378 Courses

Duration 2 Days 12 CPD hours This course is intended for The primary audience for this course is as follows: System Engineers System Administrators Architects Channel Partners Data Analysts Overview Upon completing this course, you will be able to meet these overall objectives: Describe how harnessing the power of your machine data enables you to make decisions based on facts, bot intuition or best guesses. Reduce the time you spend investigating incidents by up to 90%. Find and fix problems faster by learning new technical skills for real world scenarios. Get started with Splunk Enterprise, from installation and data onboarding to running search queries to creating simple reports and dashboards. Accelerate time to value with turnkey Splunk integrations for dozens of Cisco products and platforms. Ensure faster, more predictable Splunk deployments with a proven Cisco Validated Design and the latest Cisco UCS server. This course will cover how Splunk software scales to collect and index hundreds of terabytes of data per day, across multi-geography, multi-datacenter and cloud based infrastructures. Using Cisco?s Unified Computing System (UCS) Integrated Infrastructure for Big Data offers linear scalability along with operational simplification for single-rack and multiple-rack deployments. Cisco Integrated Infrastructure for Big Data and Splunk What is Cisco CPA? Architecture benefits for Splunk Components of IIBD and relationship to Splunk Architecture Cisco UCS Integrated Infrastructure for Big Data with Splunk Enterprise Splunk- Big Data Analytics NFS Configurations for the Splunk Frozen Data Storage NFS Client Configurations on the Indexers Splunk- Start Searching Chargeback Reporting Building custom reports using the report builder Application Containers Understanding Application Containers Understanding Advanced Tasks Task Library & Inputs CLI & SSH Task Understanding Compound Tasks Custom Tasks Open Automation Troubleshooting UCS Director Restart Module Loading Report Errors Feature Loading Report Registration REST API- Automation UCS Director Developer Tools Accessing REST using a REST client Accessing REST using the REST API browser Open Automation SDK Overview Open Automation vs. Custom Tasks Use Cases UCS Director PowerShell API Cisco UCS Director PowerShell Console Installing & Configuring Working with Cmdlets Cloupia Script Structure Inputs & Outputs Design Examples Additional course details: Nexus Humans Cisco Splunk for Cisco Integrated Infrastructure (SPLUNK) 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 Cisco Splunk for Cisco Integrated Infrastructure (SPLUNK) 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 course is intended for Business Leaders, including managers/supervisors in the following roles: Developer Architect Video Operator Overview In this course, you will learn to: Articulate the essential terms and concepts fundamental to video compression and distribution Describe the four fundamental stages of video streaming workflows: ingest, process, store and deliver Explain the importance of security in the AWS Cloud and how it is applied in video streaming workflows Analyze video streaming workflow diagrams using AWS services, based on simple to complex use cases Describe some of the key variables that influence workflow decisions Recognize how other AWS services for compliance, storage, and compute, interact with AWS Media Services in video streaming workflows and the functions they perform Describe strategies to test or prototype workflows to mitigate risk and cost impacts and optimize video streaming workflows Use the AWS Management Console to build and run simple video streaming workflows for live and video-on-demand content Recognize the automation and data analytics available for Media Services when used with AWS AI and explore media-specific use cases for these services Identify the next steps in exploring migration to the cloud for one or more Media Services This course covers the media and cloud fundamentals that will empower you to develop a cloud migration strategy for media workflows in support of business goals. The course covers important concepts related to video processing and delivery, the variables that can impact migration decisions, and real-world examples of hybrid and cloud use cases for AWS Media Services. It also introduces security, artificial intelligence, and analytics concepts to help you consider how AWS Media Services fit into your overall cloud strategy. Module 1: Important video concepts Video Metrics Video Compression Video Distribution Major Protocols Used in Video Streaming Module 2: Anatomy of streaming workflows Ingest Process Store Deliver Module 3: Using AWS services in media workflows video-on-demand (VOD) Introduction to AWS Media Services Security Variables Impacting Workflow Design VOD Simple Use Cases VOD Advanced Use Cases Lab 1: Build and run a simple video streaming workflow for VOD content Module 4: Using AWS services in media workflows live streaming Challenges of Live Streaming Live Streaming Simple Use Cases Live Streaming Advanced Use Cases Lab 2: Build and run a simple video streaming workflow for live content Module 5: Optimizing Workflows Cost Considerations Mitigating Risk Monitoring and Automation Exploring Migration Options Additional course details: Nexus Humans AWS Media Essentials for IT Business Decision Makers 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 AWS Media Essentials for IT Business Decision Makers 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 course is relevant to anyone who needs to work with and understand data including: Business Analysts, Data Analysts, Reporting and BI professionals Marketing and Digital Marketing professionals Digital, Web, e-Commerce, Social media and Mobile channel professionals Business managers who need to interpret analytical output to inform managerial decisions Overview This course will cover the basic theory of data visualization along with practical skills for creating compelling visualizations, reports and dashboards from data using Tableau. Outcome: After attending this course delegates will understand - How to move from business questions to great data visualizations and beyond How to apply the fundamentals of data visualization to create informative charts How to choose the right visualization type for the job at hand How to design and develop basic dashboards