362 Data Visualization courses

This compact crash course teaches learners to optimize their Microsoft Power BI use, gain insights into circular dependency errors and the new DAX functions, and use Power BI template files to enhance data modeling/analysis. The course provides guidance and real-world examples to streamline Power BI projects and achieve data visualization goals effectively.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

14 in 1 "IT and Analytics Training Training" Bundle only at £ 79 Get Hard Copy + PDF Certificates + Transcript + Student ID Card + e-Learning App as a Gift - Enrol Now Tired of browsing and searching for a IT and Analytics Training course you are looking for? Can't find the complete Training package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this 14-in-1 extensive IT and Analytics Training bundle that has everything you need to succeed in IT and Analytics Training and other relevant fields! After surveying thousands of learners just like you and considering their valuable feedback, this all-in-one Training bundle has been designed by industry experts. We prioritised what learners were looking for in a complete IT and Analytics Training package and developed this in-demand Training course that will enhance your skills and prepare you for the competitive job market. Furthermore, to help you showcase your expertise in this Training, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These Training certificates will enhance your credibility and encourage possible employers to pick you over the rest. This Bundle Consists of the following Premium courses: Course 01: Introduction to Data Analysis Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step Course 12: IT For Recruiters Enrol now to advance your career, and use the premium study materials from Apex Learning. Benefits you'll get from choosing Apex Learning for this training: Pay once and get lifetime access to 14 CPD courses Free e-Learning App for engaging reading materials & helpful assistance Certificates, student ID included in a one-time fee Free up your time - don't waste time and money travelling for classes Accessible, informative modules designed by expert instructors Learn anytime, from anywhere Study from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (For The Title Course) Hard Copy Certificate: Free (For The Title Course) Curriculum Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding --------Other Courses Are-------- Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step Course 12: IT For Recruiters CPD 130 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this IT and Analytics Training bundle including- Students Graduates Job Seekers Job Holders Requirements Our Training is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. This Training course has been designed to be fully compatible with tablets and smartphones, so you can access your course on Wi-Fi, 3G or 4G. Career path Having this Training expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Picture a world of endless possibilities; a realm where knowledge is power, and data reigns supreme. In today's digital age, the ability to decipher complex data sets and extract valuable insights has become an invaluable skill. With the Data Analysis with Data Science & Machine Learning bundle, you're one step closer to harnessing the full potential of data and unlocking a brighter future. The age of data is upon us, and the UK is no exception. As businesses and organisations increasingly rely on data to inform strategic decisions, professionals skilled in data analysis and machine learning are in high demand. By acquiring expertise in these fields, you'll not only enhance your career prospects but also contribute to the growth and success of the nation. Don't miss out on this opportunity to take your career to the next level. Enrol in the Data Analysis with Data Science & Machine Learning bundle today and unlock the power of data for your business. This Data Analysis with Data Science & Machine Learning Bundle Consists of the following Premium courses: Course 01: Introduction to Data Analysis Course 02: Data Analytics with Tableau Course 03: Complete Google Analytics Course Course 04: Python