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225 Courses in Leicester delivered Online

Learn Python, JavaScript, and Microsoft SQL for Data science Course

By One Education

If data is the new oil, then coding is your refinery. Whether you're exploring the depths of machine learning or navigating databases with ease, this course sharpens your edge in the competitive world of data science. With a sharp focus on three industry-leading languages—Python, JavaScript, and Microsoft SQL—you’ll build the solid foundations needed to analyse, automate, and query data confidently. From writing clean scripts to crafting smart SQL queries, you’ll develop the mindset to speak fluently in the language of data. Delivered entirely online, the course keeps your learning agile and accessible. Python lays the groundwork for analysis and automation, JavaScript helps in data visualisation and interaction, and SQL ensures you can command databases without blinking. It's not about ticking boxes—it’s about building fluency in what matters. Whether you're upskilling or aiming for a sharper digital edge, this course speaks directly to future-focused learners ready to code with purpose. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: JavaScript Section 01: Introduction Section 02: Basics Section 03: Operators Section 04: Conditional Statements Section 05: Control Flow Statements Section 06: Functions Section 07: Error Handling Section 08: Client-Side Validations Python Section 09: Introduction Section 10: Basic Section 11: Strings Section 12: Operators Section 13: Data Structures Section 14: Conditional Statements Section 15: control flow statements Section 16: core games Section 17: functions Section 18: args, KW args for Data Science Section 19: project Section 20: Object oriented programming [OOPs] Section 21: Methods Section 22: Class and Objects Section 23: Inheritance and Polymorphism Section 24: Encapsulation and Abstraction Section 25: OOPs Games Section 26: Modules and Packages Section 27: Error Handling Microsoft SQL Section 28: Introduction Section 29: Statements Section 30: Filtering Data Section 31: Functions Section 32: Joins Section 33: Advanced commands Section 34: Structure and Keys Section 35: Queries Section 36: Structure queries Section 37: Constraints Section 38: Backup and Restore Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn Python, JavaScript, and Microsoft SQL for Data science course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn Python, JavaScript, and Microsoft SQL for Data science course is open to all, with no formal entry requirements. Career path Upon successful completion of the Learn Python, JavaScript, and Microsoft SQL for Data science Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

Learn Python, JavaScript, and Microsoft SQL for Data science Course
Delivered Online On Demand22 hours
£12

Learn Python, JavaScript, and Microsoft SQL for Data science Course

By One Education

Data doesn’t speak for itself — it needs someone who can ask the right questions and write the right code. This course offers a focused introduction to three of the most widely used tools in data science: Python, JavaScript, and Microsoft SQL. You’ll explore how each language plays its part in working with data, from handling large datasets to performing analysis and visualisation. All delivered online, with no whiteboards, lab coats or cold meeting rooms involved. Whether you're curious about coding or keen to brush up on logic and structure, the course walks you through the essentials with clarity and purpose. You’ll look into the building blocks of each language, how they interact with data, and how they can be used to create meaningful insights. Designed for learners who prefer clear content over convoluted lectures, it's a straight-talking guide to getting started in data science — without trying to be clever for the sake of it. Course Curriculum: JavaScript Section 01: Introduction Section 02: Basics Section 03: Operators Section 04: Conditional Statements Section 05: Control Flow Statements Section 06: Functions Section 07: Error Handling Section 08: Client-Side Validations Python Section 09: Introduction Section 10: Basic Section 11: Strings Section 12: Operators Section 13: Data Structures Section 14: Conditional Statements Section 15: control flow statements Section 16: core games Section 17: functions Section 18: args, KW args for Data Science Section 19: project Section 20: Object oriented programming [OOPs] Section 21: Methods Section 22: Class and Objects Section 23: Inheritance and Polymorphism Section 24: Encapsulation and Abstraction Section 25: OOPs Games Section 26: Modules and Packages Section 27: Error Handling Microsoft SQL Section 28: Introduction Section 29: Statements Section 30: Filtering Data Section 31: Functions Section 32: Joins Section 33: Advanced commands Section 34: Structure and Keys Section 35: Queries Section 36: Structure queries Section 37: Constraints Section 38: Backup and Restore Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn Python, JavaScript, and Microsoft SQL for Data science course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn Python, JavaScript, and Microsoft SQL for Data science course is open to all, with no formal entry requirements. Career path Upon successful completion of the Learn Python, JavaScript, and Microsoft SQL for Data science Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

Learn Python, JavaScript, and Microsoft SQL for Data science Course
Delivered Online On Demand22 hours
£12

