370 Data Visualization courses

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

If you've ever wondered how websites work behind the scenes or wanted to build one from scratch, this beginner-friendly course is your perfect starting point. HTML lays the foundation, CSS adds the style, and JavaScript brings everything to life—together they form the language of the web. This guide walks you through each of them clearly and cleverly, like a satnav that actually knows where it’s going (and doesn’t say “recalculating” every two minutes). There’s no fluff, no jargon-filled detours—just well-structured learning designed for absolute beginners who want to learn the essentials of front-end web development. Whether you're looking to understand the basics for a personal project or simply want to speak the same language as developers, this course gives you the knowledge to navigate the web-building world with confidence. It's straightforward, engaging, and structured to help you grasp each concept without losing the will to live. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: Getting Started Introduction Course Curriculum How to Get Course requirements Getting Started on Windows, Linux or Mac How to ask a Great Questions FAQ's Setting up Development Environment Introduction Choosing Code Editor Installing Code Editor(Sublime Text) Overview of a Webpage Full HTML Code Page First 'Hello World!' page Summary HTML Fundamentals Introduction Heading Paragraph Formatting text List Items Un Ordered List Items Ordered Classes ID's in CSS Comments in HTML Summary HTML Intermediate Introduction Images Forms Form Actions Br tag Marquee Text area Tables Links Navbar / Menu HTML Entities Div tag Google Maps Summary HTML Advanced Introduction HTML5 Audio HTML5 Video Progress Bar Drag & Drop Canvas I frames Input Types Input Attributes YouTube Video Linking Creating Responsive Page Summary HTML Expert Introduction Registration Form Login Form About Us Form Contact Us Form Footer Form Integrate All Together Coding Exercise Solution for Coding Exercise Summary HTML Website Project Introduction Challenge - HTML 5 Clock Face with Numbers Project Overview Conclusion on Project Summary CSS Fundamentals Introduction CSS Syntax Creating webpage with CSS Inline CSS Internal CSS External CSS CSS Classes CSS IDs Colors Backgrounds Floating Positioning Margins Padding Borders Summary CSS Intermediate Introduction Styling Text Aligning Text Styling Links Font Family Font Styles Applying Google Fonts Box Model Icons Tables Navigation-Menu Dropdowns Summary CSS Advanced Introduction Advanced Selectors Forms Website Layout Rounded Corners Color Keywords Animations Pseudo Classes Gradients Shadows Calculations Creating Responsive Page Summary CSS Expert Introduction Button Styles Flexbox CSS Grid Pagination Multiple Columns Image Reflection UI / UX Design Social Media Icons External CSS Style adding Coding Exercise Solution for Coding Exercise Summary CSS Website Project Introduction CSS Project Getting CSS Project Overview Summary JavaScript Getting Started What is JavaScript Installing Code Editor(Sublime Text) Installing Code Editor(Visual Studio Code) Hello World Program Getting Output Summary JavaScript Fundamentals Introduction Internal JavaScript External JavaScript Inline JavaScript Async and defer Variables Data Types Numbers Boolean Arrays() Objects Comments Summary Strings Introduction Strings String Formatting String Methods Summary Operators Introduction Arithmetic operators Assignment operators Comparison operators Logical operators Summary Conditional Statements Introduction If statement If-else statement If-else-if statement Switch-case statement Summary Control Flow Statements Introduction While loop Do-while loop For loop Break Continue Coding Exercise Solution for Coding Exercise Summary Functions Introduction Creating a Function Function Call() Function with parameters Function Bind() Summary Data Visualization(Google Chats) Introduction How to Use Google chart script Line Graph chart Scatter plots chart Bar chart Pie chart 3D Pie chart Summary Error Handling Introduction Try-catch Try-catch-finally Summary Client-side Validations Introduction On Submit Validation Input Numeric Validation Login Form Validation Password Strength Check Validation Summary Publish Your Website for Live Introduction Installing Free FTP Solution (FileZilla) Setting Up FTP (File Transfer Protocol) Publish Website to Hosting Server Summary Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn to Code HTML, CSS, JavaScript: Guide for Beginners course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn to Code HTML, CSS, JavaScript: Guide for Beginners course is open to all, with no formal entry requirements. Career path Upon successful completion of the Learn to Code HTML, CSS, JavaScript: Guide for Beginners Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

The Real Estate Investment School is a four day course, that unravels the complexity of Investment Cash Flows through Advanced Modelling, Interpretation and Theory.