for Data Analysis Course 05: Quick Data Science Approach from Scratch Course 06: R Programming for Data Science Course 07: Learn MySQL from Scratch for Data Science and Analytics Course 08: Master JavaScript with Data Visualization Course 09: Excel Data Analysis Course 10: Statistics & Probability for Data Science & Machine Learning Course 11: Root Cause Analysis Course 12: Google Data Studio: Data Analytics Course 13: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query Course 14: Business Intelligence and Data Mining Masterclass Learning Outcomes: Develop a solid understanding of data analysis techniques. Learn how to collect, manage, and manipulate data effectively. Gain knowledge in the programming languages commonly used in data analysis, such as Python and R. Become proficient in data visualisation and presentation. Develop an understanding of machine learning algorithms and their application in data analysis. Learn how to use various data analysis software, including Tableau and Google Analytics. The Data Analysis with Data Science & Machine Learning bundle includes a variety of courses aimed at providing theoretical knowledge in the field of data analysis. The bundle covers a wide range of topics, starting with the basics of data analysis such as data cleaning, exploration, and visualisation. With courses such as Introduction to Data Analysis, Data Analytics with Tableau, Complete Google Analytics Course, Python for Data Analysis, Quick Data Science Approach from Scratch, R Programming for Data Science, Learn MySQL from Scratch for Data Science and Analytics, and Master JavaScript with Data Visualization, learners can get a comprehensive understanding of data analysis concepts. The course material is presented in a theoretical format to help learners gain a deep understanding of the topics. Learners can engage with the material at their own pace. The course content is presented in a way that is easy to understand, making it ideal for those who are new to the field of data analysis. Course Curriculum: Data Analysis with Data Science & Machine Learning Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding CPD 140 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Professionals seeking to upskill and enhance their data analysis knowledge and abilities. Individuals interested in pursuing a career in data analysis, data science or machine learning. Business owners or entrepreneurs looking to gain insights into their data and make data-driven decisions. Students or recent graduates seeking to gain valuable experience and knowledge in data analysis. Anyone looking to expand their knowledge and understanding of data analysis and its useful applications. Requirements This Data Analysis with Data Science & Machine Learning course has been designed to be fully compatible with tablets and smartphones. Career path Data Analyst: £24k-£42k per annum. Business Intelligence Analyst: £25k-£60k per annum. Data Scientist: £27k-£65k per annum. Machine Learning Engineer: £28k-£80k per annum. Business Analyst: £24k-£52k per annum. Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - £10 You will get the Hard Copy certificate for the Data Analysis with Data Science & Machine Learning course absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Overview This comprehensive course on Internet of Things will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Internet of Things comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Internet of Things. It is available to all students, of all academic backgrounds. Requirements Our Internet of Things is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 15 sections • 65 lectures • 08:53:00 total length •Module 01: Introduction: 00:02:00 •Module 02: Course Agenda: 00:03:00 •Module 01: Introduction to Internet of Things: 00:13:00 •Module 02: Choosing Cloud Services for IoT: 00:05:00 •Module 03: What is Raspberry Pi Part 1?: 00:09:00 •Module 04: What is Raspberry Pi Part 2?: 00:06:00 •Module 01: Downloading OS for Raspberry Pi Noobs-Raspbian: 00:07:00 •Module 02: Install OS using NOOBS: 00:11:00 •Module 03: Remote Control of Raspberry Pi Using VNC Viewer: 00:10:00 •Module 04: Install OS using Raspbian Image part 1: 00:06:00 •Module 