1650 Courses

In this course, we will master the syntax and structure of RSpec then learn to utilize test-driven development principles to design and implement clean test specs and reduce dependencies in the test suite by mocking objects with class and instance doubles. We will also explore the wide collection of RSpec matches available to test the code.

Decision Making and Problem Solving (Virtual) We may live in an era of fast technology and increasing reliance upon automation, but our human abilities to think critically, make careful decisions, and solve nuanced problems are more important than ever. Our personal lives depend on those things, and so do the lives of our organizations. Since business is now conducted at remarkable speeds, we put our organizations at great risk daily when we have weak competencies with decision-making and problem-solving. Decisions and solutions that are executed impulsively and without structured approaches can create more problems or make existing ones worse! This course aims to help participants improve their skills so they can execute well and add value to the workplace. Learners will experience multiple decision-making and problem-solving models, tools, and techniques meant for the real world. They will learn how to align their growing toolboxes with the right situational contexts so that they can transfer that skill to the workplace. They will also discover how indecision, cognitive bias, and default thought processes can create obstacles to effective decision-making and problem-solving. What you will Learn Recognize the importance of making a sound decision in a timely manner Infer types of cognitive biases and obstacles that impact decision-making Separate facts, requirements, ideas, and perceptions when making a decision or solving problems Apply structured decision-making and problem-solving approaches Conduct cause and effect and Force Field analyses Evaluate alternative solution methods using various techniques Analyze real world situations to determine the best aligned decision-making and problem-solving models, tools, and techniques Implement decision-making and problem-solving models, tools, and techniques Getting Started Foundation Concepts Contextualizing decisiveness and problem-solving Discriminating between decisiveness and problem-solving Understanding Decision-Making Decision-making challenges and impacts Key drivers of good decision-making Thought processes and obstacles Knowledge, skills, and abilities (KSAs) Decision-Making Models and Supporting Tools Decision-making models, tools, and guidelines Tools to evaluate alternatives Translating requirements into action Problem-Solving Defining the problem Problem-solving models Cause and effect analysis Quick hit vs. innovative problem-solving Summary and Next Steps Course summary Personal action plan

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

This Knife Skills course will enable you to chop, slice and dice like a Ninja!

With this 2-in-1 course, you will get access to AWS Technical Essentials and AWS Certified Solutions Architect - Associate certification exam content.

Learn new Android APIs such as RoomDatabase, ML Kit for face recognition, Cloud Firestore, Firebase, Maps, and the Android Studio IDE (integrated development environment)

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Learn Vue.js through a practical, project-based approach, along with understanding how to use the Vue CLI and Firebase storage

Overview Grow your knowledge of essential Healthcare Assistant ideas and procedures in patient care, interpersonal and effective communication abilities, assisting patients with a range of Healthcare Assistant requirements and ailments, safety procedures, and infection control regulatory frameworks. You will also learn about Healthcare Assistant moral issues and develop the confidence and abilities necessary to succeed in this rewarding Healthcare Assistant position. By taking this Healthcare Assistant training, you can try to make a real difference in the lives of those you care for. The Healthcare Assistant role is vital in providing essential support to patients and healthcare teams. With the right Healthcare Assistant training, you'll be equipped to handle the demands of the job and excel in the Healthcare Assistant field. Take action now! Enrol in our Healthcare Assistant Training course today to fulfil your potential as a Healthcare Assistant and have a significant influence in the Healthcare Assistant sector! How will I get my certificate? Upon successful completion of the Healthcare Assistant Training course, you'll be eligible to receive your certificate. You can conveniently order your certificate directly through our platform. Who is This course for? There is no experience or previous qualifications required for enrolment in this Healthcare Assistant Training. It is available to all students, of all academic backgrounds. Requirements Our Healthcare Assistant Training course is designed for maximum flexibility and accessibility: It is optimised for use on PCs, Macs, laptops, tablets, and smartphones. Study easily on your tablet or smartphone, accessible with any Wi-Fi connection. No time limit for completion; study at your own pace and on your own schedule. Basic English proficiency is required to ensure effective learning. Career Path Having this qualification will increase the value of your CV and open you up to multiple sectors, such as : Healthcare Assistant: £18,000 - £25,000 per year Senior Care Assistant: £22,000 - £30,000 per year Clinical Support Worker: £20,000 - £28,000 per year Healthcare Supervisor: £25,000 - £35,000 per year Nursing Assistant: £20,000 - £28,000 per year Note: Salaries vary based on experience, location, and industry. Course Curriculum 2 sections • 19 lectures • 08:08:00 total length •Module 1: Working in Different Healthcare Settings: 00:13:00 •Module 2: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1: 00:50:00 •Module 3: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2: 00:48:00 •Module 4: Maintaining Medical Records: 00:19:00 •Module 5: Confidentiality in a Medical Environment: 00:14:00 •Module 6: Health and Safety Responsibilities: 00:51:00 •Module 7: Hygiene in Nursing: 00:28:00 •Module 8: Infection Control: 00:58:00 •Module 9: Mobility and Immobility Issues of Patients in Nursing: 00:15:00 •Module 10: Rights and Responsibilities as a Health and Social Care Worker: 00:39:00 •Module 11: Role as A Caregiver and Healthcare Professional: 00:23:00 •Module 12: Providing Care or Treatment to People Who Lack Capacity: 00:14:00 •Module 13: Managing Service Delivery in Health and Social Care: 00:11:00 •Module 14: Medical Jargon and Terminology: 00:25:00 •Module 15: Effects of Covid-19 on Human Life: 00:19:00 •Module 16: Preventions and Social Measures to Be Taken: 00:28:00 •Module 17: Information Technology in Health Care: 00:14:00 •Module 18: Artificial Intelligence, Data Science and Technological Solutions against Covid-19: 00:19:00 •Assignment - Healthcare Assistant Training: 00:00:00

