7467 Courses delivered Online

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

This one-day workshop will give you a better understanding of the components and operations of an Access database. It is designed to build on a user's existing skills and includes useful action queries to allow greater manipulation of a database. This workshop will help participants: Ensure the integrity of their databases Manage field properties Use the query functions effectively Save time with the query expression builder Create different types of query more quickly Design better forms Link expressions in forms Create better and more useful reports Import and export tables more easily 1 Table relationship integrity Identifying relationships Identifying criteria for data integrity Applying referential integrity Managing relationship join types 2 Table field properties Field properties overview Using input mask field Using default value fields Using field validation rules 3 Query functions Running aggregate function calculations Running sum, average, count, max and min functions Grouping calculated data 4 Query calculations Using query operators and expressions Adding calculated fields to a query Using the query expression builder 5 Action queries Creating make table queries Creating append queries Creating update queries Creating delete queries 6 Designing forms Adding form controls Aligning and arranging form controls Adding pictures and labels to forms Adding new fields to a form Controlling tab order Adding command buttons Adding a combo box control Formatting data using conditional formatting 7 Form expressions (calculations) Using the form expression builder Working with a property sheet within a form Linking expressions within a form 8 Working with reports Creating reports with the report wizard Inserting report fields Formatting fields Inserting report headers and footers Working with a property sheet within a report 9 Grouped reports Creating groups with the report wizard Sorting grouped data Grouping alphabetically Grouping on date intervals Creating sub reports Adding calculations to groups 10 Importing and exporting tables Importing tables into Access Exporting tables from Access Importing and linking data in Access

Duration 2 Days 12 CPD hours This course is intended for Administrators Overview Please refer to course overview This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administering servers and content, in a distributed environment. In the course, participants will identify requirements for the installation and configuration of a distributed IBM Cognos Analytics software environment, implement security in the environment, and manage the server components. Students will also monitor and schedule tasks, create data sources, and manage and deploy content in the portal and IBM Cognos Administration. Introduction to IBM Cognos Analytics administration IBM Cognos Analytics components Administration workflow IBM Cognos Administration IBM Cognos Configuration Identify IBM Cognos Analytics architecture Features of the IBM Cognos Analytics architecture Examine the multi-tiered architecture, and identify logging types and files Examine IBM Cognos Analytics servlets Performance and installation planning Balance the request load Configure IBM Cognos Analytics Secure the IBM Cognos Analytics environment Identify the IBM Cognos Analytics security model Define authentication in IBM Cognos Analytics Define authorization in IBM Cognos Analytics Identify security policies Secure the IBM Cognos Analytics environment Administer the IBM Cognos Analytics server environment Administer IBM Cognos Analytics servers Monitor system performance Manage dispatchers and services Tune system performance, and troubleshoot the server Audit logging Dynamic cube data source administration workflow Manage run activities View current, past, and upcoming activities Manage schedules Manage content in IBM Cognos Administration Data sources and packages Manage visualizations in the library Deployment Other content management tasks Examine departmental administration capabilities Create and manage team members Manage activities Create and manage content and data Manage system settings Manage Themes, Extensions, and Views Share services with multiple tenants

Duration 2 Days 12 CPD hours This course is intended for Administrators Overview Please refer to course overview This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administering servers and content, in a distributed environment. In the course, participants will identify requirements for the installation and configuration of a distributed IBM Cognos Analytics software environment, implement security in the environment, and manage the server components. Students will also monitor and schedule tasks, create data sources, and manage and deploy content in the portal and IBM Cognos Administration. Introduction to IBM Cognos Analytics administration IBM Cognos Analytics components Administration workflow IBM Cognos Administration IBM Cognos Configuration Identify IBM Cognos Analytics architecture Features of the IBM Cognos Analytics architecture Examine the multi-tiered architecture, and identify logging types and files Examine IBM Cognos Analytics servlets Performance and installation planning Balance the request load Configure IBM Cognos Analytics Secure the IBM Cognos Analytics environment Identify the IBM Cognos Analytics security model Define authentication in IBM Cognos Analytics Define authorization in IBM Cognos Analytics Identify security policies Secure the IBM Cognos Analytics environment Administer the IBM Cognos Analytics server environment Administer IBM Cognos Analytics servers Monitor system performance Manage dispatchers and services Tune system performance, and troubleshoot the server Audit logging Dynamic cube data source administration workflow Manage run activities View current, past, and upcoming activities Manage schedules Manage content in IBM Cognos Administration Data sources and packages Manage visualizations in the library Deployment Other content management tasks Examine departmental administration capabilities Create and manage team members Manage activities Create and manage content and data Manage system settings Manage Themes, Extensions, and Views Share services with multiple tenants

