46 Observation courses in Nottingham delivered Live Online

Welcome to GLA Tutors, a pioneering platform for primary school tutoring and SATs preparation.  At GLA Tutors, we understand the importance of a strong foundation in primary school subjects and strive to provide comprehensive support to help children excel in their academic journey. Our team of expert tutors is well-versed in the English National Curriculum, which forms the basis of primary school education in the UK. We have carefully analysed the curriculum requirements to ensure that our tutoring services cover all key subjects and align with the learning objectives set by the curriculum. Our provision for tutoring all primary school subjects encompasses a wide range of subjects, including: 1. English: - Reading comprehension - Vocabulary development - Grammar and punctuation - Writing skills 2. Mathematics: - Number and place value - Addition, subtraction, multiplication, and division - Fractions, decimals, and percentages - Geometry and measurement 3. Science: - Investigating scientific phenomena - Understanding the natural world - Conducting experiments and making observations - Developing scientific thinking and inquiry skills 4. History: - Understanding historical events and timelines - Exploring significant figures and civilizations - Analysing primary and secondary sources - Developing historical knowledge and critical thinking skills 5. Geography: - Studying different landscapes and environments - Investigating human and physical geography - Exploring global issues and sustainability - Developing geographical skills and understanding At GLA Tutors, we understand that preparing children for SATs can be a challenging task. Our tutors are well-versed in the SATs format and content, and they provide targeted support to help children excel in their exams. We cover all areas of the SATs, including English (reading, grammar, and writing) and Mathematics, ensuring that children are well-prepared and confident on exam day. Our tutoring sessions are designed to be engaging and interactive, fostering a love for learning and encouraging children to reach their full potential. We use a variety of teaching techniques, including hands-on activities, visual aids, and interactive resources, to make learning enjoyable and effective. With GLA Tutors, you can be assured that your child will receive top-quality tutoring in all primary school subjects and be well-prepared for SATs. Our tutors are committed to providing personalised support, tailoring their teaching methods to meet the unique learning needs of each child. Join us and let us help your child thrive academically and achieve success in their primary school journey and SATs.

Discuss past and present attitudes/experience around palliative care Holistic palliative care Discuss considerations and difficult conversat About this event Discuss past and present attitudes/experience around palliative care Holistic palliative care Discuss considerations and difficult conversations Accountability Advantages and disadvantages of using syringe driver Equipment required Documentation and Labelling syringes Monitoring patient checks Commonly used drugs Syringes Sites to use and avoid Use of drugs beyond licence Commonly used drugs Troubleshooting Give a practical demonstration of how to set up a syringe pump Practical session setting up a McKinley T34 syringe pump – you will need to the unit if an alternative machine is required.

Blue CSCS Card NVQ Level 2 Plant This qualification provides you with the opportunity to showcase their knowledge, skills and understanding in their chosen specialism. You will have the relevant experience in one of the specific areas. You will be operating on one of the following machines: Forward Tipping Dumper Ride on Roller Excavator Telehandler Induction As soon as you register you will be given a dedicated assessor. They will arrange an induction and together with your assessor, you will get to decide on the pathway which best proves your competency. The induction is used to plan out how you will gather the relevant evidence to complete the course. During the course The assessor will work with you to build a portfolio of evidence that allows you to showcase your knowledge, skills and experience. The assessor will also regularly review and provide you with feedback. This will allow you to keep on track to progress quickly. You will be assessed through various methods such as observations, written questions, evidence generated from the workplace, professional discussion, and witness testimonials. On completion Once all feedback has been agreed, the Internal Quality Assurer will review your portfolio and in agreement with your assessor the certificate will be applied for. To download our PDF for this course then please click here.

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

To explore the factors which affect and influence feacal continence when supporting individuals in order to effectively manage bowel incontinence.

The course seeks to improve the wellbeing and experience of people with dementia and of the care staff working with them. It should improve your confidence in managing situations you find challenging.