in Tableau that people will love to use by doing the following: Reading data sources into Tableau Setting up the roles and data types for your analysis Creating new data fields using a range of calculation types Creating the following types of charts - cross tabs, pie and bar charts, geographic maps, dual axis and combo charts, heat maps, highlight tables, tree maps and scatter plots Creating Dashboards that delight using the all of the features available in Tableau. The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to tourism. From Business Questions to Data Visualisation and Beyond The first step in any data analysis project is to move from a business question to data analysis and then on to a complete solution. This section will examine this conversion emphasizing: The use of data visualization to address a business need The data analytics process ? from business questions to developed dashboards Introduction to Tableau ? Part 1 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Introduction to Tableau ? Part 2 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Key Components of Good Data Visualisation and The Visualisation Zoo In this section the following topics will be covered: Colour theory Graphical perception & communication Choosing the right chart for the right job Data Exploration with Tableau Exploring data to answer business questions is one of the key uses of applying good data visualization techniques within Tableau. In this section we will apply the data visualization theory from the previous section within Tableau to uncover trends within the data to answer specific business questions. The types of charts that will be covered are: Cross Tabs Pie and bar charts Geographic maps Dual axis and combo charts with different mark types Heat maps Highlight tables Tree maps Scatter plots Introduction to Building Dashboards with Tableau In this section, we will implement the full process from business question to final basic dashboard in Tableau: Introduction to good dashboard design Building dashboards in Tableau

Duration 3 Days 18 CPD hours This course is intended for Network Security Operations Workload Application Administrators Security Operations Field Engineers Network Engineers Systems Engineers Technical Solutions Architects Cisco Integrators and Partners Overview After taking this course, you should be able to: Define the Cisco telemetry and analytics approach. Explore common scenarios that Cisco Tetration Analytics can solve. Describe how the Cisco Tetration Analytics platform collects telemetry and other context information. Discuss how relative agents are installed and configured. Explore the operational aspects of the Cisco Tetration Analytics platform. Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature. List the concepts of the intent-based declarative network management automation model. Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy. Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data. Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center. List the options for the Cisco Tetration Analytics platform enhancements. Explain how to perform the Cisco Tetration Analytics administration. This course teaches how to deploy, use, and operate Cisco© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management. Exploring Cisco Tetration Data Center Challenges Define and Position Cisco Tetration Cisco Tetration Features Cisco Tetration Architecture Cisco Tetration Deployment Models Cisco Tetration GUI Overview Implementing and Operating Cisco Tetration Explore Data Collection Install the Software Agent Install the Hardware Agent Import Context Data Describe Cisco Tetration Operational Concepts Examining Cisco Tetration ADM and Application Insight Describe Cisco Tetration Application Insight Perform ADM Interpret ADM Results Application Visibility Examining Cisco Tetration Intent-Based Networking Describe Intent-Based Policy Examine Policy Features Implement Policies Enforcing Tetration Policy Pipeline and Compliance Examine Policy Enforcement Implement Application Policy Examine Policy Compliance Verification and Simulation Examining Tetration Security Use Cases Examine Workload Security Attack Prevention Attack Detection Attack Remediation Examining IT Operations Use Cases Key Features and IT Operations Use Cases Performing Operations in Neighborhood App-based Use Cases Examining Platform Enhancement Use Cases Integrations and Advanced Features Third-party Integration Examples Explore Data Platform Capabilities Exploring Cisco Tetration Analytics Administration Examine User Authentication and Authorization Examine Cluster Management Configure Alerts and Syslog Additional course details: Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 2 Days 12 CPD hours This course is intended for The audience for this course includes professionals who are new to Looker who are interested in leveraging Looker for data analysis, visualization, and reporting. The course is designed for individuals seeking to gain a comprehensive understanding of Looker's functionalities and apply these skills in their organizations to drive data-driven decision-making. Overview This course combines expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert facilitator, you'll explore and gain: Comprehensive understanding of Looker's platform: Gain a solid foundation in Looker's key features, functionality, and interface, enabling you to navigate and utilize the platform effectively for your data analysis and visualization needs. Mastery of LookML and data modeling: Develop proficiency in Looker's unique data modeling language, LookML, to create customized and efficient data models that cater to your organization's specific requirements. Expertise in creating insightful Explores: Learn to build, customize, and save Explores with dimensions, measures, filters, and calculated fields, empowering you to analyze your data and uncover valuable insights. Proficiency in dashboard design and sharing: Acquire the skills to design visually appealing and informative dashboards, share them with different user roles, and schedule exports to keep stakeholders informed and up-to-date. Enhanced content organization with folders and boards: Understand how to effectively use folders and boards to organize, manage, and discover content within Looker, making it easily accessible for you and your team. Optional: Advanced visualization