This course starts with data transformation strategies, exploring capabilities in the Power Query Editor, and data-cleansing practices. It looks at the Advanced Query Editor to view the M language code. This course focuses on advanced DAX measures that include filtering conditions, with a deep dive into time intelligence measures. Like the M query language, DAX is a rich functional language that supports variables and expression references. This course also looks at the creation of dynamic dashboards and incorporates a range of visualisations available in Power BI Desktop and online in the AppSource. The course finishes with a look at setting up end user level security in tables. 1 The query editor Split by row delimiter AddDays to determine deadlines Advanced query editor 2 Fuzzy matching joins Matching inconsistencies by percentage Matching with transformation table 3 Logical column functions Logical functions IF, AND, OR Using multiple conditions Including FIND in functions 4 Editing DAX measures Make DAX easier to read Add comments to a measure Using quick measures 5 The anatomy of CALCULATE Understanding CALCULATE context filters Adding context to CALCULATE with FILTER Using CALCULATE with a threshold 6 The ALL measure Anatomy of ALL Create an ALL measure Using ALL as a filter Use ALL for percentage 7 DAX iterators Anatomy of iterators A closer look at SUMX Using RELATED in SUMX Create a RANKX RANKX with ALL 8 Date and time functions Overview of functions Create a DATEDIFF function 9 Time intelligent measures Compare historical monthly data Create a DATEADD measure Creating cumulative totals Creating cumulative measures Visualising cumulative totals 10 Visualisations in-depth Utilising report themes Create a heatmap Comparing proportions View trends with sparklines Group numbers using bins Setting up a histogram 11 Comparing variables Visualising trendlines as KPI Forecasting with trendlines Creating a scatter plot Creating dynamic labels Customised visualisation tooltips Export reports to SharePoint 12 User level security Setting up row level security Testing user security

This course is designed for those already using Power BI Desktop and are ready to work with more comprehensive elements of analysing and reporting in Power BI. The course maintains a balanced look at data analysis including the Power Query Editor, with a deep dive into writing DAX formulas, and enhanced dashboard visualisations. The aim of this course is to provide a more complete understanding of the whole Power BI analytics process, by working with business examples that will equip you with the necessary skills to output comprehensive reports and explore Power BI's analytical capabilities in more depth. 1 The Query Editor Grouping rows in a table Split row by delimiter Add days to determine deadlines The query editor 2 Fuzzy Matching Joins Matching inconsistencies by percentage Matching with transformation table 3 The Query Editor M Functions Adding custom columns Creating an IF function Nested AND logics in an IF function 4 DAX New Columns Functions Including TRUE with SWITCH Using multiple conditions The FIND DAX function The IF DAX function Logical functions IF, AND, OR 5 Editing DAX Measures Making DAX easier to read Add comments to a measure Using quick measures 6 The Anatomy of CALCULATE Understanding CALCULATE filters Add context to CALCULATE with FILTER Using CALCULATE with a threshold 7 The ALL Measure Anatomy of ALL Create an ALL measure Using ALL as a filter Use ALL for percentages 8 DAX Iterators Anatomy of iterators A closer look at SUMX Using RELATED with SUMX Create a RANKX RANKX with ALL 9 Date and Time Functions Overview of functions Create a DATEDIFF function 10 Time Intelligent Measures Compare historical monthly data Create a DATEADD measure Creating cumulative totals Creating cumulative measures Visualising cumulative totals 11 Visualisations In-Depth Utilising report themes Applying static filters Group data using lists Group numbers using bins Creating heatmaps Comparing proportions View trends with sparklines 12 Comparing Variables Visualising trendlines as KPI Forecasting with trendlines Creating a scatter plot Creating dynamic labels Customised visualisation tooltips Export reports to SharePoint