9 Data Mining courses delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. GETTING STARTED WITH RECOMMENDER SYSTEMS * Technical requirements * What is a recommender system? * Types of recommender systems MANIPULATING DATA WITH THE PANDAS LIBRARY * Technical requirements * Setting up the environment * The Pandas library * The Pandas DataFrame * The Pandas Series BUILDING AN IMDB TOP 250 CLONE WITH PANDAS * Technical requirements * The simple recommender * The knowledge-based recommender BUILDING CONTENT-BASED RECOMMENDERS * Technical requirements * Exporting the clean DataFrame * Document vectors * The cosine similarity score * Plot description-based recommender * Metadata-based recommender * Suggestions for improvements GETTING STARTED WITH DATA MINING TECHNIQUES * Problem statement * Similarity measures * Clustering * Dimensionality reduction * Supervised learning * Evaluation metrics BUILDING COLLABORATIVE FILTERS * Technical requirements * The framework * User-based collaborative filtering * Item-based collaborative filtering * Model-based approaches HYBRID RECOMMENDERS * Technical requirements * Introduction * Case study and final project ? Building a hybrid model ADDITIONAL COURSE DETAILS: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. GETTING STARTED WITH RECOMMENDER SYSTEMS * Technical requirements * What is a recommender system? * Types of recommender systems MANIPULATING DATA WITH THE PANDAS LIBRARY * Technical requirements * Setting up the environment * The Pandas library * The Pandas DataFrame * The Pandas Series BUILDING AN IMDB TOP 250 CLONE WITH PANDAS * Technical requirements * The simple recommender * The knowledge-based recommender BUILDING CONTENT-BASED RECOMMENDERS * Technical requirements * Exporting the clean DataFrame * Document vectors * The cosine similarity score * Plot description-based recommender * Metadata-based recommender * Suggestions for improvements GETTING STARTED WITH DATA MINING TECHNIQUES * Problem statement * Similarity measures * Clustering * Dimensionality reduction * Supervised learning * Evaluation metrics BUILDING COLLABORATIVE FILTERS * Technical requirements * The framework * User-based collaborative filtering * Item-based collaborative filtering * Model-based approaches HYBRID RECOMMENDERS * Technical requirements * Introduction * Case study and final project ? Building a hybrid model ADDITIONAL COURSE DETAILS: Nexus Humans Building Recommendation Systems with Python (TTAI2360) 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 Building Recommendation Systems with Python (TTAI2360) 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 is an Intermediate PowerBI course geared for experienced users who wish to leverage the tool's more advanced capabilities Overview This course is about 50% hands-on lab and 50% lecture, designed to train attendees in essential PowerBI data handling functions and reporting skills, coupling the most current, effective techniques with the soundest practices. Attendees of this course will gain practical examples from the experienced instructor who has deployed and configured Power BI reporting in a wide variety of businesses. Working in a hands-on learning environment led by our expert facilitator, students will learn how to: Create Advanced Power BI Reports Advanced understanding of the data schemas and extracting data Perform advanced transformations of data or any data schema Utilize time-phased data in the creation of complex analyses Create new measures using DAX Filter data using row-level security Create and deploy content packs Use Power BI to integrate with line-of-business applications Next Level Power BI for Experienced Users is a two day, course that provides attendees already experienced with Microsoft Power BI basics with a hands-on exploration of intermediate and beyond level features. This course is geared for attendees ready to learn the advanced techniques that you, your business analysts, and your stakeholders need to create complex information from projects, program, and portfolio reporting to utilizing time-phased data and, potentially, data from your enterprise?s other line-of-business tools. GET PROJECT ONLINE DATA * Select and mine relevant tables with ODATA * Advanced ODATA data mining * Importing other data formats * Advanced Editing of data queries ADVANCED DATA TRANSFORMATIONS * Managing table relationships * Creating & using data hierarchies * Creating custom columns and measures and metrics for filtering and reporting CREATING POWER BI REPORTS * Using advanced visualizations * Configuring drill-down * Modifying visual interactions * Importing and creating custom visuals CONFIGURE POWER BI SECURITY * Creating Dashboard and row-level security * Utilizing Filtering using row-level security PUBLISHING REPORTS AND DASHBOARDS * Building Mobile Reporting * Creating and deploying content packs * Configuring natural language query

