692 Courses

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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 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

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.

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization. Overview After completing this course delegates will be capable of writing effective R code to manipulate, analyse and visualise data to enable their organisations make better, data-driven decisions. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Course Outline Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R programming language is one of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. The course will explore the following topics through a series of interactive workshop sessions: What is R? Basic R programming conventions Data structures in R Accessing data in R Descriptive statistics in R Statistical analysis in R Data manipulation in R Data visualisation in R Additional course details: Nexus Humans Beginning Data Analytics With R 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 Beginning Data Analytics With R 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 This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Level 3 Award in Education and Training | Previously PTLLS Level 3 Award in Education and Training - AET, previously PTLLS, is the mandatory minimum qualification if you want to be a FE teacher or trainer in your chosen field in the UK. If you are thinking about getting into teaching, this Level 3 Award in Education and Training (AET), previously known as the PTLLS course, is a great way to start. As a teacher, you can play a significant role in society and positively impact your students' lives. Currently, there is a huge demand for teaching jobs across the globe, and you know how satisfying it could be! So, if you aspire to be a changemaker, this is the ultimate course we can offer you. This course is designed so that both freshers and those working in an educational setting can get the benefit. Further, through this course, the current teachers can achieve formal recognition of their skills. Awarding Body The Level 3 Award in Education and Training course is accredited by TQUK. A certificate from this renowned awarding body will bring you out as a highly marketable candidate within the desired industry. There is Something More for You with This Level 3 Award in Education and Training | Previously PTLLS Course As a course provider, we highly value our learners' needs. That is why we are giving you some relevant courses for teaching and training for free to benefit you. With these courses, you can further enrich your knowledge and employability skills. Here are those - Diploma in Special Education Needs (SEN) EYFS Teaching Diploma Early Years Level 4 Primary Teaching Diploma Level 2 Diploma for the Early Years Practitioner Learning Outcomes of the Level 3 Award in Education and Training | Previously PTLLS Course By the end of this course, you will be able to: Identify and perform the roles of a teacher. Explain and apply the teaching and learning approaches. Plan a training session for your learners of different backgrounds. Describe the teachers' attributes and expectations. Prepare inclusive lessons and training sessions for your students. Analyse the role, responsibilities and relationships in education and training. Prepare and administer necessary assessments for your students. Why Choose Level 3 Award in Education and Training | Previously PTLLS from Us Self-paced course, access available from anywhere. Easy to understand, high-quality study materials. Dedicated tutor support during office hour (Monday to Friday) PTLLS Course developed by industry experts. 24/7 support via live chat, phone call or email. Free PDF certificate as soon as completing the Gift Courses. Special Note: Please be informed that apart from the initial fee, you have to pay GBP 169 when submitting assignments. Level 3 Award in Education and Training | Previously PTLLS Course Curriculum PTLLS : Module 01: Understanding Roles, Responsibilities and Relationships in Education and Training Understand the Teaching Role and Responsibilities in Education and Training Understand Ways to Maintain a Safe and Supportive Learning Environment Understand the Relationships between Teachers and Other Professionals in Education and Training PTLLS : Module 02: Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Understand Inclusive Teaching and Learning Approaches in Education and Training Understand Ways to Create an Inclusive Teaching and Learning Environment Be Able to Plan Inclusive Teaching and Learning Be Able to Deliver Inclusive Teaching and Learning Be Able to Evaluate the Delivery of Inclusive Teaching and Learning PTLLS : Module 03: Understanding Assessment Understand Types and Methods of Assessment Used in Education and Training Understand How to Involve Learners and Others in the Assessment Process Understand the Role and Use of Constructive Feedback in the Assessment Process Understand Requirements for Keeping Records of Assessment in Education and Training -------------------------------------------------------- ** [Free Courses] ** [Course 02 ] ** Level 2 Diploma for the Early Years Practitioner ** Module 1: Roles and Responsibilities of the Early Years Practitioner Module 2: Health and Safety of Babies and Young Children in the Early Years Module 3: Equality, Diversity and Inclusive Practice in Early Years Settings Module 4: Safeguarding, Protection