51 Data Visualization courses delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

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

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

Duration 3 Days 18 CPD hours This course is intended for This class is designed for individuals who are (or will soon be) supporting a Salesforce implementation in a decision-making capacity. This includes, but is not limited to, business analysts, IT managers, project managers, executive leaders, and executive sponsors. This class is not recommended for individuals tasked with solution-building. Overview When you complete this course, you will be able to: Identify key stakeholders needed for a successful Salesforce implementation. Describe the Salesforce data model as it relates to Customer 360, Salesforce Clouds, and the Salesforce Platform. Communicate the appropriate security measures needed to control org and data access. Discuss which standard or custom objects and applications should be implemented based on specific requirements and use cases. Effectively strategize how to migrate data into your Salesforce org while maintaining high data quality. Understand Salesforce automation tools and how they solve for various business challenges. Analyze Salesforce data with Reports and Dashboards. Navigate the key phases and milestones of a Salesforce implementation. Explore Salesforce features and functionality and gain the knowledge to make Salesforce implementation decisions with confidence. In this 3-day, heavily discussion-based class, learn about standard and custom objects and applications, data management, data visualization, flow automation tools, security mechanisms, and more. Successfully navigate the key phases and milestones of a Salesforce implementation, effectively communicate business needs, and provide directives to team members tasked with solution-building to deliver a robust Salesforce solution that achieves business goals. Salesforce Data Model Discover the Customer 360 Platform Examine Salesforce Clouds Navigate the Salesforce Platform Review the Salesforce Platform Data Model Understand Data Visualization Security & Access Create Users Access the Org Control Data Objects & Applications Review Standard Objects Understand Custom Objects Explore Standard Applications Discover Custom Applications Salesforce Customizations Work with Fields Design Page Layouts Understand Record Types Review Dynamic Capabilities Successful Data Management Determine Data Strategy Create Data Ensure Data Quality Process Automation Streamline Business Processes Using Automation Tools Learn Purpose-Driven Automation Automate With Flow Data Analysis Using Reports & Dashboards Organize Reports and Dashboards Build Reports Create Dashboards Create an Analytics Strategy Adoption & Continued Improvement Adopt Your Implementation Evaluate Continued Improvements Additional course details: Nexus Humans Understand and Drive Your Salesforce Implementation ( BSX101 ) 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 Understand and Drive Your Salesforce Implementation ( BSX101 ) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for This training is ideally suited for data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities. It will also benefit system administrators and data engineers who wish to harness Elastic Stack's functionalities for efficient system logging, monitoring, and robust data visualization. With a focus on practical application, this course is perfect for those aspiring to solve complex data challenges in real-time environments across diverse industry verticals. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll explore: New features and updates introduced in Elastic Stack 7.0 Fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana Useful tips for using Elastic Cloud and deploying Elastic Stack in production environments How to install and configure an Elasticsearch architecture How to solve the full-text search problem with Elasticsearch Powerful analytics capabilities through aggregations using Elasticsearch How to build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis How to create interactive dashboards for effective storytelling with your data using Kibana How to secure, monitor and use Elastic Stack's alerting and reporting capabilities The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. Geared for experienced data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities, Working with Elasticsearch will explore how to use Elastic Stack and Elasticsearch efficiently to build powerful real-time data processing applications. Throughout the two-day hands-on course, you?ll explore the power of this robust toolset that enables advanced distributed search, analytics, logging, and visualization of data, enabled by new features in Elastic Stack 7.0. You?ll delve into the core functionalities of Elastic Stack, understanding the role of each component in constructing potent real-time data processing applications. You?ll gain proficiency in Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for compelling data visualization. You?ll also explore the art of crafting custom plugins using Kibana and Beats, and familiarize yourself with Elastic X-Pack, a vital extension for effective security and monitoring. The course also covers essentials like Elasticsearch architecture, solving full-text search problems, data pipeline building, and creating interactive Kibana dashboards. Learn how to deploy Elastic Stack in production environments and explore the powerful analytics capabilities offered through Elasticsearch aggregations. The course will also touch upon securing, monitoring, and utilizing Elastic Stack's alerting and reporting capabilities. Hands-on labs, captivating demonstrations, and interactive group activities enrich your learning journey throughout the course. Introducing Elastic Stack What is Elasticsearch, and why use it? Exploring the components of the Elastic Stack Use cases of Elastic Stack Downloading and installing Getting Started with Elasticsearch Using the Kibana Console UI Core concepts of Elasticsearch CRUD operations Creating indexes and taking control of mapping REST API overview Searching - What is Relevant The basics of text analysis Searching from structured data Searching from the full text Writing compound queries Modeling relationships Analytics with Elasticsearch The basics of aggregations Preparing data for analysis Metric aggregations Bucket aggregations Pipeline aggregations Substantial Lab and Case Study Analyzing Log Data Log analysis challenges Using Logstash The Logstash architecture Overview of Logstash plugins Ingest node Visualizing Data with Kibana Downloading and installing Kibana Preparing data Kibana UI Timelion Using plugins

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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.

