320 Linear courses delivered Online

Overview Embarking on the High School Math course opens doors to a world where numbers and equations form the backbone of countless real-world applications. In an era where data-driven decisions rule, this course stands as a cornerstone for those aspiring to thrive in numerous professional fields.  A recent study by the Educational Research Center highlighted that proficiency in high school mathematics is strongly correlated with success in higher education and various career paths. This course not only equips students with fundamental mathematical knowledge but also sharpens analytical and problem-solving skills, making them indispensable in today's competitive landscape. Venture on a journey of mathematical mastery. Enrol in the High School Math course today and unlock the door to a world of opportunities and intellectual growth! 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? There is no experience or previous qualifications required for enrolment on this High School Math. It is available to all students, of all academic backgrounds. Requirements Our High School Math is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 12 sections • 136 lectures • 23:03:00 total length •Introduction: 00:03:00 •What is Function?: 00:07:00 •Vertical Line Test: 00:04:00 •Value of a Function Graphically: 00:08:00 •Domain Range of a function Algebraically: 00:13:00 •Domain Range of a function Graphically: 00:06:00 •Even & Odd Functions: 00:07:00 •One to one Function: 00:05:00 •Composite Functions: 00:09:00 •How to draw Rational Functions- 1: 00:04:00 •How to draw Rational Functions- 2: 00:10:00 •Inverse of a function Algebraically: 00:05:00 •Inverse of a function Graphically: 00:09:00 •Practice Problems 1: 00:15:00 •Practice Problems 2: 00:11:00 •Resources Downloads: 00:40:00 •Introduction to Quadratic Equations: 00:04:00 •Solving Quadratic Equations by Factorization method: 00:10:00 •Writing in completed square form: 00:08:00 •Solving by completed square method: 00:08:00 •Sketching of Quadratic Graphs: 00:11:00 •Quadratic graphs using Transformations: 00:06:00 •Quadratic inequalities: 00:11:00 •Deriving Quadratic formula: 00:05:00 •Solving problems using Quadratic Formula: 00:06:00 •Equations reducible to Quadratic: 00:07:00 •Nature of Roots of Quadratic Equations: 00:04:00 •Nature of roots continues: 00:12:00 •Quadratic Equations (Resources): 00:50:00 •Distance formula: 00:15:00 •Mid point formula: 00:05:00 •Gradient of a line: 00:10:00 •Graphing using gradient and y intercept: 00:02:00 •Some standard lines: 00:04:00 •Slope intercept form y = m x +c: 00:05:00 •Intersection of line and parabola: 00:09:00 •Practice Problems from past papers (part 3): 00:12:00 •Sequence and series ( video): 00:08:00 •Arithmetic Sequence: 00:10:00 •General term of an A.P.: 00:07:00 •Finding given term is which term?: 00:05:00 •Writing sequence when two terms are known: 00:08:00 •Condition for three terms to be in A.P.: 00:05:00 •Sum to n terms of A.P.: 00:06:00 •Practice Problems 1 (A.P.): 00:08:00 •Practice problems 3 (A.P.): 00:07:00 •Practice problems 4 (A.P.): 00:10:00 •Geometric Progressions: 00:11:00 •Sum to n terms in G.P.: 00:14:00 •Sum to infinite Terms in G.P.: 00:13:00 •Practice Problems 1 (GP): 00:13:00 •Practice Problems 2 (GP): 00:06:00 •Practice Problems based on AP and GP both: 00:15:00 •Sequence and series Text 1: 00:40:00 •Sequence and series Text 2: 00:55:00 •What is Factorial?: 00:06:00 •n-choose -r problems: 00:06:00 •Properties of n - choose -r: 00:05:00 •Expanding using Binomial Theorem: 00:11:00 •Finding the indicated term in the Binomial expansion: 00:10:00 •Finding the indicated term