This Tableau Desktop Training course is a jumpstart to getting report writers and analysts with little or no previous knowledge to being productive. It covers everything from connecting to data, through to creating interactive dashboards with a range of visualisations in two days of your time. For Private options, online or in-person, please send us details of your requirements: This Tableau Desktop Training course is a jumpstart to getting report writers and analysts with little or no previous knowledge to being productive. It covers everything from connecting to data, through to creating interactive dashboards with a range of visualisations in two days of your time. Having a quick turnaround from starting to use Tableau, to getting real, actionable insights means that you get a swift return on your investment of time and money. This accelerated approach is key to getting engagement from within your organisation so everyone can immediately see and feel the impact of the data and insights you create. This course is aimed at someone who has not used Tableau in earnest and may be in a functional role, eg. in sales, marketing, finance, operations, business intelligence etc. The course is split into 3 phases and 9 modules: PHASE 1: GET READY MODULE 1: LAUNCH TABLEAU Check Install & Setup Why is Visual Analytics Important MODULE 2: GET FAMILIAR What is possible How does Tableau deal with data Know your way around How do we format charts Dashboard Basics – My First Dashboard MODULE 3: DATA DISCOVERY Connecting to and setting up data in Tableau How Do I Explore my Data – Filters & Sorting How Do I Structure my Data – Groups & Hierarchies, Visual Groups How Tableau Deals with Dates – Using Discrete and Continuous Dates, Custom Dates Phase 2: GET SET MODULE 4: MAKE CALCULATIONS How Do I Create Calculated Fields & Why MODULE 5: MAKE CHARTS Charts that Compare Multiple Measures – Measure Names and Measure Values, Shared Axis Charts, Dual Axis Charts, Scatter Plots Showing Relational & Proportional Data – Pie Charts, Donut Charts, Tree Maps MODULE 6: MAKE TABLES Creating Tables – Creating Tables, Highlight Tables, Heat Maps Phase 3: GO MODULE 7: ADD CONTEXT Reference Lines and Bands MODULE 8: MAKE MAPS Answering Spatial Questions – Mapping, Creating a Choropleth (Filled) Map MODULE 9: MAKE DASHBOARDS Using the Dashboard Interface Dashboard Actions This training course includes over 25 hands-on exercises and quizzes to help participants “learn by doing” and to assist group discussions around real-life use cases. Each attendee receives a login to our extensive training portal which covers the theory, practical applications and use cases, exercises, solutions and quizzes in both written and video format. Students must use their own laptop with an active version of Tableau Desktop 2018.2 (or later) pre-installed. What People Are Saying About This Course “Excellent Trainer – knows his stuff, has done it all in the real world, not just the class room.”Richard L., Intelliflo “Tableau is a complicated and powerful tool. After taking this course, I am confident in what I can do, and how it can help improve my work.”Trevor B., Morrison Utility Services “I would highly recommend this course for Tableau beginners, really easy to follow and keep up with as you are hands on during the course. Trainer really helpful too.”Chelsey H., QVC “He is a natural trainer, patient and very good at explaining in simple terms. He has an excellent knowledge base of the system and an obvious enthusiasm for Tableau, data analysis and the best way to convey results. We had been having difficulties in the business in building financial reports from a data cube and he had solutions for these which have proved to be very useful.”Matthew H., ISS Group