OVERVIEW DIAD is a one-day, hands-on workshop for business analysts, covering the breadth of Power BI capabilities. The course focuses on five practical Labs and at the end of the day, attendees will better understand how to: Connect and transform data from a variety of data sources. Define business rules and KPIs. Explore data with powerful interactive visuals. Build stunning reports. Share their dashboards with their team business partners and publish them to the web. The course content is managed by the Power BI engineering team at Microsoft. There is no exam associated with the course. COURSE BENEFITS: Learn how to clean, transform, and load data from various sources Create and manage a data model in Power BI consisting of multiple tables connected with relationships Build Measures and other calculations in the DAX language to plot in reports Manage and share report assets to the Power BI Service WHO IS THE COURSE FOR? Data Analysts and Management Consultants with little or no experience of Power BI who wish to upgrade their knowledge to include Business Intelligence Analysts looking for a quick introduction to Power BI who don’t have the time for the full three day PL-300 course Marketers in data-intensive organisations who need new tools to build visually appealing, dynamic charts for their stakeholders to use LAB OUTLINE Lab 1 Accessing & Preparing The Data Load data from Excel and CSV sources Manipulate the data to prepare it for reporting Prepare tables in Power Query and load them into the data model Lab 2 Data Modelling And Exploration Create a range of different charts Highlight and cross-filter Create new groups and hierarchies Add new measures to the model Lab 3 Data Visualization Add conditional formatting to a report Add logos to a filter Import a custom visual Apply a custom theme Add bookmarks to the report to tell a story Lab 4 Publishing A Report And Creating A Dashboard Create a Workspace in the Power BI Service Publish a report to the Service Create a Dashboard and pin visuals to it Generate and view insights Lab 5 Collaboration Share a Dashboard Access a Dashboard on a Mobile Device

Data has become the most important resource for every organisation – but the insights gained from data analysis will only ever be truly valuable if they can be clearly expressed to other people.  This course is for anybody who works with data, and needs to communicate the meaning that's in the numbers to colleagues, customers, bosses or external stakeholders.  It will give you or your team the confidence and skills to translate raw data into compelling visual stories for your key audiences. The principles and skills covered apply to the simplest PowerPoint chart, to more complex interactive visualisations.  We’ll work with you before the course to ensure that we understand your organisation and what you’re hoping to achieve.  Sample learning content Session 1: What makes a great data-driven story The key elements of a successful infographic or presentation. Industry best practice, and discussion of good (and bad) examples. A simple framework for identifying the Audience, Story and Action. Session 2: Data in context How to balance function and aesthetic appeal. Identifying the right graph, chart, infographic or other visual. Framing the data and providing contextual information. Session 3: Designing for the human brain Using colours to add emphasis and meaning. Design and layout principles, and creating hierarchies of information. The principle of ‘self-sufficiency’, and removing clutter. Session 4: Navigation and narrative Tailoring visualisations for different types of communications. Structuring presentations and longer reports. Thinking in layers to create interactive dashboards. Delivery We deliver our courses over Zoom, to maximise flexibility. The training can be delivered in a single day, or across multiple sessions. All of our courses are live and interactive – every session includes a mix of formal tuition and hands-on exercises. To ensure this is possible, the number of attendees is capped at 16 people.  Tutor Alan Rutter is the founder of Fire Plus Algebra. He is a specialist in communicating complex subjects through data visualisation, writing and design. He teaches for General Assembly and runs in-house training for public sector clients including the Home Office, the Department of Transport, the Biotechnology and Biological Sciences Research Council, the Health Foundation, and numerous local government and emergency services teams. He previously worked with Guardian Masterclasses on curating and delivering new course strands, including developing and teaching their B2B data visualisation courses. He oversaw the iPad edition launches of Wired, GQ, Vanity Fair and Vogue in the UK, and has worked with Condé Nast International as product owner on a bespoke digital asset management system for their 11 global markets.
 Testimonial “I was familiar with Alan’s work as a Guardian Masterclass instructor on data visualisation and digital journalism, which made it easy for me to recommend him for onsite training at the Liverpool School of Tropical Medicine. We had a large group of people interested in honing their abilities to depict their research and stories in engaging ways. Alan’s course provided great insight about common communication pitfalls and how to avoid them, how to become better communicators by understanding the audience diversity, and it showcased some great online tools for creating infographics. This should be mandatory training for all students, academics, report writers and those involved with conveying research to the media as it will help increase the clarity and accessibility of our own research stories.”
 Dr Lee Haines | Liverpool School of Tropical Medicine

Duration 3 Days 18 CPD hours This course is intended for The course will likely be attended by SQL Server report creators who are interested in alternative methods of presenting data. Overview After completing this course, students will be able to: ? Perform Power BI desktop data transformation. ? Describe Power BI desktop modelling. ? Create a Power BI desktop visualization. ? Implement the Power BI service. ? Describe how to connect to Excel data. ? Describe how to collaborate with Power BI data. ? Connect directly to data stores. ? Describe the Power BI developer API. ? Describe the Power BI mobile app. The main purpose of the course is to give students a good understanding of data analysis with Power BI. The course includes creating visualizations, the Power BI Service, and the Power BI Mobile App. Introduction to Self-Service BI Solutions Introduction to business intelligence Introduction to data analysis Introduction to data visualization Overview of self-service BI Considerations for self-service BI Microsoft tools for self-service BI Lab : Exploring an Enterprise BI solution Introducing Power BI Power BI The Power BI service Lab : Creating a Power BI dashboard Power BI Using Excel as a data source for Power BI The Power BI data model Using databases as a data source for Power BI The Power BI service Lab : Importing data into Power BI Shaping and Combining Data Power BI desktop queries Shaping data Combining data Lab : Shaping and combining data Modelling data Relationships DAX queries Calculations and measures Lab : Modelling Data Interactive Data Visualizations Creating Power BI reports Managing a Power BI solution Lab : Creating a Power BI report Direct Connectivity Cloud data Connecting to analysis services Lab : Direct Connectivity Developer API The developer API Custom visuals Lab : Using the developer API Power BI mobile app The Power BI mobile app Using the Power BI mobile app Power BI embedded

The Real Estate Analyst course has been taught non-stop to global real estate firms over the last 25 years, and is without doubt the core financial modelling training in your career portfolio. Whether you have an upcoming financial modelling test for a new job or an APC exam, the Real Estate Analyst course is the choice for you.