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

🚀 Unlock Your Excel Potential with the Microsoft Excel Crash Course 2024: Masterclass for Weekend! 🚀 Are you tired of spending endless hours trying to figure out Microsoft Excel on your own? Do you want to boost your skills and become an Excel pro without sacrificing your entire weekend? Look no further - our Microsoft Excel Crash Course 2024 is here to revolutionize the way you work with spreadsheets!   🔥 Why Excel Matters: In today's data-driven world, Excel proficiency is a non-negotiable skill. Whether you're a student, a professional, or someone looking to enhance your productivity, mastering Excel can open doors to new opportunities.   💡 What Sets Our Masterclass Apart: ✅ Weekend-Friendly Format: We get it - your time is precious! That's why our course is designed to be completed over a weekend, giving you the skills you need without disrupting your busy schedule. ✅ Hands-On Learning: Forget boring lectures! Dive straight into hands-on, practical exercises that reinforce your learning. Our step-by-step approach ensures you grasp each concept thoroughly. ✅ Up-to-Date Content: Excel evolves, and so do we! Our 2024 edition is packed with the latest features and best practices, ensuring you're on the cutting edge of spreadsheet mastery. ✅ Expert Guidance: Learn from seasoned Excel experts who've cracked the code to efficient and effective spreadsheet use. Benefit from their real-world insights and insider tips that go beyond basic functions. ✅ Lifetime Access: Purchase once, benefit forever! Gain access to our course materials, updates, and a supportive community, ensuring you stay on top of your Excel game for years to come.   📚 Course Highlights: 👉 Essential Excel Functions: From basic formulas to advanced functions, we cover it all. 👉 Data Visualization: Transform your data into impactful charts and graphs. 👉 Pivot Tables Mastery: Unlock the power of data analysis with Pivot Tables. 👉 Time-Saving Shortcuts: Work smarter, not harder, with keyboard shortcuts. 👉 Advanced Formulas: Impress your colleagues with advanced calculations. 👉 Automation with Macros: Streamline your tasks with Excel automation. 👉 Error Handling: Learn how to troubleshoot and avoid common mistakes.   🎓 Who Should Enroll: Professionals looking to enhance their spreadsheet skills. Students aiming to boost their academic performance. Entrepreneurs and business owners seeking data-driven insights. Anyone wanting to save time and increase productivity.   🌟 Get Ready to Excel! Don't miss out on this opportunity to become an Excel maestro in just one weekend! Enroll now in the Microsoft Excel Crash Course 2024: Masterclass for Weekend and empower yourself with the skills that will set you apart. 👉 Limited seats available. Secure your spot now! 👈 Disclaimer: Results may vary based on individual commitment and practice. Course Curriculum Module- 1 Introduction 00:00 Getting started on Windows, macOS, Android, and IOS 00:00 How to ask great questions 00:00 FAQ 00:00 Start Excel 00:00 Module- 2 Overview of top Excel tips 00:00 Add numbers like a pro 00:00 Save time by filling cells automatically 00:00 Did data enter into one column - Split it 00:00 Switch data around by transposing it 00:00 Sort and filter for quick results 00:00 Create a data table for tasks to make it easy 00:00 Make drop-down list 00:00 Quick Data Analysis 00:00 Great charts for your visualization 00:00 Pivot Tables for Data Analytics 00:00 Module- 3 Introduction 00:00 Basic formula operations 00:00 Mathematical functions level1 00:00 Mathematical functions level2 00:00 Text functions level1 00:00 Text functions level2 00:00 Logical functions 00:00 Date time functions 00:00 V Lookup formula 00:00 HLookup formula 00:00 HLookup Match formula 00:00 Match Index formula 00:00 Module- 4 Introduction 00:00 XLookup 00:00 Handling NA and Approximates match in XLookup 00:00 Wildcard matching in XLookup 00:00 Module- 5 Introduction 00:00 Split Text into columns 00:00 Flash Fill 00:00 Remove Duplicates 00:00 Data Validation 00:00 Get - import Data from Text 00:00 Get - import Data from CSV 00:00 Module- 6 Introduction 00:00 Creating Pivot Table 00:00 Value field settings 00:00 Number format 00:00 Pivot Table Design 00:00 Excel Exercise 00:00 Solution for Excel Exercise 00:00 Module- 7 Introduction 00:00 Excel Charts - Categories 00:00 Elements of a chart 00:00 Easy way to create charts 00:00 Column or Bar charts 00:00 Formatting charts 00:00 Line charts 00:00 Area charts 00:00 Pie and Doughnut charts 00:00 Format Area Plot or XY chart 00:00 Scatter or bubble charts 00:00

This course is designed for students who already have foundational knowledge and skills in Excel and who wish to perform robust and advanced data and statistical analysis with Microsoft Excel using PivotTables, use tools such as Power Pivot and the Data Analysis ToolPak to analyze data and visualize data and insights using advanced visualizations in charts and dashboards in Excel.