Duration 3 Days 18 CPD hours Overview Our engaging instructors and mentors are highly-experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working within in a hands-on learning environment, guided by our expert team, attendees will explore: New Features Overview Multitenant New Features Security Features Cloud Services Networking Globalization Big Data Support Database Installation and Configuration Database Tuning Backup and Recovery Oracle 19c New Features is a hands-on course that explores the newest features such as Big Data Enhancements, Security, Multitenant features, Oracle Cloud Services, Networking, and much more. Oracle is one of the leading databases in industry today. Learn what their latest flagship product has to offer from industry experts. ORACLE 19C NEW FEATURES OVERVIEW * Introduction to Oracle 19c New Features ORACLE 19C MULTITENANT NEW FEATURES * Refreshable PDB Switchover * PDB Integration with Data Guard * PDB Snapshot Carousel * CDB Fleet Management ORACLE 19C SECURITY FEATURES * Profile Lockdown * Create a User Defined Master Encryption Key * Encrypted Passwords in Database Links and Data Pump * Create Keystores for Pluggable Databases * Datapump and Unified Auditing * Schema Only Accounts ORACLE 19C CLOUD SERVICES * Oracle IaaS * Oracle Saas * Oracle PaaS ORACLE 19C NETWORKING * Database Connection Manager * Database Proxy Support * Tenant Isolation ORACLE 19C GLOBALIZATION * New globalization for Bind Variables * New Database Local Support * Additional Unicode Support BIG DATA SUPPORT * New Analytic Support * Data Mining Data Warehouse * Additional Parallel Processing Support * Inline External Tables DATABASE INSTALLATION AND CONFIGURATION * Zero Downtime Upgrades * Dry Run Command implementation * New location for Password File * Improved Bulk Operations DATABASE TUNING * SQL Tuning Advisor and Exadata * New SQL Tuning Set API * Concurrent SQL and Sql Performance Analyzer * Database In Memory Features * In Memory Support for External Tables * In Memory Features for Analytics ORACLE 19C BACKUP AND RECOVERY * Active Pluggable Cloning * Pluggable and non Pluggable Database Migration ADDITIONAL COURSE DETAILS: Nexus Humans Oracle 19c New Features (TTOR20019) 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 Oracle 19c New Features (TTOR20019) 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 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 3 Days 18 CPD hours This course is intended for Blockchain Architects Blockchain DevelopersApplication Developers Blockchain System AdministratorsNetwork Security Architects Cyber Security ExpertsIT Professionals w/cyber security experience Overview Those who attend the Security for Blockchain Professionals course and pass the exam certification will have a demonstrated knowledge of:Identifying and differentiating between security threats and attacks on a Blockchain network.Blockchain security methods, best practices, risk mitigation, and more.All known (to date) cyber-attack vectors on the Blockchain.Performing Blockchain network security risk analysis.A complete understanding of Blockchain?s inherent security features and risks.An excellent knowledge of best security practices for Blockchain System/Network Administrators.Demonstrating appropriate Blockchain data safeguarding techniques. This course covers all known aspects of Blockchain security that exist in the Blockchain environment today and provides a detailed overview of all Blockchain security issues, including threats, risk mitigation, node security integrity, confidentiality, best security practices, advanced Blockchain security and more. FUNDAMENTAL BLOCKCHAIN SECURITY CRYPTOGRAPHY FOR THE BLOCKCHAIN HASH FUNCTIONS * Public Key Cryptography * Elliptic Curve Cryptography * A Brief