115 Scientist courses in Grays delivered Live Online

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Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Explore the power of your mindset to affect mood, motivation and happiness + practical ways to bring more positivity to your life.

Duration 3 Days 18 CPD hours This course is intended for Application Developer Data Modeler BI / BW Consultant Data Scientist Database Administrator SAP HANA Support Personnel Overview This course will prepare you to: Push down data intensive tasks to the SAP HANA database using SQL and SQLScript Understand how to code using advanced SQLScript techniques Follow recommended practices for writing optimal SQL and SQLScript Debug and analyze SQL and SQLScript code This course provides students with basic SQL knowledge or refreshes their SQL knowledge, and enables them to use basic and some advanced SQL techniques for querying and manipulating data in an SAP HANA database. Getting Started What is SQL and SQLScript? Understanding how ABAP developers work with SQLScript Understanding XS Advanced and HDI Working with Web IDE for SAP HANA Understanding the course data SQL Logic Container Creating user-defined functions Creating database procedures Trapping errors in SQLScript User defined libraries Declarative Logic Using declarative logic Imperative Logic Using imperative Logic Transactional Savepoints How to implement transactional savepoints Analytic Operations Using OLAP Analytic features Implementing Temporal Tables Working with Hierarchies Working with Hierarchies Troubleshooting and Best Practices Tools for troubleshooting Best Practices Appendix Starting from the beginning with SQL fundamentals Additional course details: Nexus Humans HA150 SAP HANA 2.0 SPS05 SQLScript for SAP HANA 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 HA150 SAP HANA 2.0 SPS05 SQLScript for SAP HANA 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.

Maximize the value of data assets in the oil and gas sector with EnergyEdge's assessment-based training course on Python programming and analytics.

Introduction to AI for Business

Aligned with the AIGP certification program, AI Governance Professional Training is for professionals tasked with implementing AI governance and risk management in their organizations. It provides baseline knowledge and strategies for responding to complex risks associated with the evolving AI landscape. This training meets the rapidly growing need for professionals who can develop, integrate and deploy trustworthy AI systems in line with emerging laws and policies. About This Course This training teaches critical artificial intelligence governance concepts that are also integral to the AIGP certification exam. While not purely a 'test prep' course, this training is appropriate for professionals who plan to certify, as well as for those who want to deepen their AI governance knowledge. Both the training and the exam are based on the same body of knowledge.   Module 1: Foundations of artificial intelligence Defines AI and machine learning, presents an overview of the different types of AI systems and their use cases, and positions AI models in the broader socio-cultural context. Module 2: AI impacts on people and responsible AI principles Outlines the core risks and harms posed by AI systems, the characteristics of trustworthy AI systems, and the principles essential to responsible and ethical AI. Module 3: AI development life cycle Describes the AI development life cycle and the broad context in which AI risks are managed. Module 4: Implementing responsible AI governance and risk management Explains how major AI stakeholders collaborate in a layered approach to manage AI risks while acknowledging AI systems' potential societal benefits. Module 5: Implementing AI projects and systems Outlines mapping, planning and scoping AI projects, testing and validating AI systems during development, and managing and monitoring AI systems after deployment. Module 6: Current laws that apply to AI systems Surveys the existing laws that govern the use of AI, outlines key GDPR intersections, and provides awareness of liability reform. Module 7: Existing and emerging AI laws and standards Describes global AI-specific laws and the major frameworks and standards that exemplify how AI systems can be responsibly governed. Module 8: Ongoing AI issues and concerns Presents current discussions and ideas about AI governance, including awareness of legal issues, user concerns, and AI auditing and accountability issues. Accreditation The associated exam is accredited by the IAPP under its ANSI Accreditation Who Should Attend? Any professionals tasked with developing AI governance and risk management in their operations, and anyone pursuing IAPP Artificial Intelligence Governance Professional certification. Prerequisites A general understanding of AI, Corporate Governance, and Business value would be of benefit to participants. Assessment As with all IAPP exams, the AIGP is a 90 question, multiple choice exam to be completed within 150 minutes. Exams are hosted by Pearsonvue and can be taken either remotely, or via any one of hundreds of exam venues globally. A passing score is achieved at 70% Our Guarantee We are an approved IAPP training provider Exam pass guarantee, or retrain until you do, for free What's Included? Participant Guide Study Guide Practice Exam Exam voucher Breakfast, lunch, coffees and snacks (Classroom courses only) Certification Logo