05: Install OS using Raspbian Image part 2: 00:02:00 •Module 01: Getting Around Raspbian Operating System part 1: 00:10:00 •Module 02: Getting around Raspbian Operating System part 2: 00:08:00 •Module 03: Getting around Raspbian Operating System part 3: 00:06:00 •Module 04: How To Run Python program On Raspberry Pi: 00:09:00 •Module 01: Raspberry PI GPIO Concepts: 00:07:00 •Module 02: Raspberry Pi GPIO Interfacing Single LED: 00:17:00 •Module 03: Raspberry Pi GPIO Interfacing Multiple LED's: 00:09:00 •Module 04: Rapberry Pi GPIO Interfacing Buzzer: 00:03:00 •Module 01: Raspberry Pi and Transistorized Switching: 00:09:00 •Module 02: Raspberry Pi and Relay part 1: 00:13:00 •Module 03: Raspberry Pi and Relay part 2: 00:08:00 •Module 01: Accepting Digital Input on Raspberry Pi Part 1: 00:13:00 •Module 02: Accepting Digital Inputs on Raspberry Pi Part 2: 00:07:00 •Module 01: Sensor Interfacing With Raspberry Pi LDR1: 00:05:00 •Module 02: Sensor Interfacing With Raspberry Pi LDR 2: 00:10:00 •Module 03: Sensor Interfacing With Raspberry Pi LDR 3: 00:07:00 •Module 04: Sensor Interfacing with Rapberry Pi DHTT11 part 1: 00:10:00 •Module 05: Sensor Interfacing with Rapberry Pi DHTT11 part 2: 00:10:00 •Module 06: Sensor Interfacing with Raspberry pi Using SenseHAT: 00:11:00 •Module 07: Ultrasonic Sensor Interfacing with Raspberry Pi: 00:14:00 •Module 01: BMP180 with Raspberry Pi: 00:07:00 •Module 02: Enabling I2C on Raspberry Pi: 00:05:00 •Module 03: BMP180 Python Code: 00:06:00 •Module 01: Getting Started With IoT: 00:11:00 •Module 02: Getting Started with Microsoft Azure IoT Hub Part 1: 00:04:00 •Module 03: Getting Started with Microsoft Azure IoT Hub Part 2: 00:05:00 •Module 04: Getting Started with Microsoft Azure IoT Hub Part 3: 00:09:00 •Module 05: Create Device inside Azure IoT Hub: 00:06:00 •Module 06: Enable Azure Cloud Shell and enable IoT Extension: 00:08:00 •Module 07: Send Data to Azure IoT Hub Using Python Program: 00:09:00 •Module 08: Send Actual Temperature and Humidity Values to Azure IoT hub: 00:03:00 •Module 09: Storing the Data on Microsoft Azure Using Custom Gateway: 00:13:00 •Module 10: Save data to blob storage using Stream Analytics Job: 00:12:00 •Module 11: Data Visualization with Power BI Part 1: 00:07:00 •Module 12: Data Visualization with Power BI Part 2: 00:12:00 •Module 13: Creating Custom web app with azure for data visualization Part 1: 00:10:00 •Module 14: Creating Custom web app with azure for data visualization Part 2: 00:14:00 •Module 15: Creating Custom web app with azure for data visualization Part 3: 00:12:00 •Module 16: Dealing with password error while pushing your webapp to azure: 00:01:00 •Module 17: Cleaning up Azure Resources: 00:02:00 •Module 18: Remote Monitoring using Azure Logic App Part 1: 00:12:00 •Module 19: Remote Monitoring using Azure Logic App Part 2: 00:10:00 •Module 01: Introduction to Thingspeak: 00:06:00 •Module 02: Create an account and send data to Thingspeak: 00:08:00 •Module 01: Getting started with SaaS IoT Platform io.adafruit.com: 00:08:00 •Module 02: What is MQTT?: 00:10:00 •Module 03: Sending Data to Adafruit Io Using MQTT Part 1: 00:17:00 •Module 04: Sending Data to Adafruit io Using MQTT part 2: 00:14:00 •Module 05: Home automation project with adafruit IO Part 1: 00:15:00 •Module 06: Home Automation Project with Adafruit IO Part 2: 00:02:00 •Module 01: IoT Security: 00:14:00 •Module 02: Conclusion: 00:01:00 •Resources - Internet of Things: 00:00:00 •Assignment - Internet of Things: 00:00:00

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

Package Details: Number of Courses: 30 Courses Accreditation: CPD Quality Standards Free Certificates:PDF: 30Hardcopy: 30 (Delivery Charge Applicable) Courses Access: Lifetime Instalment Payment Available Basic Programming (C, Java, Python, SQL) Course 01: Diploma in Computer Programming Course 02: C# Programming - Beginner to Advanced Course 03: Master JavaScript with Data Visualization Course 04: SQL Programming Masterclass Course 05: Python Programming for Everybody Course 06: Javascript Programming for Beginners Course 07: jQuery: JavaScript and AJAX Coding Bible Structural Programming Course 01: Kotlin Programming : Android Coding Course 02: Sensors Course 03: Remote