1243 Business Analysis courses delivered Online

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it's often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 1 Days 6 CPD hours This course is intended for New administrators, business analysts or report writers who are new to creating reports or dashboards within Salesforce. Overview A student in this class will learn the basic Salesforce object model, and how to create and secure reports and dashboards. The instructor will lead students through exercises to create tabular, summary, matrix and join reports. Students will learn advanced reporting functionality such as charting, report summary fields, bucket fields, conditional highlighting, advanced report filters and building custom report types. Finally, the student will learn how to create and run dashboards and schedule and email reports and dashboards. This course is specifically designed to teach administrators, business analysts or report writers how to utilize the basic and advanced analytic capabilities of Salesforce. Introductions / Login to Training OrgsOverview of Salesforce Object ModelTabular, Summary, Matrix, Join ReportsCharts, Bucket Fields, Report Summary Fields, Conditional HighlightingCustom Report TypesDashboardsReport & Dashboard Scheduling Additional course details: Nexus Humans Introduction to Salesforce.com Analytics - Building Reports and Dashboards 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 Introduction to Salesforce.com Analytics - Building Reports and Dashboards 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 introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences, Arrays, Dictionaries and Sets About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Duration 2 Days 12 CPD hours This course is intended for Application Consultants, Business Analysts, and Process Owners Overview Learn to navigate the catalog, create and approve requisitions, and receive against POs. This course covers all of the functions of SAP Ariba Buying related to the creation of requisitions, the issuing of purchase orders, and receiving against purchase orders. Course Outline Introduction The Dashboard Catalogs Requisitions Accounting Approval Managing POs Receiving SAP Ariba Mobile Searching and Reporting Collaboration Demand Aggregation

Duration 2 Days 12 CPD hours This course is intended for Application Developers Business Analysts Developer End Users Functional Implementer Java Developer System Analysts Technical Administrator Overview Create administrator-level personalizations Personalize configurable pages Utilize advanced personalization features Implement flexfields on OA Framework-based pages Create custom look and feel definitions Create user-level personalizations This course will be applicable for customers who have implemented Oracle E-Business Suite Release 12 or Oracle E-Business Suite 12.1. This course will be applicable for customers who have implemented Oracle E-Business Suite Release 12 or Oracle E-Business Suite 12.1.

Duration 2 Days 12 CPD hours This course is intended for Application consultants, Business Analysts, Executives, Technology Consultants, Users Overview By the end of this course, students will be able to:Explain SAP LumiraCreate documents and acquire dataPrepare datasetsVisualize dataShare stories In this course, students will learn how to create stunning and interactive visualizations by choosing a rich library of visualization types, ranging from scatter plots, heat and geo maps to tag clouds, box plots and network charts. Course Outline Positioning and Overview of SAP Lumira Discovery Navigating the BI Launchpad Acquiring Data Enrich the Dataset Create Visualizations Create a Story Sharing Options Using the Lumira Discovery Formula Editor Additional Data Sources Data Mashups

Duration 2 Days 12 CPD hours This course is intended for Applications consultants, business analysts, business process owners, developer consultants, help desk consultants, project managers, and technology consultants. Overview This course will prepare you to: Give an overview of the S/4HANA embedded analytics architecture Explain the analytical offerings per user type Give an Introduction to SAP Best Practices for Analytics with SAP S/4HANA Explain the integration scenarios with SAP Business Warehouse In this course, students will gain an overview of the S/4HANA embedded analytics real-time operational reporting, its offerings per user type for the S/4HANA 610 and its integration with SAP Business Warehouse. Module 1 S/4HANA and S/4HANA embedded analytics introduction Module 2 S/4HANA embedded analytics architecture overview Module 3 Analytical Consumption Module 4 SAP Best Practices for Analytics with SAP S/4HANA Module 5 SAP Business Warehouse Integration Scenarios

Duration 2 Days 12 CPD hours This course is intended for Application Consultants, Business Analysts, and Program Managers Overview Describe how to organize and run payroll including subsequent activities and problem-solving aids This course provides the mandatory foundation knowledge required for processing payroll transactions in SAP HCM. Payroll Overview Setting Up the User Interface Identifying Payroll Elements Payroll Data Entering payroll data Payroll Elements Organizing a Payroll Run Reviewing the Payroll Status Infotype Identifying Retroactive Payroll Entries Payroll Process Running Payroll Payroll Reports Reporting on Payroll Generating Remuneration Statements Analyzing Payroll Results Analyzing Wage Types Reviewing Ad Hoc Query Functionality Post Payroll Results Posting Environments Verifying a Posting Run Updating a Live Posting Run Verifying Documents Bank Transfers & Check Preparation Generating Employee Payments Process Model Running a Payroll Process Model SuccessFactors Employee Central Payroll Outling employee central payroll basics

Duration 2 Days 12 CPD hours This course is intended for Application Consultant Business Analyst Business Process Architect Business Process Owner /Team Lead /Power User Enterprise Architect Executive Program/Project Manager Overview This course will prepare you to: Identify the key functional areas of SAP S/4HANA Finance Describe the capabilities of SAP S/4HANA Finance using selected business processes Understand integration aspects between Finance and Management Accounting Understand the concept of SAP FIORI in the context of SAP S/4HANA Finance This course will prepare you to Identify the key functional areas of SAP S/4HANA Finance Describe the capabilities of SAP S/4HANA Finance using selected business processes Understand integration aspects between Finance and Management Accounting Understand the concept of SAP FIORI in the context of SAP S/4HANA Finance Course Outline Positioning of Finance processes in the SAP S/4HANA Finance solution Financial Accounting: General Ledger Accounting Financial Accounting: Accounts Payable Integration Aspects between materials Management and Financial Accounting Management Accounting: Cost Centers Management Accounting: Internal Orders Financial Accounting: Asset Accounting Financial Accounting: Accounts Receivable Brief Overview of the Central Finance Option