323 Algorithms courses in London delivered Online

The Statistical Analysis Training Course is pivotal in the modern world, offering essential skills that are increasingly demanded across various industries. As businesses and organizations generate vast amounts of data, the ability to analyze and interpret this data becomes crucial. Learning from The Statistical Analysis Training Course equips individuals with expertise in key areas such as probability, hypothesis testing, regression analysis, and predictive analytics, enhancing their employability. In the UK, proficiency gained from this Statistical Analysis Training course can significantly boost job opportunities, with data analysts and statisticians earning an average salary of £35,000 to £50,000 annually. The demand for statistical analysis skills is on the rise, with the sector experiencing a growth rate of 33% over the past five years. Advantages of the Statistical Analysis Training course include a comprehensive understanding of both foundational and advanced statistical concepts, which are integral in roles across finance, healthcare, marketing, and technology. The Statistical Analysis Training Course ensures that learners are well-versed in modern analytical techniques, making them valuable assets in a data-driven economy. As the importance of data analytics continues to grow, so does the value of this training, making it an indispensable tool for career advancement. Key Features: CPD Certified Statistical Analysis Course Free Certificate Developed by Specialist Lifetime Access Course Curriculum: Statistical Analysis Training Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Learning Outcomes: Grasp fundamental statistical concepts for data analysis proficiency. Understand measures of central tendency and dispersion in datasets. Apply probability theory to make informed statistical decisions. Utilize hypothesis testing techniques to draw meaningful conclusions. Master regression analysis for predictive modelling and trend identification. Embrace Bayesian methods and enhance statistical inference capabilities. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Statistical Analysis Training course is accessible to anyone eager to learn more about this topic. Through this course, you'll gain a solid understanding of Statistical Analysis Training. Moreover, this course is ideal for: Aspiring data analysts seeking statistical foundations for career advancement. Professionals in research roles aiming to refine statistical analysis skills. Students pursuing degrees in mathematics, economics, or related disciplines. Business professionals looking to leverage data-driven insights for strategic decisions. Anyone interested in enhancing statistical literacy and analytical reasoning abilities. Requirements There are no requirements needed to enrol into this Statistical Analysis Training course. We welcome individuals from all backgrounds and levels of experience to enrol into this Statistical Analysis Training course. Career path After finishing this Statistical Analysis Training course you will have multiple job opportunities waiting for you. Some of the following Job sectors of Statistical Analysis Training are: Data Analyst - £30K to £45K/year. Statistician - £35K to £50K/year. Market Research Analyst - £25K to £40K/year. Business Intelligence Analyst - £35K to £55K/year. Healthcare Data Analyst - £30K to £50K/year. Certificates Digital certificate Digital certificate - Included Certificate of Completion Digital certificate - Included Will be downloadable when all lectures have been completed.