and Welfare of Babies and Young Children in Early Years Settings Module 5: Understand How to Support Children's Development Module 6: Support Care Routines for Babies and Young Children Module 7: Support the Planning and Delivery of Activities, Purposeful Play Opportunities and Educational Programmes Module 8: Promote Play in an Early Years Setting Module 9: Support Well-being of Babies and Young Children for Healthy Lifestyles Module 10: Support Babies and Young Children to be Physically Active Module 11: Support the Needs of Babies and Young Children with Special Educational Needs and Disability Module 12: Promote Positive Behaviour in Early Years Settings Module 13: Partnership Working in the Early Years Module 14: Support Children's Transition to School [Course 03 ] ** Primary Teaching Diploma ** Module 01: Primary Education in the UK Module 02: Responsibilities, Qualifications and Skills Module 03: Initial Teacher Training Module 04: Types of Schools in the UK Module 05: Importance of Early Years in Development Module 06: EYFS Teaching Techniques Module 07: Teaching Primary English Module 08: Teaching Primary Mathematicsv Module 09: Teaching Primary Science, Design and Technology Module 10: Teaching Other Primary Subjects Module 11: Children Having Special Education Needs or Disabilities Module 12: EYFS Framework in 2021 Module 13: Self-Management and Dealing with Stress [Course 04] ** Early Years Level 4 ** Module 1: Supporting the Emotional and Physical Well-being of Children Module 2: Equality, Diversity and Professional Partnerships Module 3: Health and Safety Legislation Module 4: Observations, Assessments and Planning Module 5: Safeguarding Children Module 6: The Early Years Foundation Stage Module 7: Managing in a Nursery Environment Module 8: Engaging in Professional Development [Course 05 ] ** EYFS Teaching Diploma ** Module 1: An Overview of EYFS Module 2: Significance of Early Years in Development Module 3: Teaching Techniques Focusing EYFS Module 4: Curriculum for EYFS Teaching Module 5: EYFS Teaching Career Module 6: Fostering Learning and Development Module 7: Children Having Special Education Needs or Disabilities Module 8: Guiding Parents to the Early Years Foundation Stage Module 9: Safety Requirements Module 10: The EYFS Educational Philosophies and Privileges Module 11: The Process of Registration, Inspection, and Quality Improvement Module 12: EYFS Framework in 2021 Module 13: Finding Work as an EYFS Teacher [ Course 06 ] ** Diploma in Special Education Needs (SEN) ** Module 1: Introduction Module 2: A Quick Overview of the New SEND Code of Practice Module 3: Legislation Related to SEN Module 4: SEN Assessments and Descriptors Module 5: Education for Children with Disabilities Module 6: Common SEN Conditions Found Within Mainstream Schools Module 7: Assessment and Planning for Children with Special Educational Needs Module 8: The Broader Children's Workforce Associated with Special Education Needs Provisions Module 9: Working with Other Professionals and Other Sources of Support and Information ------------------------------------------------------- Level 3 Award in Education and Training | Previously PTLLS Qualification at a Glance Total Qualification Time (TQT - 120 Hours Guided Learning Hours (GLH) - 48 Hours Credit Value - 12 Minimum Age - 19 Purpose of the Level 3 Award in Education and Training | Previously PTLLS Qualification This Level 3 Award in Education and Training | Previously PTLLS qualification is designed to prepare learners for employment and support them to progress to a teaching role within the workplace. It has been developed for those intending to work in the post-16 education and training sector. What Skills You Will Gain from this Level 3 Award in Education and Training | Previously PTLLS Course You will gain the following marketable qualities through the Level 3 Award in Education and Training course. Different hiring managers are looking for these below-mentioned qualities. Become an expert in education and training. Learn about the teacher's role. Master the learning and teaching approaches. Increase your session planning skills. Gain knowledge in teacher expectations & attributions. Understand how to deliver inclusive sessions which engage and motivate learners. Explain roles, responsibilities, and relationships in education and training. Identify how to create assessments in education and training. Level 3 Award in Education and Training | Previously PTLLS Assessment Method In order to be eligible for the certification, you need to complete the following assessments. Three written assignments One microteaching session - 60 Minutes (Which can be submitted as a recorded video) Please note that you will be given precise instruction in the course material about writing the assignments and performing the micro-teach session. Moreover, if you face any difficulty, your tutor will be there to support you. You must submit all assignments via the online portal with full tutor support. Certification As soon as you submit your assignments and micro-teaching video, your tutor will assess those. Based on the assessor's evaluation, you will be graded either achieved/not achieved yet. If you are in the achieved category, you will receive an accredited certificate from the renowned awarding body TQUK. Progression If you achieve this Level 3 Award in Education and Training | Previously PTLLS qualification, you could progress to: Level 3 Award in Assessing Vocationally Related Achievement Level 3 Certificate