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

This module aims to develop knowledge from research activities to gain an understanding of international trade using Marketing , Social Media and how AI plays a role in International Marketing

Course Information This highly interactive course will provide guidance on why and how to implement a quality system successfully into the research laboratory. By doing so, you will position your innovation for the success it deserves. But leave things as they are and there is a good chance that your science will not realise its full potential should success, and its consequences, come your way. A quality system in your research laboratory is the most effective and efficient way to: Help scientists work more efficiently Ensure discoveries can be defended Protect the value of intellectual property. This course is particularly aimed at those working in early phase research environments which are not constrained by the regulatory requirements of the Good Practice regulations but are producing intellectual property, testing and/or products for the therapeutic market. For organisational reasons, rather than regulatory ones, this is a place where you need to get it right. The programme is delivered by leaders in the field who, quite simply, ‘have done it’. Whether delegates are at senior management level seeking strategic direction, a laboratory head wishing to deliver science that will stand the test of time or a quality professional thrown in at the deep end, this course will provide key insight and practical guidance to underpin future success. Based on risk based systems, tried and tested over many years in the workplace, the programme will help delegates to define, train, implement and monitor the quality of their research, irrespective of field or discipline. Delegates will learn how to help position their organisation for success. Course content: Delegates will be guided thoughtfully through each key component of the process in a stimulating learning environment. The course probes all avenues of the research quality arena, from an initial understanding of the cultural aspects of the scientific discovery environment, to managing quality in outsourced research programmes. Computer systems and e-data security in the research environment will be discussed and pragmatic solutions described to help manage the ballooning cloud of e-data. In addition, the ever blurring boundary between the regulated and non-regulated research environments will be discussed and delegates given perspective on future developments in the field. With this knowledge, delegates will be able to get it ‘right first time’. Is this course for you? The course is designed for all those involved in the research laboratory quality arena and it has been tailored to meet the needs of scientific management, bench scientists and quality professionals alike. Delegates get immediate access to highly experienced tutors who will share their wisdom and insights in an area where few others have been successful. The course is linked with the RQA guidance which builds on years of experience and forms the foundation of the programme. Tutors Tutors will be comprised of (click the photos for biographies): Louise Handy Director, Handy Consulting Ltd Sandrine Bongiovanni Associate Director in Research and Quality Compliance, Novartis Programme Please note timings may be subject to alteration. Day 1 09:00 Registration 09:10 Welcome and Introductions 09:20 History and Overview of the Field Examples of business and regulatory risks and the consequences of low quality in research. A look at the standards and guidelines that exist. 10:00 The Culture, the Politics and the Scientist's Perspective Understanding research environments, the drivers and the challenges. 10:30 Break 10:45 Workshop - Risk Management Thinking about risk management and prioritisation. Looking at the critical factors for the implementations of a successful quality system. 12:15 Workshop - Feedback 12:45 Lunch 13:45 Personnel, Plans, Procedures, Facilities, Equipment, Materials and Reagents Looking at planning the work, defining procedures in a way which promotes robust science without compromising brilliance and ensuring that all these elements are demonstrably fit for their intended purpose. 14:30 Workshop - Assay Validation How much validation is required at what stage? What do we need to validate an assay? 15:00 Workshop - Feedback 15:15 Research, Work Records, Archives and Research Review Data and records which are accurate, attributable, legally attestable and safe to permit reconstruction experiments and studies. Looking at aspects of the work where there is a chance to review, correct or improve the science, the data and the processes. 16:15 Continual Improvement and Quality Systems Reviewing implementation of a quality system, finding opportunities for improvement, understanding culture change. 16:45 Questions and Answers 17:00 Close of Course Extra Information Course Material This course will be run completely online. You will receive an email with a link to our online system, which will house your licensed course materials and access to the remote event. Please note this course will run in UK timezone. The advantages of this include:   Ability for delegates to keep material on a mobile device Ability to review material at any time pre and post course Environmental benefits – less paper being used per course Access to an online course group to enhance networking. You will need a stable internet connection, a microphone and a webcam. CPD Points 7 Points   Development Level Develop