Duration 2 Days 12 CPD hours This course is intended for The audience for this course includes professionals who are new to Looker who are interested in leveraging Looker for data analysis, visualization, and reporting. The course is designed for individuals seeking to gain a comprehensive understanding of Looker's functionalities and apply these skills in their organizations to drive data-driven decision-making. Overview This 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, you'll explore and gain: Comprehensive understanding of Looker's platform: Gain a solid foundation in Looker's key features, functionality, and interface, enabling you to navigate and utilize the platform effectively for your data analysis and visualization needs. Mastery of LookML and data modeling: Develop proficiency in Looker's unique data modeling language, LookML, to create customized and efficient data models that cater to your organization's specific requirements. Expertise in creating insightful Explores: Learn to build, customize, and save Explores with dimensions, measures, filters, and calculated fields, empowering you to analyze your data and uncover valuable insights. Proficiency in dashboard design and sharing: Acquire the skills to design visually appealing and informative dashboards, share them with different user roles, and schedule exports to keep stakeholders informed and up-to-date. Enhanced content organization with folders and boards: Understand how to effectively use folders and boards to organize, manage, and discover content within Looker, making it easily accessible for you and your team. Optional: Advanced visualization techniques for impactful storytelling: Master advanced visualization techniques, including customizations with HTML, CSS, and JavaScript, and interactive visualizations using Looker's API, to create compelling data stories that resonate with your audience. Discover the power of data analytics and visualization with our hands-on, two-day introductory course Looker Bootcamp: Analyzing and Visualizing Data with Looker. Designed for professionals who want to unlock valuable insights from their data, this immersive training experience will guide you through Looker's cutting-edge features and provide you with the essential skills to create engaging, interactive, and insightful reports and dashboards. Our experienced trainers will take you on a journey from the fundamentals of Looker and its unique data modeling language, LookML, to advanced visualization techniques and content organization strategies, ensuring you leave the course equipped to make data-driven decisions with confidence. Throughout the course, you will have the opportunity to participate in practical exercises and workshops that will help you apply the concepts and techniques learned in real-world scenarios. You will explore the potential of Looker's Explores, dive into LookML's capabilities, and master the art of dashboard design and sharing. Learn how to organize and manage your content with folders and boards and harness the power of advanced visualization techniques to make your data come alive. Getting Started with Looker Overview of Looker and its key features Navigating the Looker interface Looker terminology and basic concepts Connecting to Data Sources Setting up and managing data connections Exploring database schemas Understanding LookML: Looker's data modeling language Creating and Customizing Explores Building and customizing Explores Adding dimensions, measures, and filters Creating calculated fields Saving and organizing Explores Data Visualization Creating visualizations using Looker's visualization library Customizing chart types, colors, and labels Displaying visualizations in dashboards Introduction to Looker's API for custom visualizations Advanced Explores and LookML LookML refresher and best practices Creating derived tables and data transformations Managing access controls and data permissions Organizing and Sharing Content with Folders and Boards Introduction to folders and boards in Looker Creating and managing folders for organizing content Setting up boards for easy content discovery Sharing folders and boards with different user roles and permissions Dashboard Design and Sharing Best practices for dashboard design Adding, arranging, and resizing visualizations Scheduling and exporting dashboard data Advanced Visualization Techniques Customizing visualizations with HTML, CSS, and JavaScript Creating interactive visualizations using Looker's API Integrating Looker visualizations with other tools Hands-on Workshop and Project Participants work on a guided project to apply the skills learned Trainer provides individual support and guidance Project Presentations, Q&A, and Training Wrap-up Additional course details: Nexus Humans Looker Bootcamp: Analyzing and Visualizing Data with Looker (TTDVLK02) 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 Looker Bootcamp: Analyzing and Visualizing Data with Looker (TTDVLK02) 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.

Take this tremendous opportunity to transform yourself into highly skilled and recognised python experts by enrolling on our

Step into the world of data analysis and gain practical experience analyzing real-world datasets with the help of this course. This course will not only guide you in analyzing data efficiently in Python from scratch but also help you in conducting your own analysis with Python and extracting valuable insights that can transform your business!