Sensing in ArcGIS Course 04: Linux Shell Scripting Course 05: Bash Scripting, Linux and Shell Programming Course 06: Coding with Scratch Course 07: CSS Coding Data Driven Programming Course 01: Quick Data Science Approach from Scratch Course 02: Secure Programming of Web Applications Course 03: A-Frame Web VR Programming Course 04: Complete AutoLISP Programming AutoCAD Programming Course 01: AutoCAD Programming using C# with Windows Forms Course 02: AutoCAD Programming using VB.NET with Windows Forms Programming for Computer Building Course 01: Building Your Own Computer Course 02: Neuro-linguistic Programming (NLP) Diploma Programming for Security Course 01: Cyber Security Incident Handling and Incident Response Course 02: Computer Networks Security Course 03: IT Asset Department IT Soft Skills Course 01: Functional Skills IT Course 02: CompTIA Healthcare IT Technician Course 03: Basic Google Data Studio Course 04: Data Analytics with Tableau Course 05: Recovering from a Job Loss in Technology Computer Programming Fundamental Course This Computer Programming Fundamental bundles' curriculum has been designed by Computer Programming Fundamental experts with years of Computer Programming Fundamental experience behind them. The Computer Programming Fundamental course is extremely dynamic and well-paced to help you understand Computer Programming Fundamental with ease. You'll discover how to master Computer Programming Fundamental skills while exploring relevant and essential topics. CPD 310 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Computer Programming Fundamental Course Requirements Computer Programming Fundamental Course Career path Computer Programming Fundamental Course

Give a compliment to your career and take it to the next level. This SQL (Structured Query Language) will provide you with the essential knowledge and skills required to shine in your professional career. Whether you want to develop skills for your next job or want to elevate skills for your next promotion, this coursewill help you keep ahead of the pack. The course incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can reinforce your professional skills and essential knowledge, reaching out to the level of expertise required for your position. Further, this SQL (Structured Query Language) will add extra value to your resume to stand out to potential employers. Throughout the programme, it stresses how to improve your competency as a person in your profession while at the same time it outlines essential career insights in this job sector. Consequently, you'll strengthen your knowledge and skills; on the other hand, see a clearer picture of your career growth in future. By the end of the SQL (Structured Query Language), you can equip yourself with the essentials to keep you afloat into the competition. Along with this SQL (Structured Query Language) course, you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free. This Bundle Consists of the following Premium courses: Course 01: SQL Server for Beginners Course 02: Microsoft SQL Server Development for Everyone Course 03: Python for Data Analysis Course 04: Coding with HTML, CSS, & JavaScript Course 05: Modern Web Designing - Level 2 Course 06: Diploma in PHP Web Development Course 07: Front End Web Development Diploma Course 08: Secure Programming of Web Applications Course 09: Linux for Absolute Beginners! Course 10: Ethical Hacking Course 11: Creativity and Problem Solving Skills So, enrol now to advance your career! Benefits you'll get choosing Apex Learning for this SQL (Structured Query Language): One payment, but lifetime access to 11 CPD courses Certificate, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £6*11 = £66) Hard Copy Certificate: Free (For The Title Course: Previously it was £10) Curriculum of the Bundle Course 01: SQL Server for Beginners Introduction Setup Basic queries Security MSSQL and different drivers Files General Course 02: Microsoft SQL Server Development for Everyone Introduction Manipulating Tables and Data Relationships Foreign Keys Group By and Aggregate Functions Advanced Server Objects and Concepts Course 03: Python for Data Analysis Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 04: Coding with