from end: 00:09:00 •Finding the coefficient for given exponent (index) of the variable: 00:08:00 •Finding the term independent of variable: 00:05:00 •Expanding in increasing and decreasing powers of x: 00:09:00 •Practice problems 1: 00:12:00 •Practice Problems 2: 00:09:00 •Practice problems 3: 00:10:00 •Past papers problems 1: 00:15:00 •Past Paper problems 2: 00:13:00 •Past Paper problems 3: 00:09:00 •Resources in this section: 00:48:00 •What is Derivative?: 00:07:00 •Derivation of formula for Derivative: 00:06:00 •Differentiation by definition or First Principle: 00:06:00 •Power Rule: 00:20:00 •Practice Problems on Power Rule 1: 00:07:00 •Practice Problems on Power Rule 2: 00:07:00 •Practice Problems on Power Rule 3: 00:05:00 •Practice Problems on Power Rule 4: 00:11:00 •Practice Problems on Power Rule 5: 00:07:00 •Tangents and Normals- Basics: 00:12:00 •Practice- Tangents and Normals Part 1: 00:16:00 •Practice- Tangents and Normals Part 2: 00:13:00 •Practice- Tangents and Normals Part 3: 00:11:00 •Practice- Tangents and Normals Part 4: 00:14:00 •Stationary Points - Basics: 00:13:00 •Practice- Increasing Decreasing & Maxima Minima part 1: 00:11:00 •Practice- Increasing Decreasing & Maxima Minima part 2: 00:12:00 •Practice- Increasing Decreasing & Maxima Minima part 3: 00:10:00 •Concavity-Basics: 00:02:00 •Concavity & Second Derivative: 00:08:00 •Second Derivative Test: 00:09:00 •Practice Problems on second derivative: 00:04:00 •Practice Problem of Maxima Minima using second derivative test Part 1: 00:17:00 •Practice Problem of Maxima Minima using second derivative test Part 2: 00:10:00 •Practice Problem of Maxima Minima using second derivative test Part 3: 00:07:00 •Practice Problem of Maxima Minima using second derivative test Part 4: 00:07:00 •Applications of Maxima and Minima Part 1: 00:09:00 •Applications of Maxima and Minima Part 2: 00:07:00 •Applications of Maxima and Minima Part 3: 00:10:00 •Applications of Maxima and Minima Part 4: 00:09:00 •Applications of Maxima and Minima Part 5: 00:10:00 •Applications of Maxima and Minima Part 6: 00:08:00 •Past Paper Problems on applications of maxima and minima Part 1: 00:09:00 •Past Paper Problems on applications of maxima and minima Part 2: 00:09:00 •Past Paper Problems on applications of maxima and minima Part 3: 00:08:00 •Past Paper Problems on applications of maxima and minima Part 4: 00:07:00 •Chain Rule: 00:12:00 •Rate of change part 1: 00:05:00 •Rate of change part 2: 00:10:00 •Rate of change part 3: 00:07:00 •Past Paper Problems using chain rule -1: 00:06:00 •Past Paper Problems using chain rule - 2: 00:07:00 •Past Paper Problems using chain rule 3: 00:07:00 •Past Paper Problems using chain rule -4: 00:04:00 •Graphical Method of solving pair of linear equations: 00:10:00 •Video lecture on Graphical method: 00:05:00 •Method of elimination by substitution: 00:10:00 •Video lecture on substitution method: 00:06:00 •Method of elimination by equating the coefficients: 00:10:00 •Video lecture on equating coefficients method: 00:09:00 •Practice Problems on Linear equation: 00:20:00 •How to take up this course?: 00:10:00 •Background of Algebra: 00:10:00 •Language of Alg ebra: 00:10:00 •Finding Values of algebraic expressions: 00:14:00 •Fractional Indices: 00:10:00 •Higher Indices: 00:07:00 •Rules of Brackets: 00:04:00 •Simplification by removing brackets (BODMAS): 00:11:00 •Simplifications of Algebraic Fractions: 00:07:00 •Solving complex Linear Equations in one variable: 00:10:00 •Factorization by taking out common factor: 00:10:00 •Factorization by grouping the terms: 00:09:00 •Factorize using identity a ² - b ²: 00:07:00 •Factorization by middle term split: 00:12:00

