contractorsHandover prior to work commencementDuring the workReviewing the work on completion Practical guidance on the integration of health and safety controls into organisational procedures for contractor management 1 Introduction Who are contractors? Why manage contractors? Different types of contractors Costs of poor contractor performance 2 Overview of health and safety law and liability Health and safety law and statutory duties Relevance of civil and criminal law Enforcement and prosecution 3 Relevant legislation for controlling contractors Health and Safety at Work Act 1974 Management of Health and Safety at Work Regulations 1999 Construction (Design and Management) Regulations 1994 (as amended, 2007) Other relevant legislation Contract law 4 Managing contractors in practice Exercise - how well is it happening? The objectives Five step approachPlanning of the workSelecting contractorsHandover prior to work commencementDuring the workReviewing the work on completion 5 Planning the work Scope and extent Risk assessment Interface and other activities Who controls what? Contract arrangements 6 Selecting the right contractor(s) Locating contractor organisations Selection the right contractors Assessing contractor competence Approved lists/frameworks Tender process 7 Pre-work commencement Co-ordination and co-operation Exchange of information Contractor risk assessments and method statements Permits to work Case study exercise 8 During contract work Communication and liaison Supervision and inspection of the work Inspection and reporting procedures Security issues Facilities and access 9 Reviewing work on completion Why, what and how? Achieving continuous improvement in contractor performance 10 Questions, discussion and review

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

About this Course This 5 full-day course provides a comprehensive understanding of all the commissioning procedures for combined cycle power plants. The Commissioning Management System (CMS) of combined cycle power plants is covered in detail in this course. This includes all the commissioning procedures and documents, purpose of commissioning, responsibilities, system description, organization, working parties, test teams, documentation, testing and commissioning schedules, test reports, safety, plant certification, and plant completion report. The course provides also a thorough understanding of all the commissioning requirements for gas turbines, steam turbines and auxiliaries, generator and auxiliaries, electrical equipment, switchgear equipment, switchgear and transformers. All the stages of the commissioning procedure are covered in-depth in this course. This includes preparation - planning various activities, pre-commissioning checks and tests, typical commissioning schedule, detailed tests and commissioning procedures and instructions for every component in a combined cycle power plant, instrumentation, trial run of the equipment, safety and precautions, commissioning of combined cycle power plant systems, safety rules clearance certificates, procedure for the control and handling of defects, commissioning reports, operational testing, first fire, generator synchronization, performance testing, heat rate testing, emission testing, contract testing, CO2 concentration tests, electrical full-load rejection test, duct burner testing, partial load stability test, and reliability test. This course is a MUST for anyone who is involved in the pre-commissioning or commissioning of any combined cycle power plant equipment because it provides detailed pre-commissioning checks and tests, and detailed tests and commissioning procedures and instructions for every component in a combined cycle power plant. In addition, the seminar provides an in-depth coverage of all preparation, planning activities, commissioning schedules, trial run of each combined cycle power plant equipment, safety and precautions, safety rules clearance certificates, procedures for handling defects, and commissioning reports. Training Objectives Pre-commissioning Checks and Tests, Detailed Tests and Commissioning Procedures and Instructions for Every Equipment in Combined Cycle Power Plants: Gain a thorough understanding of all pre-commissioning checks and tests, and all commissioning procedures and instructions for every equipment in combined cycle power plants Commissioning Management System (CMS) of Combined Cycle Power Plants: Discover the benefits of the CMS of combined cycle power plants including all commissioning procedures and documents, purpose of commissioning, responsibilities, system description, organization, working parties, test teams, documentation, testing and commissioning schedules, test reports, safety, plant certification, and plant completion report Commissioning Procedures and Instructions for Heat Recovery Steam Generators, Air Blow and Steam Blow of Steam and Gas Piping in Combined Cycle Power Plants: Learn about the commissioning procedures and instructions for heat recovery steam generators, chemical cleaning of heat recovery steam generators, air blow and gas blow of steam and gas piping in combined cycle power plants, safety valve setting and soot blowers Commissioning Procedures and Instructions for Gas Turbines and Steam Turbines: Gain a thorough understanding of all the commissioning procedures and instructions for gas and steam turbines and