techniques for impactful storytelling: Master advanced visualization techniques, including customizations with HTML, CSS, and JavaScript, and interactive visualizations using Looker's API, to create compelling data stories that resonate with your audience. Discover the power of data analytics and visualization with our hands-on, two-day introductory course Looker Bootcamp: Analyzing and Visualizing Data with Looker. Designed for professionals who want to unlock valuable insights from their data, this immersive training experience will guide you through Looker's cutting-edge features and provide you with the essential skills to create engaging, interactive, and insightful reports and dashboards. Our experienced trainers will take you on a journey from the fundamentals of Looker and its unique data modeling language, LookML, to advanced visualization techniques and content organization strategies, ensuring you leave the course equipped to make data-driven decisions with confidence. Throughout the course, you will have the opportunity to participate in practical exercises and workshops that will help you apply the concepts and techniques learned in real-world scenarios. You will explore the potential of Looker's Explores, dive into LookML's capabilities, and master the art of dashboard design and sharing. Learn how to organize and manage your content with folders and boards and harness the power of advanced visualization techniques to make your data come alive. Getting Started with Looker Overview of Looker and its key features Navigating the Looker interface Looker terminology and basic concepts Connecting to Data Sources Setting up and managing data connections Exploring database schemas Understanding LookML: Looker's data modeling language Creating and Customizing Explores Building and customizing Explores Adding dimensions, measures, and filters Creating calculated fields Saving and organizing Explores Data Visualization Creating visualizations using Looker's visualization library Customizing chart types, colors, and labels Displaying visualizations in dashboards Introduction to Looker's API for custom visualizations Advanced Explores and LookML LookML refresher and best practices Creating derived tables and data transformations Managing access controls and data permissions Organizing and Sharing Content with Folders and Boards Introduction to folders and boards in Looker Creating and managing folders for organizing content Setting up boards for easy content discovery Sharing folders and boards with different user roles and permissions Dashboard Design and Sharing Best practices for dashboard design Adding, arranging, and resizing visualizations Scheduling and exporting dashboard data Advanced Visualization Techniques Customizing visualizations with HTML, CSS, and JavaScript Creating interactive visualizations using Looker's API Integrating Looker visualizations with other tools Hands-on Workshop and Project Participants work on a guided project to apply the skills learned Trainer provides individual support and guidance Project Presentations, Q&A, and Training Wrap-up Additional course details: Nexus Humans Looker Bootcamp: Analyzing and Visualizing Data with Looker (TTDVLK02) 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 Looker Bootcamp: Analyzing and Visualizing Data with Looker (TTDVLK02) 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.

The Fintech Frontier: Why FDs Need to Know About Fintech,” the podcast where we delve into the world of financial technology There are numerous areas where fintech can make a significant impact. For example, payment processing and reconciliation can be streamlined through digital payment solutions and automated tools. Data analytics and artificial intelligence can enhance financial forecasting, risk management, and fraud detection. Blockchain technology can revolutionize supply chain finance and streamline processes involving multiple parties. By understanding the capabilities of these fintech solutions, FDs can identify areas for improvement and select the right technologies to optimise their financial operations. Additionally, fintech can greatly enhance financial reporting and analysis. Advanced data analytics tools can extract meaningful insights from vast amounts of financial data, enabling FDs to make data-driven decisions and identify trends and patterns. Automation of repetitive tasks, such as data entry and reconciliation, reduces the risk of errors and frees up valuable time for FDs to focus on strategic initiatives. The adoption of cloud-based financial management systems also provides flexibility, scalability, and real-time access to financial data, empowering FDs to make informed decisions on the go. With the rapid pace of fintech advancements, how can FDs stay up to date and navigate the evolving fintech landscape? Continuous learning and engagement with the fintech community are key. Attend industry conferences, participate in webinars and workshops, and engage with fintech startups and established players. Networking with professionals in the field, joining fintech-focused associations, and following relevant publications and blogs can help FDs stay abreast of the latest fintech developments. Embracing a mindset of curiosity and adaptability is crucial in navigating the ever-changing fintech landscape. I would also encourage FDs to foster partnerships and collaborations with fintech companies. Engage in conversations with fintech providers to understand their solutions and explore potential synergies. By forging strategic partnerships, FDs can gain access to cutting-edge technologies and co-create innovative solutions tailored to their organisation’s unique needs. As we conclude, do you have any final thoughts or advice for our FD audience regarding fintech? Embrace fintech as an opportunity, not a threat. Seek to understand its potential and how it can align with your organisation’s goals and strategies. Be open to experimentation and pilot projects to test the viability of fintech solutions. Remember that fintech is a tool to enhance and optimize financial processes, and as FDs, we have a crucial role in driving its effective implementation. https://www.fdcapital.co.uk/podcast/the-fintech-frontier-why-fds-need-to-know-about-fintech/ Tags Online Events Things To Do Online Online Conferences Online Business Conferences #event #fintech #knowledge #fds #frontier