Bayfield Training and the University of Cambridge Department of Land Economy This prestigious eight-week online Real Estate course is suitable for modellers new to Real Estate and experienced Real Estate Analysts looking to formalise their skill set. This course will equip you with skills to start building your own financial models and the certificate will give your employers and colleagues reassurance of your expertise. If you want to enhance your financial modelling skills over an extended period, the Real Estate Financial Modelling Certificate is the course for you. This online real estate course has been put together exclusively for the Real Estate Financial Modelling certificate, using state of the art digital resources such as animations, video-steps, digital whiteboard and video-interactions as well as the spreadsheets, text based resources and tutor contact you would expect from any of Bayfield Training’s classroom based courses.  Assessment is in the form of a guided model build exercise and written model appraisal. Participants benefit from an additional months access to the course platform after submission of the assessment. On this course you will: Become a competent model builder, building Real Estate Financial Models from scratch Learn tricks and concepts from financial modelling experts with decades of experience in practice and academia Learn how to use Real Estate Financial Models to make informed investment decisions Learn at a consistent pace over 8 weeks allowing you to take the time to fully grasp this important skill This course is suitable for: Chartered Surveyors Asset Managers Financial Controllers Financial Analysts Investment Managers Property Managers Real Estate Students/Recent Graduates Course Outline: Module 1 - Economic Context Introduction to Real Estate Asset Modelling and how it relates to and is distinct from Econometric Models. Introduction to Real Estate Asset Modelling and how it relates to and is distinct from Econometric Models. Why Real Estate Asset Modelling is important Understanding the occupier, asset and development markets and the relevant modelling approaches for each Real Estate Sub-Sector Key Performance Indicators Introduction to conventional valuations and financial mathematics Understanding and minimising errors in Financial Models Module 2 - Cash Flow Fundamentals Constructing a financial model from first principles. Understanding all the components of a basic cash flow model Understand why Corporate Finance Models and Real Estate Models are different Financial Model Design Making the link between valuations, income mathematics and discounted cash flows Internal Rate of Return, Net Present Value,Worth and other metrics Features and techniques to aid fast model building Module 3 - Developing the Cash Flow Adapting financial models for different periodicities and building rent functions for different rent behaviour. Understanding how leases vary with respect to rent over time: Rent Reviews, Break Clauses, Rent Free Periods, Lease Expiries etc. Developing the concept of a Rent Function in Excel Logic mathematics and Logic functions Projecting rent to adapt to different lease contracts and growth patterns Alternative solutions to Logic functions Comparative analysis of lease structures in different jurisdictions and adapting financial models Module 4 - Real Estate Data Understanding the different sources of data, constructing basic time series models and recognising basic patterns. Key Property market indicators Characteristics and application of key input metrics for Real Estate Appraisals Understanding the eight components of Real Estate Price Dynamics Awareness of the different sources of data Reading and using general property market reports Constructing basic time series models and recognising basic patterns Module 5 - Development Appraisals Constructing a development appraisal from first principles and understanding development return metrics. Understanding the key components of a development project Understanding the difference between development and investment appraisals Residual Appraisals and Profit Calculations Cost orientated cash flows and phased sales Cumulative construction cost patterns: incidental, fixed, loaded and S-curve Development return metrics and Modified IRRs Module 6 - Multi-let Cash Flows Constructing an advanced multi-let cash flow model and learn different techniques to build flexible rent functions. Multi-let and portfolio model design principles Building complex date functions and date series The three multi-let rent projection techniques Perpendicular Rent Functions Incorporate advanced rent adjustments into the Rent Function Incorporate sector and period varying rental growth rates Simplifying OPEX and CAPEX projections Discounting techniques on complex and volatile cash flows Module 7 - Project Finance Constructing a flexible Real Estate Debt Finance model. Revision of the Mathematics of amortisation and debt finance Basic senior debt models and geared net cash flow LTV, IRR and Interest Rate Dynamics Flexible term, repayment options and deferred interest Loan covenant tests Understanding different tranches of debt Understanding how complex debt structures impact the returns to different parties Module 8 - Model Interpretation and Risk Analysis Learn how to read, analyse and report on real estate financial models. Understand how to read models and develop an investment narrative Visualise model outputs using graph functions, conditional formatting, dynamic symbols and dashboards Learn how to use built-in Sensitivity, Scenario Analysis tools and third-party add-ins Learn how to construct varied project scenarios in a systematic way Introduction to Monte Carlo Analysis and VBA Optimising sale dates and other parameters Create well written, attractive and persuasive reports Included in the Course Bayfield Training and University of Cambridge Accredited Certificate & LinkedIn Proficiency Badge 64 CPD Hours 1 Month Post-Course Access to the Digital Platform - 12 Months Post-Course Access to the Platform can be purchased for an additional fee Course Files Q&A Webinars and Guest Speaker Webinars Further Learning Resources (Reading, Files and Videos) Post Course Support - Two Months of Questions & Answers 2024 Cohort Dates Include: 3rd June to 29th July 5th August to 25th September 2024 30th September to 25th November For more information, please contact Sam on the Bayfield Training Sales team: E - s.musgrave@bayfieldtraining.com T - 01223 517851 W - www.bayfieldtraining.com