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Teachers will become familiar with the software, aiding each learner access to their college dashboard as a designer, so they can complete a heat loss report and other heating design elements. Furthermore each learner will have access to send surveys from the heat engineer app (Apple or Android) which once sent will be received within the college dashboard. Where teachers can assess the survey.

A thorough account of the UK and European legal framework and its requirements as regards managing environmental performance. This course will help staff to understand: * The framework of UK and European legislation and its enforcement * The principal features of the legislation as they apply to your organisation's activity/product/service * The benefit of having an Environmental Management System such as ISO 14001 * How their own actions and decisions can either expose or protect the organisation in relation to its legal obligations 1 INTRODUCTION AND OBJECTIVES 2 INTRODUCTION TO ENVIRONMENTAL LAW AND ENFORCEMENT * Sources of law (European and UK) * Structure and enforcement * Key legislation 3 INTEGRATED POLLUTION PREVENTION AND CONTROL (IPPC) AND LOCAL AIR POLLUTION AND CONTROL (LAPC) * Pollution and Prevention Control Act 1999 * EC Directives on PPC * The meaning of BAT * Transitional provisions * Fit and proper persons * Control of emissions to air * National Air Quality Strategy 4 PACKAGING AND PRODUCER RESPONSIBILITIES * Who, what and how * The Producer Responsibility Obligations (Packaging Waste) Regulations * Obligations and exemptions * Registration * Recycling and recovery obligations * Records * Duties of the Environment Agency * Offences * Developments 5 WASTE MANAGEMENT * National Waste Strategy * Waste minimisation (re-use/recycling) * Waste definition * Disposal and recovery * Controlled waste management * Hazardous waste management 6 PROPOSED LEGISLATION AND EC DIRECTIVES * EU Commission's waste and resources strategies * Implementation of ELV (End of Life Vehicles) Directive * WEEE (Waste Electrical and Electronic Equipment) Directive transposition into UK legislation * Other producer responsibility initiatives * Other proposals from the EU 7 CONCLUSION * Open forum * Summary * Close

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice ADDRESSING BUSINESS ISSUES WITH DATA SCIENCE * Topic A: Initiate a Data Science Project * Topic B: Formulate a Data Science Problem EXTRACTING, TRANSFORMING, AND LOADING DATA * Topic A: Extract Data * Topic B: Transform Data * Topic C: Load Data ANALYZING DATA * Topic A: Examine Data * Topic B: Explore the Underlying Distribution of Data * Topic C: Use Visualizations to Analyze Data * Topic D: Preprocess Data DESIGNING A MACHINE LEARNING APPROACH * Topic A: Identify Machine Learning Concepts * Topic B: Test a Hypothesis DEVELOPING CLASSIFICATION MODELS * Topic A: Train and Tune Classification Models * Topic B: Evaluate Classification Models DEVELOPING REGRESSION MODELS * Topic A: Train and Tune Regression Models * Topic B: Evaluate Regression Models DEVELOPING CLUSTERING MODELS * Topic A: Train and Tune Clustering Models * Topic B: Evaluate Clustering Models FINALIZING A DATA SCIENCE PROJECT * Topic A: Communicate Results to Stakeholders * Topic B: Demonstrate Models in a Web App * Topic C: Implement and Test Production Pipelines