in Assessing Vocational Achievement Level 3 Award in Assessing Competence in the Work Environment Level 4 Certificate in Education and Training Level 4 Certificate for the Advanced Practitioner in Schools and Colleges Level 4 Award for Technology Enabled Educators Level 5 Diploma in Education and Training Who is this course for? Wherever you work, this Level 3 Award in Education and Training | Previously PTLLS course is a must for you if you want to become a trainer. Besides, this course is also appropriate for the following. Teacher & Trainer Consultant Job Hunters & School Leavers College or University Students & Graduates Tutors & Instructors Headmasters Education Consultants Education Providers Office Clerk & Administration Assistant Educational Psychologist Lecturer or Classroom Assistant Requirements Level 3 Award in Education and Training There are no fixed entry requirements for attending the Level 3 Award in Education and Training course. However, you must be 19 years old or over upon certification. An interest in teaching adults and E-learning will be beneficial. Moreover, you must have basic ICT and time management skills. It will help you complete online written assignments. Since this is a Level 3 course, you must be ready to read through various texts, cross-reference, research theories or principles or practises of effective teaching. Finally, you need to demonstrate practical teaching skills via assessment work. Career path You can choose from a variety of professions either in teaching or training upon completion of the Level 3 Award in Education and Training | Previously PTLLS course. These include - Primary School Teacher Secondary School Teacher Further Education Teacher Private Tutor Freelance Trainer Trainer at Your Workplace

Are you bored of searching the internet for a Adult Teacher Trainer old PTLLS course? Can't manage to discover a proper course that meets all of your requirements? Don't worry, you've just discovered the solution. Take a moment to browse through this comprehensive course that includes everything you need to succeed. The Adult Teacher Trainer old PTLLS programme is intended for individuals who want to work in education, most likely as early age instructors and trainers in a variety of contexts. This is the first step in becoming an entry-level teacher in the United Kingdom. Furthermore, the Adult Teacher Trainer old PTLLS is accredited by TQUK, one of the leading awarding organisations in the UK! Adult Teacher Trainer old PTLLS is the mandatory minimum qualification if you want to be a FE teacher or trainer in your chosen field in the UK. If you are thinking about getting into teaching, this Adult Teacher Trainer old PTLLS, is a great way to start. As a teacher, you can play a significant role in society and positively impact your students' lives. Currently, there is a huge demand for teaching jobs across the globe, and you know how satisfying it could be! So, if you aspire to be a changemaker, this is the ultimate course we can offer you. This Adult Teacher Trainer old PTLLS is designed so that both freshers and those working in an educational setting can get the benefit. Further, through this course, the current teachers can achieve formal recognition of their skills. Awarding Body The Adult Teacher Trainer old PTLLS is accredited by TQUK. A certificate from this renowned awarding body will bring you out as a highly marketable candidate within the desired industry. There is Something More for You with This Adult Teacher Trainer old PTLLS Course. Although we understand, many of you only come to search for PTLLS or AET courses but still here is a small gift for learners. As a course provider, we highly value our learners' needs. That is why we are giving you some relevant courses with Adult Teacher Trainer old PTLLS Course. Because these extra teaching and training courses will benefit you. With these courses, you can further enrich your knowledge and employability skills. Here are those - Diploma in Special Education Needs (SEN) EYFS Teaching Diploma Early Years Level 4 Primary Teaching Diploma Level 2 Diploma for the Early Years Practitioner Learning Outcomes of the Adult Teacher Trainer old PTLLS Course By the end of this course, you will be able to: Identify and perform the roles of a teacher. Explain and apply the teaching and learning approaches. Plan a training session for your learners of different backgrounds. Describe the teachers' attributes and expectations. Prepare inclusive lessons and training sessions for your students. Analyse the role, responsibilities and relationships in education and training. Prepare and administer necessary assessments for your students. This Adult Teacher Trainer old PTLLS programme will be the perfect choice for any individual to kickstart a new career. Anyone who wants to pursue a career in education will find it gratifying and enjoyable. It is a good occupation since you will not only satisfy your own growth. Also it will assist many learners in achieving theirs. It is also a high-demand business, with many schools and universities looking for qualified instructors. It is a job in which you will not only teach but also learn. The Adult Teacher Trainer old PTLLS is a nationally recognised award for anybody who is presently teaching or training. Moreover, it will help those who desire to enter the field and get the necessary qualifications to work as a teacher or trainer. Why Choose Adult Teacher Trainer old PTLLS from Us Self-paced course, access available from anywhere. Easy to understand, high-quality study materials. Dedicated tutor support during office hour (Monday to Friday) Course developed by industry experts. 