R Programming for Data Science Course

By One Education

Step into the world of data with the sharp edge of R — a language that speaks fluently to numbers, charts, and trends. This R Programming for Data Science course is tailored for those with a curious mind and a spreadsheet-weary soul. Whether you're peering into data for the first time or have long wrestled with rows and columns, this course guides you through the essentials of R with a touch of wit and a solid sense of direction. It’s not about learning everything; it’s about learning what matters, and learning it well. From data wrangling to visual storytelling, you'll gain the tools to make sense of messy datasets and turn them into something meaningful. Tidy code, smart analysis, and clear plots await — all from the comfort of your device. This course speaks directly to analysts, researchers, students and data enthusiasts keen to harness the power of R without the fluff. It’s structured, to the point, and just detailed enough to keep things interesting. Who said data has to be dull? Meet the Endorsement The Quality Licence Scheme has been designed specifically to recognise high-quality courses. This R Programming for Data Science course materials are recognised by Quality Licence Scheme (QLS). This ensures the deep research and quality resource allocation behind the development phase of the course. In addition, the QLS certificate enriches your CV and recognises your quality study on the relevant subject. Meet the Accreditation CPD Quality Standards (CPD QS) accreditation assure the R Programming for Data Science course training and learning activities are relevant, reliable, and upto date. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: Unit 01: Data Science Overview Unit 02: R and RStudio Unit 03: Introduction to Basics Unit 04: Vectors Unit 05: Matrices Unit 06: Factors Unit 07: Data Frames Unit 08: Lists Unit 09: Relational Operators Unit 10: Logical Operators Unit 11: Conditional Statements Unit 12: Loops Unit 13: Functions Unit 14: R Packages Unit 15: The Apply Family - lapply Unit 16: The apply Family - sapply & vapply Unit 17: Useful Functions Unit 18: Regular Expressions Unit 19: Dates and Times Unit 20: Getting and Cleaning Data Unit 21: Plotting Data in R Unit 22: Data Manipulation with dplyr How is the R Programming for Data Science Course assessed? To simplify the procedure of evaluation and acknowledgement for learners, we provide an automated assessment system. For each test, the pass mark will be set to 60%. Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme After successfully completing the R Programming for Data Science course, learners will be able to order an endorsed certificate as proof of their achievement. The hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying - Within the UK: £109 International: £109 + £10 (postal charge) = £119 CPD Acknowledged Certificate from One Education After successfully completing this R Programming for Data Science course, you will qualify for the CPD acknowledged certificate from One Education, as proof of your continued expert development. Certificate is available in both PDF & hardcopy format, which can be received by paying - PDF Certificate: £9 Hardcopy Certificate (within the UK): £15 Hardcopy Certificate (international): £15 + £10 (postal charge) = £25 CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This R Programming for Data Science course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This R Programming for Data Science course is open to all, with no formal entry requirements. Career path Upon successful completion of the R Programming for Data Science Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

R Programming for Data Science Course
Delivered Online On Demand3 weeks
£12

Python With Data Science

By Nexus Human

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

Python With Data Science
Delivered OnlineFlexible Dates
Price on Enquiry

Healthcare Assistant Course

3.0(2)

By Alpha Academy

Investment Banking: LBO, IPO and M&A Online Course This Investment Banking Training: LBO, IPO and M&A course gives you a clear and practical introduction to the world of investment banking. You will learn how investment banks operate, how they value companies, and how major financial deals like leveraged buyouts, initial public offerings and mergers and acquisitions work. The course explains these processes in a simple, step-by-step way, helping you build the skills to succeed in corporate finance and deal-making. Course Curriculum Module 1: Working in Different Healthcare Settings Module 2: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care – Part 1 Module 3: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care – Part 2 Module 4: Maintaining Medical Records Module 5: Confidentiality in a Medical Environment Module 6: Health and Safety Responsibilities Module 7: Hygiene in Nursing Module 8: Infection Control Module 9: Mobility and Immobility Issues of Patients in Nursing Module 10: Rights and Responsibilities as a Health and Social Care Worker Module 11: Role as a Caregiver and Healthcare Professional Module 12: Providing Care or Treatment to People Who Lack Capacity Module 13: Managing Service Delivery in Health and Social Care Module 14: Medical Jargon and Terminology Module 15: Effects of Covid-19 on Human Life Module 16: Preventions and Social Measures to Be Taken Module 17: Information Technology in Health Care Module 18: Artificial Intelligence, Data Science and Technological Solutions Against Covid-19 (Learn more about this online course)

Healthcare Assistant Course
Delivered Online On Demand1 hour
FREE