77 Maths courses in Driffield delivered Live Online

Read and discuss this novel of what it is to be human Highlights Join this book club and gain extra motivation to read in English 7 hours of live classes to help you develop fluent speaking in English Read this contemporary novel and discuss love, emotional intelligence and humanity Be corrected by an expert English teacher to remove errors and mistakes Practise and develop your English online in this course featuring Matt Haig's The Humans. Each week you read some chapters, discuss them in class and improve specific areas of vocabulary or grammar while also examining aspects of the novel. Personalised feedback improves your grammar, vocabulary and pronunciation. The Book Club book From the writer of The Midnight Library and How to Stop Time comes The Humans - a funny novel about alien abduction, mathematics, and what it is to be human. It combines hilarious moments with thought-provoking insights into how we interact. Maths professor Andrew Martin, is dead. His body is now taken over by an alien, who discovers that the professor wasn't very nice. The alien finds himself learning more about the professor, his family, and “the humans”. When he starts to feel for his own wife and son - who have no clue he’s not the real Andrew- the alien has a decision to make - complete his mission or abandon it and start a new life with the humans. Geoff says: "I laughed out loud reading this - but it also addresses serious themes. It shines a light on what it means to be human, and gaining emotional intelligence."

You will learn how to add, subtract, multiply and divide fractions. PREVIOUS KNOWLEDGE NEEDED: Basic addition, subtraction and multiplication.

Teaching Assistant Level 1, 2 & 3 Diploma – CPD Accredited Become a skilled and confident Teaching Assistant with this CPD-accredited course! Learn essential classroom support techniques, child development strategies, SEN support, and subject-specific teaching skills. Progress from beginner to advanced levels and boost your career in education. Study online at your own pace! 🚀 Enroll now & make a difference in children's education!

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

Forget your fear of all things finance, quickly understand financial terms and techniques and make a more calculated contribution in your management role.

Python Data Analytics with Python using Numpy, Pandas, Dataframes. Most attendees are in-work Data Professional. Private individuals are very welcome. Our Style: Hands-on, Practical Location: Online, Instructor-led

We have tutors available to help with all academic subject K-12 including advanced placement. We will take the time to get to know your student, their personality, goals, and learning style to make sure we find the best fit tutor. We know that if your student likes the person they are working with they will work hard and get better results. 

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.

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client