24/7 support via live chat, phone call or email. Free PDF certificate as soon as completing the Gift Courses. ***Main Course*** Adult Teacher Trainer old PTLLS ***Free Courses*** Course 01 : Level 2 Diploma for the Early Years Practitioner Course 02 : Primary Teaching Diploma Course 03 : Level 4 Early Years Course Course 04 : EYFS Teaching Diploma Course 05 : Diploma in Special Education Needs (SEN) ***Other Benefits of Adult Teacher Trainer old PTLLS Course*** Free 6 PDF Certificate Lifetime Access Free Retake Exam Tutor Support Special Note: Please be informed that apart from the initial fee, you have to pay GBP 169 when submitting assignments. The Adult Teacher Trainer old PTLLS fulfils - Industry requirements and will help you get a full grasp of a teacher and trainer's duties and responsibilities, The boundaries imposed with regard to teaching, and how to present motivating class sessions to encourage students. By learning this Adult Teacher Trainer old PTLLS You will learn about the role, responsibilities, and duties of a teacher. Besides, you will be able to identify the significance of your existence in your students' lives. Also, You will know how you can bring effective change to them with your influence through this Adult Teacher Trainer old PTLLS. Further, inclusive teaching and learning is a very important concept in an educational setting. That is why we have included this Adult Teacher Trainer old PTLLS in our course so that you can learn. Besides, you will get an in-depth idea about how to perform an assessment. Curriculum [Course 01 ] Adult Teacher Trainer old PTLLS Course Curriculum Module 01: Understanding Roles, Responsibilities and Relationships in Education and Training Understand the Teaching Role and Responsibilities in Education and Training Understand Ways to Maintain a Safe and Supportive Learning Environment Understand the Relationships between Teachers and Other Professionals in Education and Training Module 02: Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Understand Inclusive Teaching and Learning Approaches in Education and Training Understand Ways to Create an Inclusive Teaching and Learning Environment Be Able to Plan Inclusive Teaching and Learning Be Able to Deliver Inclusive Teaching and Learning Be Able to Evaluate the Delivery of Inclusive Teaching and Learning Module 03: Understanding Assessment Understand Types and Methods of Assessment Used in Education and Training Understand How to Involve Learners and Others in the Assessment Process Understand the Role and Use of Constructive Feedback in the Assessment Process Understand Requirements for Keeping Records of Assessment in Education and Training ----------------------------------------------- [Course 02 ] ** Level 2 Diploma for the Early Years Practitioner ** Module 1: Roles and Responsibilities of the Early Years Practitioner Module 2: Health and Safety of Babies and Young Children in the Early Years Module 3: Equality, Diversity and Inclusive Practice in Early Years Settings Module 4: Safeguarding, Protection and Welfare of Babies and Young Children in Early Years Settings Module 5: Understand How to Support Children's Development Module 6: Support Care Routines for Babies and Young Children Module 7: Support the Planning and Delivery of Activities, Purposeful Play Opportunities and Educational Programmes Module 8: Promote Play in an Early Years Setting Module 9: Support Well-being of Babies and Young Children for Healthy Lifestyles Module 10: Support Babies and Young Children to be Physically Active Module 11: Support the Needs of Babies and Young Children with Special Educational Needs and Disability Module 12: Promote Positive Behaviour in Early Years Settings Module 13: Partnership Working in the Early Years Module 14: Support Children's Transition to School --------------- [Course 03 ] ** Primary Teaching Diploma ** Module 01: Primary Education in the UK Module 02: Responsibilities, Qualifications and Skills Module 03: Initial Teacher Training Module 04: Types of Schools in the UK Module 05: Importance of Early Years in Development Module 06: EYFS Teaching Techniques Module 07: Teaching Primary English Module 08: Teaching Primary Mathematicsv Module 09: Teaching Primary Science, Design and Technology Module 10: Teaching Other Primary Subjects Module 11: Children Having Special Education Needs or Disabilities Module 12: EYFS Framework in 2021 Module 13: Self-Management and Dealing with Stress ------------- [Course 04] ** Early Years Level 4 ** Module 1: Supporting the Emotional and Physical Well-being of Children Module 2: Equality, Diversity and Professional Partnerships Module 3: Health and Safety Legislation Module 4: Observations, Assessments and Planning Module 5: Safeguarding Children Module 6: The Early Years Foundation Stage Module 7: Managing in a Nursery Environment Module 8: Engaging in Professional Development ------------------- [Course 05 ] ** EYFS Teaching Diploma ** ------------------- [ Course 06 ] ** Diploma in Special Education Needs (SEN) ** --------------- Adult Teacher Trainer old PTLLS Qualification at a Glance Total Qualification Time (TQT - 120 Hours Guided Learning Hours (GLH) - 48 Hours Credit Value - 12 Minimum Age - 19 Purpose of the Adult Teacher Trainer old PTLLS Qualification This Adult Teacher Trainer old PTLLS qualification is designed to prepare