776 Observation courses

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.

Our Bowel Care training will enable learners to deliver effective and thorough support to individuals who have difficulty managing their bowel habits due to immobility or illness. Bowel Care training is aimed at support workers with no experience of bowel care and can also be attended by nurses who may like to update their knowledge.

The Advanced Driving Course is designed to promote the principles of road safety and of enhanced driving methods. Training is delivered in cars or vans and encourages drivers to achieve high driving standards. This course is relevant to any driver wishing to hone their driving skills. There are many benefits; being a safer driver is just one aspect.

Product Overview: Clinical Psychology Online Course Are you passionate about understanding human behavior, diagnosing psychological disorders, and helping individuals lead fulfilling lives? Embark on a transformative journey with our comprehensive Clinical Psychology Online Course. Developed by industry experts and seasoned practitioners, this course equips you with the knowledge, skills, and insights necessary to thrive in the field of clinical psychology. Module 1: Introduction to Clinical Psychology In Module 1, you will delve into the foundations of clinical psychology, exploring its history, theories, and key concepts. Gain an understanding of the role of clinical psychologists in various settings and learn about ethical considerations and professional standards in the field. Module 2: Assessment and Diagnosis Module 2 focuses on the critical aspects of assessment and diagnosis in clinical psychology. Discover a range of assessment tools and techniques used to evaluate mental health issues and develop proficiency in formulating accurate diagnoses based on empirical evidence and clinical observations. Module 3: Treatment Modalities Explore the diverse array of treatment modalities available in clinical psychology in Module 3. From traditional psychotherapy approaches to contemporary interventions such as cognitive-behavioral therapy (CBT) and mindfulness-based therapies, this module provides a comprehensive overview of effective treatment strategies for various psychological disorders. Module 4: Psychopathology and Personality Disorders Module 4 delves deep into the intricate nature of psychopathology and personality disorders. Gain insights into the etiology, symptomatology, and diagnostic criteria of common mental health disorders, including mood disorders, anxiety disorders, and personality disorders. Explore the latest research findings and evidence-based interventions for managing these complex conditions. Module 5: Child and Adolescent Psychology In Module 5, you will focus on the unique challenges and considerations involved in working with children and adolescents in a clinical setting. Explore developmental theories, behavioral assessment techniques, and therapeutic approaches tailored to address the specific needs of young clients. Learn how to effectively collaborate with families and other professionals to promote positive outcomes for children and adolescents struggling with mental health issues. Key Features: Comprehensive curriculum covering essential topics in clinical psychology. Engaging multimedia content, including video lectures, case studies, and interactive simulations. Self-paced learning to accommodate diverse schedules and learning styles. Access to experienced instructors and peer support through online forums and discussions. Practical assignments and assessments to reinforce learning and assess proficiency. Certificate of completion upon successfully finishing the course, enhancing your credentials in the field of clinical psychology. Whether you're a psychology student seeking to expand your knowledge or a practicing professional looking to sharpen your skills, our Clinical Psychology Online Course offers a dynamic learning experience that will empower you to make a meaningful difference in the lives of others. Join us on this transformative journey and unlock your potential as a compassionate and competent clinical psychologist. Course Curriculum Module 1_ Introduction to Clinical Psychology. Introduction to Clinical Psychology. 00:00 Module 2_ Assessment and Diagnosis. Assessment and Diagnosis. 00:00 Module 3_ Treatment Modalities. Treatment Modalities. 00:00 Module 4_ Psychopathology and Personality Disorders. Psychopathology and Personality Disorders. 00:00 Module 5_ Child and Adolescent Psychology. Child and Adolescent Psychology. 00:00

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.