Introduction to Blockchain The Blocks * The Chains * The Network * Promises of the Blockchain * Blockchain Security Assumptions Digital Signature Security * Hash Function Security * Limitations of Basic Blockchain Security Public Key Cryptography Review * Real-Life Public Key Protection * Cryptography and Quantum Computers * Lab 1 (Tentative) Finding Hash Function Collisions Reversible hash function * Hash function with poor non-locality * Hash function with small search space * Breaking Public Key Cryptography Brute Forcing a Short Private Key * Brute Forcing a Poorly-Chosen Private Key CONSENSUS IN THE BLOCKCHAIN * Blockchain Consensus and Byzantine Generals Blockchain Networking Review * Byzantine Generals Problem Relation to Blockchain * Byzantine Fault Tolerance * Introduction to Blockchain Consensus Security Blockchain Consensus Breakthrough * Proof of Work What is Proof of Work? * How does Proof of Work Solve BGP? * Proof of Work Security Assumptions * Attacking Proof of Work * Proof of Stake What is Proof of Stake? * How does Proof of Stake Solve BGP? * Proof of Stake Security Assumptions * Attacking Proof of Stake * General Attacks on Blockchain Consensus * Other Blockchain Consensus Algorithms * Lab 2 (Tentative) Attacking Proof of Work Performing a 51% Attack * Performing a Selfish Mining Attack * Attacking Proof of Stake Performing a XX% Attack * Performing a Long-Range Attack * Malleable Transaction Attacks ADVANCED BLOCKCHAIN SECURITY MECHANISMS ARCHITECTURAL SECURITY MEASURES PERMISSIONED BLOCKCHAINS * Checkpointing * Advanced Cryptographic Solutions Multiparty Signatures * Zero-Knowledge Proofs * Stealth Addresses * Ring Signatures * Confidential Transactions * Lab 3 (Tentative) Permissioned Blockchains * 51% on a Checkpointed Blockchain * Data mining on a blockchain with/without stealth addresses * Zero-Knowledge Proof Simulation * Trying to fake knowledge of a ZKP * Module 4: Blockchain for Business Introduction to Ethereum Security What is Ethereum * Consensus in Ethereum * Smart Contracts in Ethereum * Ethereum Security * Pros and Cons of Ethereum Blockchains * Introduction to Hyperledger Security What is Hyperledger * Consensus in Hyperledger * Smart Contracts in Hyperledger * Hyperledger Security * Pros and Cons of Hyperledger Blockchains * Introduction to Corda Security What is Corda * Consensus in Corda * Smart Contracts in Corda * Corda Security * Pros and Cons of Corda Blockchains * Lab 4 BLOCKCHAIN RISK ASSESSMENT WHAT ARE THE RISKS OF THE BLOCKCHAIN? * Information Security * Information Sensitivity Data being placed on blockchain * Risks of disclosure * Regulatory Requirements Data encryption * Data control * PII protection * Blockchain Architectural Design Public and Private Blockchains * Open and Permissioned Blockchains * Choosing a Blockchain Architecture * Lab 5 * Exploring public/private open/permissioned blockchains? BASIC BLOCKCHAIN SECURITY BLOCKCHAIN ARCHITECTURE * User Security Protecting Private Keys * Malware * Update * Node Security * Configuring MSPs * Network Security * Lab 6 (TBD) SMART CONTRACT SECURITY INTRODUCTION TO SMART CONTRACTS * Smart Contract Security Considerations Turing-Complete * Lifetime * External Software * Smart Contract Code Auditing Difficulties * Techniques * Tools * Lab 7 (Tentative) * Try a couple of smart contract code auditing tool against different contracts with built-in vulnerabilities * Module 8: Security Implementing Business Blockchains Ethereum Best Practices * Hyperledger Best Practices * Corda Best Practices * Lab 8 NETWORK-LEVEL VULNERABILITIES AND ATTACKS INTRODUCTION TO BLOCKCHAIN NETWORK ATTACKS * 51% Attacks * Denial of Service Attacks * Eclipse Attacks * Routing Attacks * Sybil Attacks * Lab 9 * Perform different network-level attacks SYSTEM-LEVEL VULNERABILITIES AND ATTACKS INTRODUCTION TO BLOCKCHAIN SYSTEM VULNERABILITIES * The Bitcoin Hack * The Verge Hack * The EOS Vulnerability * Lab 10 SMART CONTRACT VULNERABILITIES AND ATTACKS INTRODUCTION TO COMMON SMART CONTRACT VULNERABILITIES * Reentrancy * Access Control * Arithmetic * Unchecked Return Values * Denial of Service * Bad Randomness * Race Conditions * Timestamp Dependence * Short Addresses * Lab 11 * Exploiting vulnerable smart contracts SECURITY OF ALTERNATIVE DLT ARCHITECTURES WHAT ARE ALTERNATIVE DLT ARCHITECTURES? * Introduction to Directed Acyclic Graphs (DAGs) * DAGs vs. Blockchains * Advantages of DAGs * DAG Vulnerabilities and Security * Lab 12 * Exploring a DAG network

Duration 2.5 Days 15 CPD hours This course is intended for This course is intended for those with a basic understanding of Tableau who want to pursue mastery of the advanced features.   Overview The goal of this course is to present essential Tableau concepts and its advanced functionalities to help better prepare and analyze data.   This course will use Tableau Hyper, Tableau Prep and more. GETTING UP TO SPEED ? A REVIEW OF THE BASICS * Connecting Tableau to your data * Connecting to Tableau Server * Connecting to saved data sources * Measure Names and Measure Values * Three essential Tableau concepts * Exporting data to other devices * Summary * ALL ABOUT DATA ? GETTING YOUR DATA READY * Data mining and knowledge discovery process models CRISP?DM * ALL ABOUT DATA ? JOINS, BLENDS, AND DATA STRUCTURES * All About Data - Joins, Blends, and Data Structures * Introduction to joins * Introduction to complex joins * Exercise: observing join culling * Introduction to join calculations * Introduction to spatial joins * Introduction to unions * Understanding data blending * Order of operations * No dimensions from a secondary source * Introduction to scaffolding * Introduction to data structures * Exercise: adjusting the data structure for different questions * Summary * TABLE CALCULATIONS * Table Calculations * A definition and two questions * Introduction to functions * Directional and non-directional table calculations * Application of functions * Summary * LEVEL OF DETAIL CALCULATIONS * Level of Detail Calculations * Building playgrounds * Playground I: FIXED and EXCLUDE * Playground II: INCLUDE * Practical application * Exercise: practical FIXED * Exercise: practical INCLUDE * Exercise: practical EXCLUDE * Summary * BEYOND THE BASIC CHART TYPES * Beyond the Basic Chart Types * Improving popular visualizations * Custom background images * Tableau extensions * Summary * MAPPING * Mapping * Extending Tableau's mapping capabilities without leaving Tableau * Extending Tableau mapping with other technology * Exercise: connecting to a WMS server * Exploring the TMS file * Exploring Mapbox * Accessing different maps with a dashboard * Creating custom polygons * Converting shape files for Tableau * Exercise: polygons for Texas * Heatmaps * Summary * TABLEAU FOR PRESENTATIONS * Tableau for Presentations * Getting the best images out of Tableau * From Tableau to PowerPoint * Embedding Tableau in PowerPoint * Animating Tableau * Story points and dashboards for Presentations * Summary * VISUALIZATION BEST PRACTICES AND DASHBOARD DESIGN * Visualization Best Practices and Dashboard Design * Visualization design theory * Formatting rules * Color rules * Visualization type rules * Compromises * Keeping visualizations simple * Dashboard design * Dashboard layout * Sheet selection * Summary * ADVANCED ANALYTICS * Advanced Analytics * Self-service Analytics * Use case ? Self-service Analytics * Use case ? Geo-spatial Analytics * Summary * IMPROVING PERFORMANCE * Improving Performance * Understanding the performance-recording dashboard * Exercise: exploring performance recording in Tableau desktop * Performance-recording dashboard events * Behind the scenes of the performance- recording dashboard * Hardware and on-the-fly techniques * Hardware considerations * On-the-fly-techniques * Single Data Source > Joining > Blending * Three ways Tableau connects to data * Using referential integrity when joining * Advantages of blending * Efficiently working with data sources * Tuning data sources * Working efficiently with large data sources * Intelligent extracts * Understanding the Tableau data extract * Constructing an extract for optimal performance * Exercise: summary aggregates for improved performance * Optimizing extracts * Exercise: materialized calculations * Using filters wisely * Extract filter performance * Data source filter performance * Context filters * Dimension and measure filters * Table-calculation filters * Efficient calculations * Boolean/Numbers > Date > String * Additional performance considerations * Avoid overcrowding a dashboard * Fixing dashboard sizing * Setting expectations * Summary * ADDITIONAL COURSE DETAILS: Nexus Humans Advanced Tableau 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 Advanced Tableau course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for beginner to intermediate business and requirements analysts who are looking to improve their elicitation and requirements writing and documentation skills. This course is also a great fit for technical writers, product and software testers, project managers, product owners who work closely with business analysts or who perform some for of business analysis themselves. Overview Understand the role of the business analyst and core competencies for performing successfully Discuss the criticality of business analysis and requirements for successful project outcomes Understand the main professional associations and standards supporting business analysts in the industry Discuss the common problems with requirements and explore approaches to address these issues Obtain a clear understanding of the various requirements types and the significance for eliciting each type Demonstrate your ability to identify stakeholders Explore various methods for understanding and analyzing stakeholders Discuss and apply good planning practices to requirements elicitation efforts Obtain knowledge and understanding of over 15 current and commonly applied elicitation techniques Understand how to progress from elicitation to analysis to documentation Write well-formed and validated requirements Gain understanding of the best practices for writing quality requirements Learn the technical writing techniques that apply directly to writing requirements documents Discuss writing pitfalls, risks that impact requirements, and how to address them Learn best practices for communicating and collaborating with stakeholders, sharing the results of elicitation and the resulting documentation Learn approaches for validating requirements Understand the difference between validating requirements and validating the solution With elicitation serving as a major component of the requirements process, it is imperative that business analysts maintain high competency levels in elicitation practices and technique use to help organizations overcome the requirements related challenges faced on projects. Regardless whether you are a practitioner just starting off your career in business analysis or whether you have been performing the role for some years, this course will provide insight into the latest thoughts on elicitation and writing effective requirements and present a number of current techniques that are being applied on projects across industries today. REVIEW OF FOUNDATIONAL CONCEPTS * Definition of a business analysis * Definition of business analyst * BA role vs. PM role * Business analysis competencies * Benefits of business analysis * Purpose for having a BA standard * IIBA?s BABOK© Guide and PMI?s Practice Guide in Business Analysis * Business analysis core concepts * Discussion: Project challenges UNDERSTANDING REQUIREMENTS * Common problems with requirements * Understand the problem first * Define the business need * Situation statements and moving to requirements * Understanding requirement types * Business requirements * Stakeholder/User Requirements * Solution Requirements * Functional Requirements * Non-Functional Requirements * Assumptions and Constraints * Discussions: Requirement problems, business needs, and identifying non-functional requirements DISCOVERING STAKEHOLDERS * Definition of a stakeholder * Stakeholder types * Identifying stakeholders * Performing stakeholder analysis * Stakeholders and requirements * Tips for identifying stakeholders * Grouping stakeholders * Creating a RACI model * Tips for analyzing stakeholders * Documenting results of stakeholder analysis * Workshop: Discovering stakeholders PREPARING FOR REQUIREMENTS ELICITATION * Planning for elicitation * Benefits of elicitation planning * What do you plan? * The elicitation plan * Setting objectives for elicitation * Determining the scope for elicitation * Establishing pre-work * Determining the outputs for the session * The iterative nature of elicitation * Elicitation roles * Elicitation planning techniques * Discussions: Who to involve in elicitation, planning impacts, and unplanned elicitation * Workshop: Planning for elicitation CONDUCT REQUIREMENTS ELICITATION * Elicitation skillset * Types of