HTML, CSS, & Javascript Welcome HTML 5 CSS 3 Bootstrap Project 1 - Design a Landing Page Project 2 - Business Website SProject 3 - Portfolio Course 05: Modern Web Designing - Level 2 Module: 01 1.1 Intro 1.2 Install the Tools and Get Started Module: 02 2.1 Intro to HTML 2.2 What is HTML 2.3 Start a New HTML File & Use Tags 2.4 Header Tags 2.5 Edit Text 2.6 Links 2.7 Images 2.8 Lists 2.9 Challenge 2.10 HTML Outro Module: 03 3.1 CSS Intro 3.2 Add CSS Styles 3.3 Classes and IDs 3.4 Borders 3.5 Sizing 3.6 Padding and Margin 3.7 Text Styles 3.8 DIVs 3.9 Postioning 3.10 Hover 3.11 Easily Center Elements 3.12 Fonts 3.13 Challenge 3.14 CSS Outro Module: 04 4.1 Intro to Bootstrap 4.2 Install Bootstrap 4.3 Indenting and Containers 4.4 The Grid System 4.5 Images 4.6 Buttons 4.7 Challenge 4.8 Bootstrap Outro Module: 05 5.1 Landing Page Intro 5.2 Sketch Your Landing Page 5.3 The Top Section 5.4 Polish the Top Section 5.5 Adding Images 5.6 The Main Points Section 5.7 Collecting Emails With an Opt-In Form 5.8 Challenge 5.9 Landing Page Outro Module: 06 6.1 Business Site Intro 6.2 Sketch Up 6.3 Using Fancy Font Logo 6.4 Carousel Basics 6.5 Carousel Extras 6.6 Text on Images 6.7 Phone Number Icon 6.8 Google Maps 6.9 Font Awesome 6.10 Challenge 6.11 Business Site Outro Module: 07 7.1 Intro 7.2 Portfolio Sketch 7.3 Jumbotron 7.4 Nav Bar 7.5 Panels 7.6 Challenge 7.7 Portfolio Outre Module: 08 8.1 Hosting 8.2 Bluehost 8.3 Uploading 8.4 Tips 8.5 Hosting Outro Course 06: Diploma in PHP Web Development Unit 01: Introduction Unit 02: Environment Configuration Unit 03: PHP Basics and Syntax Unit 04: PHP Forms and MySQL and User Authentication Course 07: Front End Web Development Diploma Welcome to the course! Web Development Basics - HTML Advanced HTML Concepts Introduction to Cascading Style Sheets (CSS) Advanced CSS JavaScript for Begeinners More JavaScript Concepts Getting Started with jQuery More jQuery Bootstrap Basics Project #2 - Pipboy from Fallout 4 Project #3 - Google Chrome Extension BONUS - Coding Another Google Chrome Extension Course 08: Secure Programming of Web Applications Section 01: Introduction Section 02: Well-known Vulnerabilities and Secure Programming Section 03: Conclusion and Summary Course 09: Linux for Absolute Beginners! Introduction to Linux Linux Installation Linux Command Line Interface (CLI) Essentials Advanced CLI Usage Linux Development Tools Web Development Project Web Server Setup, Host Cofiguration and App Deployment Linux User Management Linux Network Administration Course 10: Ethical Hacking Introduction to Ethical Hacking Reconnaissance - Surveying the Attack Surface Scanning and Enumeration - Getting Down to Business Network Presence Attacking Web Hacking Social Engineering - Hacking Humans Course 11: Creativity and Problem Solving Skills Getting Started The Problem Solving Method Information Gathering Problem Definition Preparing for Brainstorming Generating Solutions (I) Generating Solutions (II) Analyzing Solutions Selecting a Solution Planning Your Next Steps Recording Lessons Learned CPD 135 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this SQL (Structured Query Language) bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future. Requirements Our SQL (Structured Query Language) is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various expertise will increase the value in your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (SQL Server for Beginners) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

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

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure. In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage. Prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. AZ-900T00 Microsoft Azure Fundamentals DP-900T00 Microsoft Azure Data Fundamentals 1 - Introduction to data engineering on Azure What is data engineering Important data engineering concepts Data engineering in Microsoft Azure 2 - Introduction to Azure Data Lake Storage Gen2 Understand Azure Data Lake Storage Gen2 Enable Azure Data Lake Storage Gen2 in Azure Storage Compare Azure Data Lake Store to Azure Blob storage Understand the stages for processing big data Use Azure Data Lake Storage Gen2 in data analytics