Duration 2 Days 12 CPD hours This course is intended for New users of IBM SPSS Statistics Users who want to refresh their knowledge about IBM SPSS Statistics Anyone who is considering purchasing IBM SPSS Statistics Overview Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables. INTRODUCTION TO IBM SPSS STATISTICS * Review basic concepts in IBM SPSS Statistics * Identify the steps in the research process * Review basic analyses USE HELP READING DATA AND DEFINING METADATA * Overview of data sources * Read from text files * Read data from Microsoft Excel * Read data from databases DEFINE VARIABLE PROPERTIES SELECTING CASES FOR ANALYSES * Select cases for analyses * Run analyses for groups * Apply report authoring styles Transforming variables Compute variables * Recode values of categorical and scale variables CREATE A NUMERIC VARIABLE FROM A STRING VARIABLE USING FUNCTIONS TO TRANSFORM VARIABLES * Use statistical functions * Use logical functions * Use missing value functions * Use conversion functions * Use system variables * Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases CREATE AGGREGATE DATASETS * Restructure datasets Merging data files * Add cases from one dataset to another * Add variables from one dataset to another * Enrich a dataset with aggregated information Summarizing individual variables * Define levels of measurement * Summarizing categorical variables Summarizing scale variables Describing the relationship between variables * Choose the appropriate procedure Summarize the relationship between categorical variables * Summarize the relationship between a scale and a categorical variable Creating presentation * ready tables with Custom Tables IDENTIFY TABLE LAYOUTS * Create tables for variables with shared categories * Create tables for multiple response questions Customizing pivot tables * Perform Automated Output Modification * Customize pivot tables * Use table templates EXPORT PIVOT TABLES TO OTHER APPLICATIONS WORKING WITH SYNTAX * Use syntax to automate analyses * Create, edit, and run syntax * Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment * Set options for output * Set options for variables display Set options for default working folders ADDITIONAL COURSE DETAILS: Nexus Humans 0G53BG IBM SPSS Statistics Essentials (V26) 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 0G53BG IBM SPSS Statistics Essentials (V26) 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 5 Days 30 CPD hours This course is intended for Experienced system administrators and system integrators Consultants responsible for designing, implementing, and customizing vRealize Operations Overview By the end of the course, you should be able to meet the following objectives: List the vRealize Operations use cases Identify features and benefits of vRealize Operations Determine the vRealize Operations cluster that meets your monitoring requirements Deploy and configure a vRealize Operations cluster Use interface features to assess and troubleshoot operational problems Describe vRealize Operations certificates Create policies to meet the operational needs of your environment Recognize effective ways to optimize performance, capacity, and cost in data centers Troubleshoot and manage problems using workbench, alerts, and predefined dashboards Manage configurations Configure application monitoring using VMware vRealize Operations Cloud Appliance™ Create custom symptoms and alert definitions, reports, and views Create various custom dashboards using the dashboard creation canvas Configure widgets and widget interactions for dashboards Create super metrics Set up users and user groups for controlled access to your environment Extend the capabilities of vRealize Operations by adding management packs and configuring solutions Monitor the health of the vRealize Operations cluster by using self-monitoring dashboards This course provides you with the knowledge and skills to deploy a VMware vRealize Operations cluster that meets the monitoring requirements of your environment.This course includes advanced capabilities such as customizing alerts, views, reports, and dashboards and explains the deployment and architecture in vRealize Operations. This course explains application monitoring, certificates, policies, capacity and cost concepts, and workload optimization with real-world use cases. This course covers troubleshooting using the workbench, alerts, and predefined dashboards, and how to manage compliance and configurations. This course also covers several management packs. COURSE INTRODUCTION * Introduction and course logistics * Course objectives INTRODUCTION TO VREALIZE OPERATIONS * List the vRealize Operations use cases * Access the vRealize Operations User Interface (UI) VREALIZE OPERATIONS ARCHITECTURE * Identify the functions of components in a vRealize Operations node * Identify the types of nodes and their role in a vRealize Operations cluster * Outline how high availability is achieved in vRealize Operations * List the components required to enable Continuous Availability (CA) DEPLOYING VREALIZE OPERATIONS * Design and size a vRealize Operations cluster * Deploy a vRealize Operations node * Install a vRealize Operations instance * Describe different vRealize Operations deployment scenarios VREALIZE OPERATIONS CONCEPTS * Identify product UI components * Create and use tags to group objects * Use a custom group to group objects VREALIZE OPERATIONS POLICIES AND CERTIFICATE MANAGEMENT * Describe vRealize Operations certificates * Create policies for various types of workloads * Explain how policy inheritance works CAPACITY OPTIMIZATION * Define capacity planning terms * Explain capacity planning models * Assess the overall capacity of a data center and identify optimization recommendations WHAT-IF SCENARIOS AND COSTING IN VREALIZE OPERATIONS * Run what-if scenarios for adding workloads to a data center * Discuss the types of cost drivers in vRealize Operations * Assess the cost of your data center inventory PERFORMANCE OPTIMIZATION * Introduction to performance optimization * Define the business and operational intentions for a data center * Automate the process of optimizing and balancing workloads in data centers * Report the results of optimization potential TROUBLESHOOTING AND MANAGING CONFIGURATIONS * Describe the troubleshooting workbench * Recognize how to troubleshoot problems by monitoring alerts * Use step-by-step workflows to troubleshoot different vSphere objects * Assess your environment?s compliance to standards * View the configurations of vSphere objects in your environment OPERATING SYSTEM AND APPLICATION MONITORING * Describe native service discovery and application monitoring features * Configure application monitoring * Monitor operating systems and applications by using VMware vRealize© Operations Cloud Appliance? CUSTOM ALERTS * Create symptom definitions * Create recommendations, actions, and notifications * Create alert definitions that monitor resource demand in hosts and VMs * Build and use custom views in your environment CUSTOM VIEWS AND REPORTS * Build and use custom views in your environment * Create custom reports for presenting data about your environment CUSTOM DASHBOARDS * Create dashboards that use predefined and custom widgets * Configure widgets to interact with other widgets and other dashboards * Configure the Scoreboard widget to use a metric configuration file * Manage dashboards by grouping dashboards and sharing dashboards with users SUPER METRICS * Recognize different types of super metrics * Create super metrics and associate them with objects * Enable super metrics in policies USER ACCESS CONTROL * Recognize how users are authorized to access objects * Determine privilege priorities when a user has multiple privileges * Import users and user groups from an LDAP source EXTENDING AND MANAGING A VREALIZE OPERATIONS DEPLOYMENT * Identify available management packs in the VMware Marketplace? * Monitor the health of a vRealize Operations cluster * Generate a support bundle * View vRealize Operations logs and audit reports * Perform vRealize Operations cluster management tasks ADDITIONAL COURSE DETAILS: Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware vRealize Operations: Install, Configure, Manage [V8.6] 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 VMware vRealize Operations: Install, Configure, Manage [V8.6] 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 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.

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 0.5 Days 3 CPD hours This course is intended for This course is designed for business leaders and decision makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science. Other individuals who want to know more about basic data science concepts are also candidates for this course. This course is also designed to assist learners in preparing for the CertNexus DSBIZ™ (Exam DSZ-110) credential. Overview In this course, you will identify how data science supports business decisions. You will: Explain the fundamentals of data science Describe common implementations of data science. Identify the impact data science can have on a business The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business. DATA SCIENCE FUNDAMENTALS * What is Data Science? * Types of Data * Data Science Roles DATA SCIENCE IMPLEMENTATION * The Data Science Lifecycle * Data Acquisition and Preparation * Data Modeling and Visualization THE IMPACT OF DATA SCIENCE * Benefits of Data Science * Challenges of Data Science * Business Use Cases for Data Science ADDITIONAL COURSE DETAILS: Nexus Humans CertNexus Data Science for Business Professionals (DSBIZ) 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 CertNexus Data Science for Business Professionals (DSBIZ) 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 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 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.