elicitation techniques * Using active listening in elicitation * Techniques for performing elicitation * Benchmarking/Market Analysis * Brainstorming * Business Rules Analysis * Collaborative Games * Concept Modeling * Data Mining * Data Modeling * Document Analysis * Focus Groups * Interface Analysis * Interviews * Observation * Process Modeling * Prototyping * Survey or Questionnaire * Workshops WRITE EFFECTIVE REQUIREMENTS * Elicitation and Analysis * Requirements related issues * Implications of bad requirements * Elicitation and documentation * Writing skillset * Documenting requirements * Modeling requirements * Defining the project life cycle * Impact of project life cycle on documentation * Requirements specifications * Characteristics of good requirements * Guidelines for writing textual requirements * Structuring a requirement * Writing pitfalls * Traceability * Requirements attributes * Risks associated to requirements * Discussions: Project Life Cycle and Correcting Poorly Written Requirements * Workshops: Documenting Requirements and Identify Characteristics of Good Requirements CONFIRM AND COMMUNICATE ELICITATION REQUIREMENTS * Business analysis communication * Requirements communication * Communication skills * The 7 Cs * Timing of communication * Planning communication * Importance of Collaboration * Planning collaboration * Documenting communication/collaboration needs * Confirming elicitation results * Verify requirements * Characteristics of good requirements (revisited) * Requirements checklist * Requirements validation * Signing off on requirements * Discussions: Responsibility for Communication, Eliciting Communication Needs, Validation * Signoff * Workshops: Communicating Requirements and Obtaining Signoff EVALUATE THE SOLUTION * Business analyst role in solution evaluation * Why solutions under perform * What we are looking for in solution evaluation * When does solution evaluation occur * Performing solution evaluation * Planning solution evaluation * Metrics that might exist * Evaluating long term performance * Qualitative vs. quantitative measures * Tools & techniques used in solution evaluation * Comparing expected to actuals * When solution evaluation discovers a variance * Tools/techniques for analyzing variances * Proposing a recommendation * Communicating results of solution evaluation * Discussion: Addressing Variance WRAP UP AND NEXT STEPS * Useful books and links on writing effective requirements * BABOK© Guide * Business Analysis for Practitioners: A Practice Guide ADDITIONAL COURSE DETAILS: Nexus Humans BA04 - Eliciting and Writing Effective Requirements 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 BA04 - Eliciting and Writing Effective Requirements 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 If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. DATA EXPLORATION AND CLEANING * Python and the Anaconda Package Management System * Different Types of Data Science Problems * Loading the Case Study Data with Jupyter and pandas * Data Quality Assurance and Exploration * Exploring the Financial History Features in the Dataset * Activity 1: Exploring Remaining Financial Features in the Dataset INTRODUCTION TO SCIKIT-LEARN AND MODEL EVALUATION * Introduction * Model Performance Metrics for Binary Classification * Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve DETAILS OF LOGISTIC REGRESSION AND FEATURE EXPLORATION * Introduction * Examining the Relationships between Features and the Response * Univariate Feature Selection: What It Does and Doesn't Do * Building Cloud-Native Applications * Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients THE BIAS-VARIANCE TRADE-OFF * Introduction * Estimating the Coefficients and Intercepts of Logistic Regression * Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters * Activity 4: Cross-Validation and Feature Engineering with the Case Study Data DECISION TREES AND RANDOM FORESTS * Introduction * Decision trees * Random Forests: Ensembles of Decision Trees * Activity 5: Cross-Validation Grid Search with Random Forest IMPUTATION OF MISSING DATA, FINANCIAL ANALYSIS, AND DELIVERY TO CLIENT * Introduction * Review of Modeling Results * Dealing with Missing Data: Imputation Strategies * Activity 6: Deriving Financial Insights * Final Thoughts on Delivering the Predictive Model to the Client