382 Sciences courses in Frome delivered Live Online

The Positive Psychology course to explore core values, shape a profound vision and create an actionable plan for your best life.

This programme provides an intensive, two-day overview of the key elements of operations management, including an array of practical tips and tools to help managers be more proactive and effective in the operations management environment - whether that's in an industrial manufacturing context or in operational leadership in the service sectors. At the end of the programme, participants will: Understand the 6Cs approach to operations management Be able to apply a range of practical tools and techniques to improve their personal effectiveness towards being a more effective operations manager Be able to prepare an action plan for the critical first (or next) 100 days in their operational leadership role 1 Introduction What is Operations Management and where does it fit in? What makes a successful Operations Manager? Introducing the 6Cs of Operations Management 2 Context Link to business strategy Making a year plan Performance measurement 3 Controls Governance Reputational controls Costs and budgets Quality Operational 4 Customers Internal External Stakeholder management 5 Communication Planning Meetings Reporting Emails Notices Networking Walking the talk 6 Care People Safety, Health, Environment & Security Assets 7 Continuous improvement Process Product Proactivity Link to KPIs and Year Plan 8 Putting it all together Action planning for the first (or next) 100 days Conclusions

Duration 5 Days 30 CPD hours This course is intended for New users of HP OperationsManagement (OMi) 9.21, including:? IT Tools engineers? Operations staff? Operations managers? Availability engineers? System administrators? Network administrators Overview At the end of the course, you should be able to:? Use OMi to effectively identify, prioritize, andresolve events? Use OMi to prioritize operational activities based onan event?s impact on key business services? Create event dashboards to meet the informationneeds of specific users? Describe CI resolution and correlation? Identify the health of services and technologycomponents based on Health Indicators (HIs) andKey Performance Indicators (KPIs) presented in OMi? Create and use OMi performance graphs? Create and use OMi tools? Create and tune Topology Based Event Correlation(TBEC) correlation rules? Create and tune Stream Based Event Correlation(SBEC) correlation rules? Create and tune Time Based Event Automation(TBEA) automation rules? Create and tune event suppression rules? Manage OMi user access and permissions? Create and tune OMi notifications? Describe the deployment architecture and options? Configure integration between OMi and HPOperations Management (HPOM)? Configure integration between OMi and HPSiteScope? Configure integration between OMi and NP EndUser Management (EUM) This course is recommended for individuals who are responsible for designing, implementing, or administering effective service operations capabilities for mission-critical business services. This course is recommended for individuals who are responsible for designing, implementing, or administering effective service operations capabilities for mission-critical business services. Additional course details: Nexus Humans OMI120 - Operations Manager i Software 9.x Essentials 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 OMI120 - Operations Manager i Software 9.x Essentials 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 1.875 Days 11.25 CPD hours This course is intended for Allied Health Professionals. No prerequisites required Overview Upon completion of this course, students will - Learn the structure of the system and how to use the ICD-10-CM. - Learn how to map ICD-9-CM and ICD-10-CM codes. CEU?s Available :AAPC 4 Units (must have a current certification) AHIMA 6 Units (must have a current certification) - Become familiar with outpatient coding and reporting guidelines The focus of this class is learning the coding rules for the ICD-10-CM coding system and then applying the rules to code patient services. An Overview of ICD-10-CMUsing ICD-10-CMICD-10-CM Outpatient Coding and Reporting GuidelinesChapter Specific Guidelines Chapters 1-10Chapter Specific Guidelines Chapters 11-14Chapter Specific Guidelines Chapters 15-21

Duration 5 Days 30 CPD hours This course is intended for This course is designed for: IT Professionals in the BC/DR or system administration domain, business continuity and disaster recovery consultants, individuals wanting to establish themselves in the field of IT business, continuity and disaster recovery, IT risk managers and consultants, and CISOs and IT directors. Before taking this course, some experience in the IT BC/DR domain is recommended. More info can be found here: https://www.eccouncil.org/wp-content/uploads/2017/05/edrpv3-brochure.pdf Overview EC-Council Disaster Recovery Professional (EDRP) is a comprehensive professional course that teaches students how to develop enterprise-wide business continuity and disaster recovery plans. EDRP provides the professionals with a strong understanding of business continuity and disaster recovery principles, including conducting business impact analysis, assessing of risks, developing policies and procedures, and implementing a plan. EDRP teaches professionals how to secure data by putting policies and procedures in place, and how to recover and restore their organization's critical data in the aftermath of a disaster. EDRP provides the professionals with a strong understanding of business continuity and disaster recovery principles, including conducting business impact analysis, assessing of risks, developing policies and procedures, and implementing a plan. It also teaches professionals how to secure data by putting policies and procedures in place, and how to recover and restore their organization?s critical data in the aftermath of a disaster. The program is designed to provide much needed step-by-step guidance to attendees and then tests their knowledge through case studies. EDRPv3 addresses gaps in other BC/DR programs by providing helpful templates that are applied to BC/DR efforts in an enterprise. Course Outline Introduction to Disaster Recovery and Business Continuity Business Continuity Management (BCM) Risk Assessment Business Impact Analysis (BIA) Business Continuity Planning (BCP) Disaster Recovery Planning Process Data Backup Strategies Data Recovery Strategies Virtualization-Based Disaster Recovery System Recovery Centralized and Decentralized System Recovery BCP Testing, Maintenance, and Training Additional course details: Nexus Humans EC-Council Disaster Recovery Professional (EDRP) 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 EC-Council Disaster Recovery Professional (EDRP) 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.

Build successful and effective multi-cultural teams with our practical, bespoke training courses. Help team members to embrace and harness the skills and abilities their different ages, nationalities, generations and life experiences bring. Courses include: Knowing your team Communication styles Communicative competency in multi-cultural teams Cultural intelligence – understanding our strengths A global mindset Breaking down barriers for better team working Experiential learning – a session in a second language Team dynamics

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.