learners for employment and support them to progress to a teaching role within the workplace. It has been developed for those intending to work in the post-16 education and training sector. What Skills You Will Gain from this Course You will gain the following marketable qualities through the Adult Teacher Trainer old PTLLS course. Different hiring managers are looking for these below-mentioned qualities. Become an expert in education and training. Learn about the teacher's role. Master the learning and teaching approaches. Increase your session planning skills. Gain knowledge in teacher expectations & attributions. Understand how to deliver inclusive sessions which engage and motivate learners. Explain roles, responsibilities, and relationships in education and training. Identify how to create assessments in education and training. Adult Teacher Trainer old PTLLS Assessment Method In order to be eligible for the certification, you need to complete the following assessments. Three written assignments One microteaching session - 60 Minutes (Which can be submitted as a recorded video) Please note that you will be given precise instruction in the course material about writing the assignments and performing the micro-teach session. Moreover, if you face any difficulty, your tutor will be there to support you. You must submit all assignments via the online portal with full tutor support. Certification As soon as you submit your assignments and micro-teaching video, your tutor will assess those. Based on the assessor's evaluation, you will be graded either achieved/not achieved yet. If you are in the achieved category, you will receive an accredited certificate from the renowned awarding body TQUK. Progression If you achieve this Adult Teacher Trainer old PTLLS qualification, you could progress to: Level 3 Award in Assessing Vocationally Related Achievement Level 3 Certificate in Assessing Vocational Achievement Level 3 Award in Assessing Competence in the Work Environment Level 4 Certificate in Education and Training Level 4 Certificate for the Advanced Practitioner in Schools and Colleges Level 4 Award for Technology Enabled Educators Level 5 Diploma in Education and Training Who is this course for? Adult Teacher Trainer old PTLLS Wherever you work, this Adult Teacher Trainer old PTLLS course is a must for you if you want to become a trainer. Besides, this course is also appropriate for the following. Teacher & Trainer Consultant Job Hunters & School Leavers College or University Students & Graduates Tutors & Instructors Headmasters Education Consultants Education Providers Office Clerk & Administration Assistant Educational Psychologist Lecturer or Classroom Assistant Requirements Adult Teacher Trainer old PTLLS There are no fixed entry requirements for attending the Level 3 Award in Education and Training course. However, you must be 19 years old or over upon certification. An interest in teaching adults and E-learning will be beneficial. Moreover, you must have basic ICT and time management skills. It will help you complete online written assignments. Since this is a Level 3 course, you must be ready to read through various texts, cross-reference, research theories or principles or practises of effective teaching. Finally, you need to demonstrate practical teaching skills via assessment work. Career path Adult Teacher Trainer old PTLLS You can choose from a variety of professions either in teaching or training upon completion of the Adult Teacher Trainer old PTLLS course. These include - Primary School Teacher Secondary School Teacher Further Education Teacher Private Tutor Freelance Trainer Trainer at Your Workplace

Duration 2 Days 12 CPD hours This course is intended for This course is aimed at anyone currently working with data who is interested in using data visualisation to more effectively communicate their results. Overview At completion, delegates will understand how data visualisations can be best used to communicate actionable insights from data and be competent with the tools required to do it. Visualising data, and analytics results, is one of the most effective ways to achieve this. This course will cover the theory of data visualisation along with practical skills for creating compelling visualisations from data. Course Outline The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to horse breeding. The companies doing this most successfully understand that using sophisticated analytics approaches to unlock insights from data is only half the job. Communicating these insights to all of the different parts of an organisation is just as important as doing the actual analysis. Visualising data, and analytics results, is one of the most effective ways to achieve this. This course will cover the theory of data visualisation along with practical skills for creating compelling visualisations from data. To attend this course delegates should be competent in the use of data analysis tools such as reporting tools, spreadsheet software or business intelligence tools. The course will explore the following topics through a series of interactive workshop sessions: Fundamentals of data visualisation Data characteristics & dimensions Mapping visual encodings to data dimensions Colour theory Graphical perception & communication Interaction design Visualisation different characteristics of data: trends, comparisons, correlations, maps, networks, hierarchies, text Designing effective dashboards

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice