663 Business Analytics & Intelligence courses in Liverpool delivered Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Duration 2 Days 12 CPD hours This course is intended for Report Authors Overview Create query models Create reports based on query relationships Introduction to dimensional data Introduction to dimensional data in reports Dimensional report context Focus your dimensional data Calculations and dimensional functions Create advanced dynamic reports This offering teaches Professional Report Authors about advanced report building techniques using relational data models, dimensional data, and ways of enhancing, customizing, managing, and distributing professional reports. The course builds on topics presented in the Fundamentals course. Activities will illustrate and reinforce key concepts during this learning activity. Create query models Build a query and connect it to a report Answer a business question by referencing data in a separate query Create reports based on query relationships Create join relationships between queries Combine data containers based on relationships from different queries Create a report comparing the percentage of change Introduction to dimensional reporting concepts Examine data sources and model types Describe the dimensional approach to queries Apply report authoring styles Introduction to dimensional data in reports Use members to create reports Identify sets and tuples in reports Use query calculations and set definitions Dimensional report context Examine dimensional report members Examine dimensional report measures Use the default measure to create a summarized column in a report Focus your dimensional data Focus your report by excluding members of a defined set Compare the use of the filter() function to a detail filter Filter dimensional data using slicers Calculations and dimensional functions Examine dimensional functions Show totals and exclude members Create a percent of base calculation Create advanced dynamic reports Use query macros Control report output using a query macro Create a dynamic growth report Create a report that displays summary data before detailed data and uses singletons to summarize information Design effective prompts Create a prompt that allows users to select conditional formatting values Create a prompt that provides users a choice between different filters Create a prompt to let users choose a column sort order Create a prompt to let users select a display type Examine the report specification Examine report specification flow Identify considerations when modifying report specifications Customize reporting objects Distribute reports Burst a report to email recipients by using a data item Burst a list report to the IBM Cognos Analytics portal by using a burst table Burst a crosstab report to the IBM Cognos Analytics portal by using a burst table and a master detail relationship Enhance user interaction with HTML Create interactive reports using HTML Include additional information with tooltips Send emails using links in a report Introduction to IBM Cognos Active Reports Examine Active Report controls and variables Create a simple Active Report using Static and Data-driven controls Change filtering and selection behavior in a report Create interaction between multiple controls and variables Active Report charts and decks Create an Active Report with a Data deck Use Master detail relationships with Decks Optimize Active Reports Create an Active Report with new visualizations

Duration 2 Days 12 CPD hours This course is intended for Report authors working with dimensional data sources. Through interactive demonstrations & exercises, participants will learn how to author reports that navigate & manipulate dimensional data structures using the specific dimensional functions & features available in IBM Cognos Analytics. Introduction to Dimensional Concepts Identify different data sources and models Investigate the OLAP dimensional structure Identify dimensional data items and expressions Differentiate the IBM Cognos Analytics query language from SQL and MDX Differentiate relational and dimensional report authoring styles Introduction to Dimensional Data in Reports Work with members Identify sets and tuples in IBM Cognos Analytics Dimensional Report Context Understand the purpose of report context Understand how data is affected by default and root members Focus Your Dimensional Data Compare dimensional queries to relational queries Explain the importance of filtering dimensional queries Evaluate different filtering techniques Filter based on dimensions and members Filter based on measure values Filter using a slicer Calculations & Dimensional Functions Use IBM Cognos Analytics dimensional functions to create sets and tuples Perform arithmetic operations in OLAP queries Identify coercion errors and rules Functions for Navigating Dimesional Hierarchies Navigate dimensional data using family functions Relative Functions Navigate dimensional data using relative functions Navigate dimensional data using relative time functions Advanced Drilling Techniques & Member Sets Understand default drill-up and drill-down functionality Identify cases when you need to override default drilling behavior Configure advanced drilling behavior to support sophisticated use cases Define member sets to support advanced drilling Define member sets to support functions Set Up Drill-Through Reports Navigate from a specific report to a target report Drill down to greater detail and then navigate to target report Navigate between reports created using different data sources End-to-End Workshop Review concepts covered throughout the course

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course are Application Consultants, Business Analysts, Business Process Owners/Team Leads/Power Users, Program/Project Managers, and Users. Overview Learn how to create queries in BEx Query DesignerLearn how to use advanced query functionsLearn how to perform OLAP analysis In this course, students obtain the knowledge to create query definitions using the BEx Query Designer and to make them available for OLAP analysis. Data Warehousing Describing Data Warehousing Describing Data Warehouse Architecture Using Reporting Tools Navigation Options in Reports Using the Navigation Options in Reports Saving Analysis Views Simple Queries Creating Simple Queries Finding a Query Filtering Query Definition Data Configuring Query Properties Key Figures and Structures in Queries Creating Restricted Key Figures Creating Calculated Key Figures Creating a New Formula with Boolean Operators Configuring Properties of Key Figures Using Exception and Nested Exception Aggregation Queries with Multiple Structures Creating Structures Resolving Formula Collision Designing Detailed Queries with the Cell Editor Characteristics and Hierarchies in Queries Configuring the Properties of Characteristics Running Display and Navigation Attribute Queries Adding Hierarchies to Reports Adding External Hierarchies to a Report Using Hierarchies and Structures Creating External Hierarchies Variables in Queries Using Variables Creating Characteristic Value and Text Variables in Queries Creating Hierarchy and Hierarchy Node Variables in Queries Creating Formula Variables in Queries Activating Business Content Variables Exceptions and Conditions in Queries Creating Exceptions in Query Design Creating Conditions in Query Design Report-Report Interface Using the Report-Report Interface Query Performance Optimization Optimizing Query Performance Configuring Query Read Mode Use Performance Monitoring Tools Queries Management and Authorizations Overview Managing Query Objects Describing Authorizations Reporting Options Outlining Reporting Options for SAP NetWeaver BW Additional course details: Nexus Humans BW305 SAP Business Warehouse Query Design and Analysis 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 BW305 SAP Business Warehouse Query Design and Analysis 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 The primary audience for this course are Application Consultants, Business Analysts, and Business Process Owner/Team Lead/Power Users. Overview Create Web Intelligence documents for your reporting needsRetrieve data by building queries using SAP BusinessObjects universesUse Web Intelligence to enhance documents for easier analysisOrganize, manage and distribute documents using the BI launch pad This course is designed to give students the comprehensive skills and in-depth knowledge needed to access, analyze and share data using SAP BusinessObjects BI launch pad and SAP BusinessObjects Web Intelligence. Web Intelligence Describing Web Intelligence Concepts Explaining Web Intelligence Core Functions Describing Web Intelligence and the SAP BusinessObjects Business Intelligence Platform Web Intelligence Documents with Queries Creating Queries Creating a Document Modifying a Document?s Query Modify the Query Panel Data Return Restrictions from Queries Restricting Data with Query Filters Modifying a Query with a Predefined Query Filter Applying a Single-Value Query Filter Using Wildcards in Query Filters Using Prompts to Restrict Data Using Complex Filters Web Intelligence Document Design Modifying Web Intelligence Documents Displaying Data in Tables Using Tables Presenting Data in Free-standing Cells Presenting Data in Charts Presentation of Data in Documents Using Breaks and Calculations Using Sorts and Hiding Data Formatting Breaks and Cross Tables Filtering Report Data Ranking Data Highlighting Information with Formatting Rules Organizing a Report into Sections Web Intelligence Document Formatting Formatting Documents Formatting Charts Data Calculation Applying Formulas and Variables Using Formulas and Variables Drilling Functionality Analyzing the Data Cube Drilling in an Web Intelligence Document Web Intelligence Documentation Management Publishing Documents to the Corporate Repository Logging In to the BI Launch Pad Managing Documents in BI Launch Pad Viewing a Web Intelligence Document in the BI Launch Pad Sharing Web Intelligence Documents Setting BI Launch Pad Preferences Logging Off From the BI Launch Pad

Ai (Artificial Intelligence) for Business Leaders Recognize the challenges and limitations of Generative AI, and gain insight into the future outlook of AI in business strategy. Equip yourself with the essential skills and knowledge needed to lead in an AI-driven business landscape. Learning Outcomes: Explain the fundamentals of Artificial Intelligence for business. Analyze the strategic impact of AI on business models. Evaluate the business implications of Generative AI. Assess the challenges and limitations of Generative AI. Predict the future role of AI in business strategy. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Ai (Artificial Intelligence) for Business Leaders Course Syllabus Ai (Artificial Intelligence) for Business: Gain a comprehensive understanding of AI and its significance in the business landscape, equipping you with the foundational knowledge to lead AI-driven initiatives. Strategic Impact of Ai (Artificial Intelligence) on Business: Explore how AI strategically impacts different business models, enabling you to identify opportunities for innovation and growth. Implications of Generative Ai (Artificial Intelligence): Delve into the applications and implications of Generative AI within business contexts, fostering the ability to harness its creative potential. Challenges of Ai (Artificial Intelligence): Understand the challenges and limitations associated with Generative AI, allowing you to make informed decisions and mitigate potential risks. Ai (Artificial Intelligence) in Business Strategy: Anticipate the future landscape of AI in business strategy, enabling you to proactively adapt and lead in an AI-driven environment. Leaders in Ai (Artificial Intelligence)-Driven Business: Develop the essential skills and knowledge required to lead AI-driven initiatives effectively, ensuring your leadership in the AI era is both informed and visionary.

Duration 3 Days 18 CPD hours This course is intended for Authors Overview Please refer to course overview This course provides authors with an introduction to build reports using Cognos Analytics. Techniques to enhance, customize, and manage reports will be explored. Activities will illustrate and reinforce key concepts during this learning opportunity. What is IBM Cognos Analytics - Reporting The Welcome page Consume report content Interactive filtering Working with reports Dimensionally modeled relational dataUse personal data sources and data modules Upload personal data Upload custom images Using navigation paths in a data module Examine list reports Group data Format columns Include headers and footers Aggregate fact data Identify differences in aggregation Multiple facts and repeated information Use shared dimensions to create multi-fact queries Present repeated information Add repeated information to reports Create a mailing list report Create crosstab reports Add measures to a crosstab Data sources for a crosstab Create complex crosstab reports Add items as peers Create crosstab nodes and crosstab members Work with crosstab data Format, sort, and aggregate a crosstab Create discontinuous crosstab reportsCreate visualization reports Visualization categories Customize visualizations Client side visualizations Enhanced map visualizations Focus reports using filters Create filters to narrow the focus Use advanced detail filters Apply a filter with aggregation Use summary filters Focus reports using prompts Examine parameters and prompts Create a parameter for a report item Add a prompt page Add a prompt item to a report Identify a prompt type Create a cascading prompt Use calculations What are calculations Add Date and Time functions Add string functions Display prompt selections in report titles Customize reports with conditional formatting Three steps for conditional formatting Create a variable Assign the variable to a report object Format based on the conditional value Conditionally render report objects Drill-through definitions Navigate to related data Enhance report layout View the structure of a report Use Guided report layout Force page breaks Create horizontal pagination Modify the report structure Format objects across reports Use additional report-building techniques Enhance a report design Add objects to reports Convert a list to a crosstab Explore reuse Additional course details: Nexus Humans B6258 IBM Cognos Analytics - Author Reports Fundamentals V11.1.x 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 B6258 IBM Cognos Analytics - Author Reports Fundamentals V11.1.x 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 This is a 2 day applied course on XVA for anyone interested in going beyond merely a conceptual understanding of XVA and wants practical examples of Monte Carlo simulation of market risk factors to create exposure distributions and profiles for derivatives used for XVA pricing Learn how to do Monte Carlo simulation of key market risk factors across major asset classes to create exposure distributions and profiles (with and without collateral) for derivatives used for XVA pricing. Learn how to calculate each XVA. Learn sensitivities of each XVA and how XVA desks manage these. Learn regulatory capital treatment of counterparty credit risk (both for CCR and CVA volatility) and how to stress test this within ICAAP or system-wide external, supervisor-led capital stress test. Who the course is for Anyone involved in OTC derivatives XVA traders XVA quants Derivatives traders and salespeople Risk management Treasury staff Internal audit and finance Course Content To learn more about the day by day course content please click here To learn more about schedule, pricing & delivery options, book a meeting with a course specialist now

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

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