workloads 3 - Introduction to Azure Synapse Analytics What is Azure Synapse Analytics How Azure Synapse Analytics works When to use Azure Synapse Analytics 4 - Use Azure Synapse serverless SQL pool to query files in a data lake Understand Azure Synapse serverless SQL pool capabilities and use cases Query files using a serverless SQL pool Create external database objects 5 - Use Azure Synapse serverless SQL pools to transform data in a data lake Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement Encapsulate data transformations in a stored procedure Include a data transformation stored procedure in a pipeline 6 - Create a lake database in Azure Synapse Analytics Understand lake database concepts Explore database templates Create a lake database Use a lake database 7 - Analyze data with Apache Spark in Azure Synapse Analytics Get to know Apache Spark Use Spark in Azure Synapse Analytics Analyze data with Spark Visualize data with Spark 8 - Transform data with Spark in Azure Synapse Analytics Modify and save dataframes Partition data files Transform data with SQL 9 - Use Delta Lake in Azure Synapse Analytics Understand Delta Lake Create Delta Lake tables Create catalog tables Use Delta Lake with streaming data Use Delta Lake in a SQL pool 10 - Analyze data in a relational data warehouse Design a data warehouse schema Create data warehouse tables Load data warehouse tables Query a data warehouse 11 - Load data into a relational data warehouse Load staging tables Load dimension tables Load time dimension tables Load slowly changing dimensions Load fact tables Perform post load optimization 12 - Build a data pipeline in Azure Synapse Analytics Understand pipelines in Azure Synapse Analytics Create a pipeline in Azure Synapse Studio Define data flows Run a pipeline 13 - Use Spark Notebooks in an Azure Synapse Pipeline Understand Synapse Notebooks and Pipelines Use a Synapse notebook activity in a pipeline Use parameters in a notebook 14 - Plan hybrid transactional and analytical processing using Azure Synapse Analytics Understand hybrid transactional and analytical processing patterns Describe Azure Synapse Link 15 - Implement Azure Synapse Link with Azure Cosmos DB Enable Cosmos DB account to use Azure Synapse Link Create an analytical store enabled container Create a linked service for Cosmos DB Query Cosmos DB data with Spark Query Cosmos DB with Synapse SQL 16 - Implement Azure Synapse Link for SQL What is Azure Synapse Link for SQL? Configure Azure Synapse Link for Azure SQL Database Configure Azure Synapse Link for SQL Server 2022 17 - Get started with Azure Stream Analytics Understand data streams Understand event processing Understand window functions 18 - Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics Stream ingestion scenarios Configure inputs and outputs Define a query to select, filter, and aggregate data Run a job to ingest data 19 - Visualize real-time data with Azure Stream Analytics and Power BI Use a Power BI output in Azure Stream Analytics Create a query for real-time visualization Create real-time data visualizations in Power BI 20 - Introduction to Microsoft Purview What is Microsoft Purview? How Microsoft Purview works When to use Microsoft Purview 21 - Integrate Microsoft Purview and Azure Synapse Analytics Catalog Azure Synapse Analytics data assets in Microsoft Purview Connect Microsoft Purview to an Azure Synapse Analytics workspace Search a Purview catalog in Synapse Studio Track data lineage in pipelines 22 - Explore Azure Databricks Get started with Azure Databricks Identify Azure Databricks workloads Understand key concepts 23 - Use Apache Spark in Azure Databricks Get to know Spark Create a Spark cluster Use Spark in notebooks Use Spark to work with data files Visualize data 24 - Run Azure Databricks Notebooks with Azure Data Factory Understand Azure Databricks notebooks and pipelines Create a linked service for Azure Databricks Use a Notebook activity in a pipeline Use parameters in a notebook Additional course details: Nexus Humans DP-203T00 Data Engineering on Microsoft Azure 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 DP-203T00 Data Engineering on Microsoft Azure 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.