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

About the course “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Introduction to Quantitative and Computational Finance” provides a basis to step into the world of Quantum Computing for Finance. This introductory course will develop fundamental concepts required for an understanding of quantum algorithms and more advanced topics in computational finance. Through this course, you will learn the basics of derivative products, the Black-Scholes-Merton model for pricing vanilla derivatives, and how to compute the price of exotic options with a computer. This course is designed for all those who wish to develop their skills and start a career in quantitative finance. This course is the first part of the specialised training program: “Quantum Computing for Finance”. What Skills you will learn The fundamentals of derivative products, their types – forwards and options, and their pricing. An understanding of the Black-Scholes-Merton model, hedging and volatility modelling. The computational and modelling techniques for pricing options such as Monte-Carlo simulations and the Finite Difference method. A strong foundation in quantitative and computational skills for modelling and solving complex financial problems using Python programming language. The skills for a career in the finance industry, including quantitative asset management and trading, financial engineering, risk management, and applied research. Course Prerequisites All potential learners should have prior knowledge of the following content areas, either through completion of academic studies or relative professional preparation: Basic calculus (partial derivatives) Probability theory (with an exposure to measure theory if possible) Basic linear algebra (matrix operations) Numerical Python (NumPy essentially) The course contains several Python based programming exercises. We recommend that you install Python on your local system to practice and implement the programs explained throughout the course. For instructions and tutorials for beginners, please click on the following link: Python installation instructions and tutorials for beginners Duration The estimated duration to complete this course is approximately 4 weeks (~3hrs/week). Course assessment To complete the course and earn certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more. Instructors QuantFiQuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.

Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. Course Curriculum Project Management Fundamentals: Know the Principles and Get it Right What is a Project 00:04:00 The Four Stage Project Lifecycle 00:08:00 Project Stages and Boundaries 00:08:00 One Reason why Projects go Wrong 00:05:00 Terminology used in the Project Stages 00:05:00 More on Project Gateways / Stage Gates 00:02:00 Project Definition Stage: The Vital Foundation to Your Success Define your Project: Goals and Objectives 00:10:00 Understanding Project Scope 00:06:00 Dealing with Scope Creep 00:06:00 Project Definition: Summary 00:03:00 Project Planning Stage: Failing to Plan = Planning to Fail The Book of the Plan 00:05:00 The Stakeholder Engagement Process 00:05:00 Stakeholder Analysis 00:07:00 Milestones are your Best Friends 00:08:00 The Work Breakdown Structure 00:08:00 The Gantt Chart 00:06:00 Tools for Creating a Gantt Chart 00:04:00 The Linear Responsibility Chart (LRC) aka The RACI Chart 00:09:00 The Risk Management Process 00:04:00 Risk Analysis 00:08:00 The Six Strategies for Managing Risks 00:09:00 The Risk Register (or Risk Log) 00:06:00 Project Delivery Stage: Don't you Love it When a Plan Comes Together! The Four Essentials of Leading your Team 00:07:00 Project Delivery - The Three Key Cycles 00:12:00 Project Closure Stage: Deep Sigh - You're Nearly Done Closing Words 00:01:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Overview Build a professional profile and boost your career by enrolling in the Complete Introduction to Business Data Analysis Level 3. The world is nowadays run by data. Data analysis is one of the most crucial methods used in business worldwide. This course will help you learn the art of practical business analysis, along with its functions and objectives from a contemporary corporate perspective. You will be able to apply a data-driven approach with the knowledge learned from the course and become a successful business analyst. So, what are you waiting for? Start learning and get the benefits by enrolling 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? There is no experience or previous qualifications required for enrolment on this Complete Introduction to Business Data Analysis Level 3. It is available to all students, of all academic backgrounds. Requirements Our Complete Introduction to Business Data Analysis Level 3 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management , Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 2 sections • 14 lectures • 04:55:00 total length •Module 1: Statistics Fundamentals: 00:15:00 •Module 2: Data Analysis: 00:30:00 •Module 3: Probability: 00:30:00 •Module 4: Random Variables and Discrete Distributions: 00:25:00 •Module 5: Continuous Distributions: 00:15:00 •Module 6: Sampling Distributions: 00:15:00 •Module 7: Confidence Interval: 00:35:00 •Module 8: Hypothesis Testing with One Sample: 00:25:00 •Module 9: Hypothesis Testing with Two Samples: 00:15:00 •Module 10: The Chi-Square Distribution: 00:25:00 •Module 11: F Distribution and One-Way ANOVA: 00:25:00 •Module 12: Correlation analysis: 00:20:00 •Module 13: Simple Linear Regression Analysis: 00:20:00 •Assignment - Complete Introduction to Business Data Analysis Level 3: 00:00:00

