26 Mining courses delivered Live Online

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 5 Days 30 CPD hours This course is intended for The primary audience for this course are Application Consultants, Business Process Architects, and Business Process Owners/Team Leads/Power Users. Overview This course will prepare you to:Describe tasks performed by a typical SAP Access Control userDiscuss Harmonization topics as they relate to SAP Access ControlExplain how SAP GRC helps you to address business challengesIdentify authorization risks in typical business processesDescribe the Segregation of Duties Risk Management ProcessDescribe and configure functionality and features for SAP Access Control 10.1Use the SAP Access Control 10.1 application to analyze and manage risk, design and manage roles, and provision and manage usersDescribe the SAP Access Control 10.1 architecture and landscape, SAP Access Control Repository, and Object Level SecurityDescribe the Periodic Access Review processPlan for and manage emergency accessDiscuss the reporting frameworkConfigure workflows, including multi-stage multi-path (MSMP) workflows and BRF+Describe how the different applications of the SAP GRC Solution integrate with each otherDiscuss key steps in the SAP Access Control implementation process In this course, students gain hands-on configuration and implementation experience of SAP Access Control 10.1, as well as important concepts they will need to know in order to prepare for implementation and ongoing access risk prevention. Introduction to SAP Access Control Discussing Business Challenges and Solutions Using SAP Access Control Architecture, Security, and Authorizations Describing the System Architecture Describing Security and Authorizations Shared Configuration Settings Configuring Shared GRC Settings Configuring Shared SAP Access Control Settings Authorization Risks and the Segregation of Duties (SoD) Management Process Identifying Authorization Risks Managing Risk by Segregating Duties SAP Access Control Repository Synchronizing Objects into the Repository Scheduling and Viewing Background Jobs Risk Analysis Maintaining Shared Master Data Configuring and Maintaining the Rule Set Configuring and Using Audit Trail Tracking Using the Risk Analysis Framework Remediating Risks Mitigating Risks Mitigating Multiple Risks at One Time Business Rule Framework Creating Rules in the Business Rule Framework (BRF) Defining Business Rules Multi-Stage, Multi-Path (MSMP) Workflow Describing Multi-Stage, Multi-Path (MSMP) Workflow Maintaining MSMP Workflow Building MSMP Workflow User Provisioning Configuring User Provisioning Settings Configuring Access Request Forms Requesting Access Preparing Roles and Owner Data for MSMP Workflow Creating Simplified Access Requests Reviewing Search Request Results SAP Fiori User Experience (UX) for GRC Describing SAP Fiori UX Role Design and Management Configuring Role Management Configuring Role Methodology Configuring Role Search Attributes Planning for Technical Role Definition Planning for Business Role Definition Consolidating Roles Through Role Mining Performing Role Mass Maintenance Operations Emergency Access Management Describing Emergency Access Management Planning for Emergency Access Monitoring Emergency Access Periodic Access Review Process Planning Periodic Review Monitoring Periodic Review Reports and Custom Fields Use the Reporting Framework SAP Access Control Implementation Using the SAP Access Control Implementation Process Designing the SAP Access Control Solution Planning Upgrade and Migration Configuring SAP Access Control Implementing the SAP Access Control solution Optimizing the SAP Access Control Suite Additional course details: Nexus Humans GRC300 SAP Access Control Implementation and Configuration 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 GRC300 SAP Access Control Implementation and Configuration 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 2.5 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