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With a single payment, you will gain access to Python Programming course, including 10 premium courses, a QLS Endorsed Hardcopy certificate (for the title course) and 11 PDF certificates for Absolutely free. This Diploma in Python Programming Bundle Package includes: Main Course: Diploma in Python Programming at QLS Level 5 10 Premium Additional CPD QS Accredited Courses - Course 01: Learn Programming with Python Course 02: Python Data Science Course 03: An Introduction to Graph Theory Course 04: Cloud Computing / CompTIA Cloud+ (CV0-002) Course 05: Level-3 Machine Learning Course with Python Course 06: Learn to Use Python for Spatial Analysis in ArcGIS Course 07: Higher Order Functions in Python - Level 03 Course 08: Easy to Advanced Data Structures Course 09: Introduction to Data Analysis Course 10: Business Intelligence and Data Mining Diploma With so much valuable content included, it's no wonder that this bundle is quickly becoming one of our most popular offerings. So why wait? Enrol today and start your journey towards becoming a Python expert. Learning Outcomes of Python Programming Grasp fundamental Python concepts like variables, data types, operators, and control flow. Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Gain expertise in data analysis techniques and explore machine learning algorithms using Python. Implement Python for cloud deployment using platforms like AWS or Azure (course dependent). Utilise Python for spatial data analysis with ArcGIS (course dependent). Develop proficiency in writing clean, efficient, and maintainable Python code. Why Choose Us? Get a Free QLS Endorsed Certificate upon completion of Python Programming Get a free student ID card with Python Programming Training program (£10 postal charge will be applicable for international delivery) The Python Programming is affordable and simple to understand This course is entirely online, interactive lesson with voiceover audio Get Lifetime access to the Python Programming course materials The Python Programming comes with 24/7 tutor support Start your learning journey straightaway! *** Course Curriculum *** Main Course: Diploma in Python Programming at QLS Level 5 Unit 01: Introduction To Python Programming Unit 02: Getting Started With Python Unit 03: Conditional Branching With Python Unit 04: Importing External/Internal Library In Python Unit 05: Project Rock Paper And Scissors Unit 06: Strings Operation In Python Unit 07: Date And Time In Python Unit 08: File Handling, Read And Write Using Python Unit 09: Data Storage Structures, Tuple, List And Dictionary Unit 10: Writing User Functions In Python Unit 11: Sending Mail Unit 12: Import Tricks In Python Unit 13: Import Operating System And Platform Unit 14: Exceptions Handling In Python Unit 15: Installing Packages And Scheduling In Python Unit 16: Data Base In Python Using Sqlite Unit 17: Running Program From Command Prompt And Jupyter Notebook Unit 18: Conclusion Course 01: Learn Programming with Python Module 01 A Installing Python FREE Documentation Command Line Variables Simple Python Syntax Keywords Import Module Module 02 Additional Topics If Elif Else Iterable For Loops Execute Exceptions Module 03 Data Types Number Types More Number Types Strings More Strings Files Lists Dictionaries Tuples Sets Course 02: Python Data Science Unit 01: Introduction To Python Data Science Module 01: Introduction to Python Data Science Module 02: Environment Setup Unit 02: Data Cleaning Packages Module 01: Numpy package for calculations Module 02: Panda package for Data cleaning Unit 03: Data Visualization Packages Module 01: Matplotlib Data Visualization Part 1 Module 02: Matplotlib Data Visualization Part 2 =========>>>>> And 8 More Courses <<<<<========= How will I get my Certificate? After successfully completing the course, you will be able to order your QLS Endorsed Certificates and CPD Accredited Certificates as proof of your achievement. PDF Certificate: Free (Previously it was £12.99*11 = £143) QLS Endorsed Hard Copy Certificate: Free (For The Title Course: Previously it was £119) CPD 255 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for: Individuals who want to become proficient Python programmers. Professionals who want to enhance their programming skills. Students who want to pursue a career in data science or machine learning. Anyone interested in learning Python programming language. Requirements There is no formal requirements to enrol in this course. Career path This bundle will help you to develop your knowledge and skills to pursue different careers, such as Junior Python Developer: £25,000 - £40,000 per year. Data Analyst: £26,000 - £50,000 per year. Machine Learning Engineer: £40,000 - £80,000 per year. Data Scientist: £45,000 - £85,000 per year. Certificates CPD Accredited Digital Certificate Digital certificate - Included Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free. CPD QS Accredited Certificate Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee. Diploma in Python Programming at QLS Level 5 Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee.

Overview Instructional Design Pro (Part 1) Course is yet another 'Teacher's Choice' course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Instructional Design Pro (Part 1) Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills. Consisting of several modules, the course teaches you everything you need to succeed in this profession. The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website. Why Choose Teachers Training Some of our website features are: This is a dedicated website for teaching 24/7 tutor support Interactive Content Affordable price Courses accredited by the UK's top awarding bodies 100% online Flexible deadline Entry Requirements No formal entry requirements. You need to have: Passion for learning A good understanding of the English language Be motivated and hard-working Over the age of 16. Certification CPD Certification from The Teachers Training Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish. You can get your digital certificate (PDF) for £4.99 only Hard copy certificates are also available, and you can get one for only £10.99 You can get both PDF and Hard copy certificates for just £12.99! The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs. Unit 01: Introduction And Welcome To Curriculum Structural Design Ideas For Instructional Designers 00:15:00 What Makes An Amazing Training Curriculum 00:13:00 Unit 02: Identify With Your Audience Identify With Your Audience Introduction 00:06:00 Identify Their Roles 00:18:00 Identify Their Goals 00:09:00 Identify Their Why 00:18:00 Unit 03: Identify What They Do Identify What They Already Do 00:15:00 Identify What They Already Do Activity 00:19:00 Identify What They Want To Do 00:07:00 Identify What They Need To Do 00:10:00 Unit 04: Organize What They Do Organize What They Do Intro 00:08:00 Organize Tasks And Subtasks 00:19:00 Organize Tasks And Subtasks - Part 2 00:19:00 Create Modules And Lessons 00:17:00 Set Up A Curriculum Design Spreadsheet 00:20:00 Unit 05: Determine What To Teach Understanding Objectives 00:15:00 Understanding Objectives Activity 00:16:00 Determine Conventions To Teach 00:17:00 Determine Algorithms To Teach 00:17:00 Determine Concepts To Teach 00:18:00 Unit 06: Organize What To Teach Simplify And Group Objectives Part 1 00:06:00 Simplify And Group Objectives Part 2 00:17:00 Add Objectives To The Curriculum Design Spreadsheet 00:09:00 Mark Objectives For Assessments 00:11:00 Unit 07: Determine The Initial Class Structure Solidify Specific Course Groupings 00:15:00 Customize Course Titles And Lesson Titles 00:06:00 Determine Course Timelines 00:20:00 Assignment Assignment - Instructional Design Pro (Part 1) 00:00:00

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Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00