Description Mind Mapping Diploma A mind map is a diagram or graphic representation visualizing connections between various ideas, concepts or pieces of information. A mind map uses a non-linear graphical layout to represent various tasks, items or concepts which is connected and arranged around a central subject or concept. Mind mapping unlocks the full potential of the brain by employing word, image, number, logic, rhythm, color and spatial awareness to create a powerful graphic technique that will enhance learning and promote clearer thinking. Although the term mind mapping is familiar to many, the technique hasn't been fully utilized by people. Mind mapping has been proven to offer great help in various aspects of our life especially while making notes and representing a complex piece of information. Mind mapping is essential while making notes from a book, taking notes during meetings and preparing for talks and lectures. All this can be created just using pen and paper. Mind mapping software are also available to generate maps but without basic understanding of how mind mapping works, it isn't easy to use the software. Mind Mapping Diploma introduces mind mapping as a visualization technique that can be deployed effectively in various spheres of our lives whether it is work or personal. The benefits of visualization techniques over textual representation techniques are clearly summarized in Mind Mapping Diploma. Mind Mapping Diploma reveals why mind mapping works and how it is a brain-friendly technique that allows quick assimilation, effective retention and recalling of information. The process of creating a mind map is discussed step-by-step and in detail in Mind Mapping Diploma. The course of Mind Mapping Diploma also makes a plausible comparison between mind mapping using the traditional pen and paper technique and using a computer software. Along with the art of generating a mind map, Mind Mapping Diploma also throws light into different elements of this outstanding graphic technique and how to put them into effective use. Mind Mapping Diploma does not confine the technique of mind mapping with strict rules and compulsory instructions to follow, it lets you experiment and develop your own individual style of mind mapping. Mind Mapping Diploma covers using mind mapping technique for the most common and frequent needs like note-making for lectures and talks and note-making from talks and meetings. The scope of Mind Mapping technique also includes learning and preparing for examinations through mind mapping. Anyone who completes Mind Mapping Diploma will not only be able to generate effective personalized mind maps but also be able to read textual information through the lens of mind mapping so that it can be easily converted to a mind map within a very short period of time. Mind Mapping Diploma is a course designed for everyone who finds that there is need to better organize the information in and around them to utilize it in the most effective manner possible. What you will learn 1: Introducing Mind Mapping 2: Visualizing Information 3: Generating Mind Maps 4: Why Mind Mapping Works 5: Mind Mapping: Talks and Lectures 6: Handling Text with Mind Mapping 7: Software versus Traditional Mind Mapping 9: Mind Mapping Reading Techniques 10: Learning and Preparing for Exams with Mind Mapping Course Outcomes After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college. Assessment Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter. Accreditation Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.

Improve your knowledge of goldwork with this intricate design

You will be introduced to the content of our online course Doing Qualitative Research, including the complete open data set of the Qualitative Research Journeys project

All about containers, Docker, and Kubernetes for Python engineers.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.