auxiliaries including acid cleaning of oil pipelines, lubrication and governing system (oil flushing and hydraulic testing), jacking oil system, governing system, regenerative system, barring gear, vacuum tightness test, first rolling of turbine and data logging Commissioning Procedures and Instructions for Generator and Auxiliaries: Discover all the commissioning procedures and instructions for generator and auxiliaries including generator, seal oil system, hydrogen gas system, stator water system, rolling and start-up of generators Commissioning Procedures and Instructions for Electrical Equipment: Learn about all the commissioning procedures and instructions for electrical equipment including switchyard equipment, switchgear, transformers and motors Operational Testing, Performance Testing, Heat Rate Testing, Emission Testing of Combine Cycle Power Plants: Gain a thorough understanding of operational testing, first fire, generator synchronization, performance testing, heat rate testing, emission testing, contract testing, CO2 concentration tests, electrical full-load rejection test, duct burner testing, partial load stability test, and reliability test of combined cycle power plants Target Audience Engineers of all disciplines Managers Technicians Maintenance personnel Other technical individuals Training Methods The instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all the delegates gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught to their own organization. Trainer Your specialist course leader has more than 32 years of practical engineering experience with Ontario Power Generation (OPG), one of the largest electric utility in North America. He was previously involved in research on power generation equipment with Atomic Energy of Canada Limited at their Chalk River and Whiteshell Nuclear Research Laboratories. While working at OPG, he acted as a Training Manager, Engineering Supervisor, System Responsible Engineer and Design Engineer. During the period of time, he worked as a Field Engineer and Design Engineer, he was responsible for the operation, maintenance, diagnostics, and testing of gas turbines, steam turbines, generators, motors, transformers, inverters, valves, pumps, compressors, instrumentation and control systems. Further, his responsibilities included designing, engineering, diagnosing equipment problems and recommending solutions to repair deficiencies and improve system performance, supervising engineers, setting up preventive maintenance programs, writing Operating and Design Manuals, and commissioning new equipment. Later, he worked as the manager of a section dedicated to providing training for the staff at the power stations. The training provided by him covered in detail the various equipment and systems used in power stations. In addition, he has taught courses and seminars to more than four thousand working engineers and professionals around the world, specifically Europe and North America. He has been consistently ranked as 'Excellent' or 'Very Good' by the delegates who attended his seminars and lectures. He written 5 books for working engineers from which 3 have been published by McGraw-Hill, New York. Below is a list of the books authored by him; Power Generation Handbook: Gas Turbines, Steam Power Plants, Co-generation, and Combined Cycles, second edition, (800 pages), McGraw-Hill, New York, October 2011. Electrical Equipment Handbook (600 pages), McGraw-Hill, New York, March 2003. Power Plant Equipment Operation and Maintenance Guide (800 pages), McGraw-Hill, New York, January 2012. Industrial Instrumentation and Modern Control Systems (400 pages), Custom Publishing, University of Toronto, University of Toronto Custom Publishing (1999). Industrial Equipment (600 pages), Custom Publishing, University of Toronto, University of Toronto, University of Toronto Custom Publishing (1999). Furthermore, he has received the following awards: The first 'Excellence in Teaching' award offered by PowerEdge, Singapore, in December 2016 The first 'Excellence in Teaching' award offered by the Professional Development Center at University of Toronto (May, 1996). The 'Excellence in Teaching Award' in April 2007 offered by TUV Akademie (TUV Akademie is one of the largest Professional Development centre in world, it is based in Germany and the United Arab Emirates, and provides engineering training to engineers and managers across Europe and the Middle East). Awarded graduation 'With Distinction' from Dalhousie University when completed Bachelor of Engineering degree (1983). Lastly, he was awarded his Bachelor of Engineering Degree 'with distinction' from Dalhousie University, Halifax, Nova Scotia, Canada. He also received a Master of Applied Science in Engineering (M.A.Sc.) from the University of Ottawa, Canada. He is also a member of the Association of Professional Engineers in the province of Ontario, Canada. 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 post training support and fees applicable Accreditions And Affliations

Duration 2 Days 12 CPD hours This course is intended for This is an Intermediate and beyond-level Tableau course geared for experienced Tableau users who wish to leverage Tableau's more advanced capabilities. Overview This skills-focused course combines expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert facilitator, students will learn how to: Understand what data works best with Tableau Desktop and how to shape and clean it appropriately to get Learn how to maximize flexibility from Tableau Desktop. Learn how Tableau Prep folds into the analytic cycle, and when to prep data in Tableau Prep vs. Tableau Desktop. Understand the terminology used in Tableau Prep. Know how Tableau Prep approaches data sampling. Create and understand data prep flows that address common scenarios encountered in data preparation, as applied to common data use cases Know how to view data prepared in Tableau Prep using Tableau Desktop. Understand data exploration and validation in Tableau Prep and Tableau Desktop. Geared for experienced Tableau Users, Tableau Prep Building (Tableau Data Prep) for Experienced Users is a two-day hands-on course designed to provide you with the tools and knowledge of how to prepare and shape data in Tableau Prep. It?s best suited for people who have 3-6 months experience in Tableau Desktop and are somewhat familiar with writing calculations. Throughout the course, our instructors will take you from conceptual data preparation material to creating useful Tableau Prep flows that can be output to Tableau Desktop for analysisNOTE: The Tableau Training Series is independent-format training that can be tuned and adjusted to best meet your needs. Our materials are flexible, comprehensive, and are always instructed by a senior instructor with a deep understanding of Tableau and its most current features, benefits and functionality in a wide array of uses. This is not Official Tableau Training. Course Outline Introduction to the workspace Introduction to the workflow Data literacy concepts Connecting to and configuring data Exploring data Cleaning data Preferred data structures in Tableau Shaping data Combining data Opening a data sample and creating an output file Best practices for data preparation Complex flows Starting with a question Hands-on data preparation Additional course details: Nexus Humans Tableau Prep Building (Tableau Data Prep) for Experienced Users (TTDTAB010) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Tableau Prep Building (Tableau Data Prep) for Experienced Users (TTDTAB010) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Data Science for Marketing Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This course starts with the basics then moves seamlessly to an intermediate level. It includes a comprehensive yet balanced look at the four main components that make up Power BI Desktop: Report view, Data view, Model view, and the Power Query Editor. It also demonstrates how to use the online Power BI service. It looks at authoring tools that enables you to connect to and transform data from a variety of sources, allowing you to produce dynamic reports using a library of visualisations. Once you have those reports, the course looks at the seamless process of sharing those with your colleagues by publishing to the online Power BI service. The aim of this course is to provide a strong understanding of the Power BI analysis process, by working with real-world examples that will equip you with the necessary skills to start applying your knowledge straight away. 1 Getting started The Power BI process Launching Power BI Desktop The four views of Power BI Dashboard visuals 2 Connecting to files Connect to data sources Connect to an Excel file Connect to a CSV file Connect to a database Import vs. DirectQuery Connect to a web source Create a data table 3 Transforming data The process of cleaning data Column data types Remove rows with filters Add a custom column Append data to a table Fix error issues Basic maths operations 4 Build a data model Table relationships Manage table relationships 5 Merge queries Table join kinds Merging tables 6 Create report visualisations Creating map visuals Formatting maps Creating chart visuals Formatting chart Tables, matrixes, and cards Control formatting with themes Filter reports with slicers Reports for mobile devices Custom online visuals Export report data to Excel 7 The power query editor Fill data up and down Split columns by delimiter Add conditional columns Merging columns 8 The M formula Creating M functions Create an IF function Create a query group 9 Pivot and unpivot tables Pivot tables in the query editor Pivot and append tables Pivot but don't summarise Unpivot tables Append mismatched headers 10 Data modelling revisited Data model relationships Mark a calendar as a date table 11 Introduction to calculated columns New columns vs. measures Creating a new column calculation The SWITCH function 12 Introduction to DAX measures Common measure categories The SUM measure Adding measures to visuals COUNTROWS and DISINCTCOUNT functions DAX rules 13 The CALCULATE measure The syntax of CALCULATE Things of note about CALCULATE 14 The SUMX measure The SUMX measure X iterator functions Anatomy of SUMX 15 Introduction to time intelligence Importance of a calendar table A special lookup table The TOTALYTD measure Change year end in TOTALYTD 16 Hierarchy, groups and formatting Create a hierarchy to drill data Compare data in groups Add conditional formatting 17 Share reports on the web Publish to the BI online service Get quick insights Upload reports from BI service Exporting report data What is Q&A? Sharing your reports 18 Apply your learning Post training recap lesson

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice