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The Build Your Algebra Fundamentals is a wonderful learning opportunity for anyone who has a passion for this topic and is interested in enjoying a long career in the relevant industry. It's also for anyone who is already working in this field and looking to brush up their knowledge and boost their career with a recognised certification. This Build Your Algebra Fundamentals consists of several modules that take around 11 hours to complete. The course is accompanied by instructional videos, helpful illustrations, how-to instructions and advice. The course is offered online at a very affordable price. That gives you the ability to study at your own pace in the comfort of your home. You can access the modules from anywhere and from any device. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Mock exams Multiple-choice assessment Certification Upon successful completion of the course, you will be able to obtain your course completion PDF Certificate at £9.99. Print copy by post is also available at an additional cost of £15.99 and the same for PDF and printed transcripts. Course Content Introduction Lecture 1 Introduction 00:03:00 Fundamental concepts on Algebraic Expressions Lecture 2 What is Algebra 00:02:00 Lecture 3 Simple Equations 00:05:00 Lecture 4 What are Polynomials 00:04:00 Lecture 5 Terms in Polynomials 00:03:00 Lecture 6 Degree of Polynomials 00:05:00 Lecture 7 Writing statements to algebraic form 00:04:00 Operations on Algebraic Expressions Lecture 8 Integers and common mistakes in solving integers 00:13:00 Lecture 9 Arrangement of Terms 00:07:00 Lecture 10 Powers on integers 00:04:00 Lecture11 Simplification using BODMAS 00:08:00 Lecture 12 Distributive Properties in Polynomials 00:04:00 Lecture 13 Simplify Polynomials 00:10:00 Lecture 14 Additions of Polynomials 00:06:00 Lecture 15 Subtractions of Polynomials 00:10:00 Indices ( Exponents) Lecture 16 The rules of Indices in algebra 00:11:00 Lecture 17 Fractional indices 00:10:00 Lecture 18 Understanding indices (practice questions) 00:07:00 Lecture 19 Problems from IGCSE Last year papers 00:09:00 Multiplication and Division of Algebraic expressions Lecture 20 Multiplication of monomial to Polynomial 00:09:00 Lecture 21 Multiplication of Polynomial by Polynomial 00:06:00 Lecture 22 Division of algebraic expression by a monomial 00:08:00 Lecture 23 Division of algebraic expression by another polynomial 00:09:00 Lecture 24 Division of a polynomial by another polynomial with remainder 00:11:00 Brackets in Algebra Lecture 25 Rules of brackets 00:04:00 Lecture 26 Simplification by removing brackets 00:11:00 Linear equations in one variable Lecture 27 Simplification of algebraic fractions 00:07:00 Lecture 28 Rules to solve linear equations in one variable 00:03:00 Lecture 29 Solving linear equations in one variable 00:07:00 Lecture 30 Solving complex linear equations in one variable 00:10:00 Lecture 31 Word problems on linear equations in one variable 00:13:00 Algebraic Identities Lecture 32 What are Identities? 00:05:00 Lecture 33 Identity ( a + b ) ² 00:13:00 Lecture 34 Identity ( a - b ) ² new 00:07:00 Lecture 35 Identity a² - b² = (a-b) (a +b ) new 00:07:00 Lecture 36 -- Standard Identities ( a + b + c ) ² = a ² + b ² + c ² + 2 a b + 2 a c +2 b c old 00:07:00 Lecture 37 Identity (x + a) (x + b) Identity Derivation & Application new 00:08:00 Lecture 38 Pascal's Triangle _ Identity ( a + b ) ³ new 00:07:00 Lecture 39 Identities( a - b ) ³, ( a ³ + b ³) and (a ³ - b ³) new 00:13:00 Lecture 40 - Standard Identities a ³ + b ³ + c ³ - 3 a b c 00:10:00 Formula : Change of subject of formula Lecture 41 -Changing the subject of formula 00:08:00 Linear Inequalities Lecture 42 - Linear Inequalities 00:12:00 Resolve into factors Lecture 43 - Factorization by taking out common factor 00:10:00 Lecture 44 - Factorization by grouping the terms 00:09:00 Lecture 45 - factorize using identity a ² - b ² 00:07:00 Lecture 46 - factorize using identity (a + b )² and (a - b )² (2) 00:08:00 Lecture 47 - factorize using identity ( a + b + c ) ² 00:05:00 Lecture 48 - factorization by middle term split 00:12:00 Algebraic Fractions Lecture 49 -Simplification of algebraic fractions 00:06:00 Coordinate axis - points and Line graph Lecture 50 All that you need to know about co ordinate axis 00:04:00 Lecture 51 Some important facts needed to draw line graph 00:03:00 Lecture 52 - How to draw a line graph on coordinate plane 00:03:00 Lecture 53 Drawing line graphs 00:06:00 System of simultaneous linear equations in two variables Lecture 54 Simultaneous Linear Equations in two variables- intro 00:03:00 Lecture 55 Graphical method of solving linear equations 00:06:00 Lecture 56 Graphical method - more problems 00:10:00 Lecture 57 Method of Elimination by substitution 00:09:00 Lecture 58 Method of Elimination by Equating coefficients 00:11:00 Lecture 59 Method of Elimination by cross multiplication 00:07:00 Lecture 60 Equations reducible to simultaneous linear equations 00:12:00 Lecture 61 Word Problems on Linear equations 00:18:00 Polynomials Lecture 62 Polynomials and Zeros of polynomials 00:10:00 Lecture 63 Remainder Theorem 00:04:00 Lecture 64 Factor Theorem 00:08:00 Lecture 65 Practice problems on Remainder and Factor Theorem 00:09:00 Lecture 66 Factorization using factor Theorem 00:10:00 Quadratic Polynomials Lecture 67 Zeros of polynomials α, β & γ 00:10:00 Lecture 68 Relation between zeros and coefficients of a polynomials 00:13:00 Lecture 69 Finding polynomials if zeros are known 00:06:00 Lecture 70 Practice problems on zeros of polynomials 00:10:00 Lecture 71Problems solving with α and β (part 1) 00:11:00 Lecture 72 Problems solving with α and β (part 2) 00:10:00 Quadratic Equations Lecture73 what are Quadratic equations 00:03:00 Lecture 74 Solutions by factorization method 00:12:00 Lecture 75 Solutions by completing square formula 00:06:00 Lecture 76 Deriving Quadratic formula 00:05:00 Lecture 77 Practice problems by Quadratic formula 00:07:00 Lecture 78 Solving complex quadratic equations by Quadratic Formula 00:11:00 Lecture 79 Solutions of reducible to Quadratic Formula 00:09:00 Lecture 80 Skilled problems on Quadratic Equations 00:07:00 Lecture 81 Exponential problems reducible to Quadratic Equations 00:06:00 Lecture 82 Nature of Roots of Quadratic Equations 00:09:00 Lecture 83 Word problems on quadratic Equations Part 1 00:13:00 Lecture 84 Word problems on quadratic Equations Part 2 00:11:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist? Well then, you've come to the right place!

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. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Welcome to the course Introduction 00:02:00 Setting up R Studio and R crash course Installing R and R studio 00:05:00 Basics of R and R studio 00:10:00 Packages in R 00:10:00 Inputting data part 1: Inbuilt datasets of R 00:04:00 Inputting data part 2: Manual data entry 00:03:00 Inputting data part 3: Importing from CSV or Text files 00:06:00 Creating Barplots in R 00:13:00 Creating Histograms in R 00:06:00 Basics of Statistics Types of Data 00:04:00 Types of Statistics 00:02:00 Describing the data graphically 00:11:00 Measures of Centers 00:07:00 Measures of Dispersion 00:04:00 Introduction to Machine Learning Introduction to Machine Learning 00:16:00 Building a Machine Learning Model 00:08:00 Data Preprocessing for Regression Analysis Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Importing the dataset into R 00:03:00 Univariate Analysis and EDD 00:03:00 EDD in R 00:12:00 Outlier Treatment 00:04:00 Outlier Treatment in R 00:04:00 Missing Value imputation 00:03:00 Missing Value imputation in R 00:03:00 Seasonality in Data 00:03:00 Bi-variate Analysis and Variable Transformation 00:16:00 Variable transformation in R 00:09:00 Non Usable Variables 00:04:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy variable creation in R 00:05:00 Correlation Matrix and cause-effect relationship 00:10:00 Correlation Matrix in R 00:08:00 Linear Regression Model The problem statement 00:01:00 Basic equations and Ordinary Least Squared (OLS) method 00:08:00 Assessing Accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy - RSE and R squared 00:07:00 Simple Linear Regression in R 00:07:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting result for categorical Variable 00:05:00 Multiple Linear Regression in R 00:07:00 Test-Train split 00:09:00 Bias Variance trade-off 00:06:00 Test-Train Split in R 00:08:00 Regression models other than OLS Linear models other than OLS 00:04:00 Subset Selection techniques 00:11:00 Subset selection in R 00:07:00 Shrinkage methods - Ridge Regression and The Lasso 00:07:00 Ridge regression and Lasso in R 00:12:00 Classification Models: Data Preparation The Data and the Data Dictionary 00:08:00 Importing the dataset into R 00:03:00 EDD in R 00:11:00 Outlier Treatment in R 00:04:00 Missing Value imputation in R 00:03:00 Variable transformation in R 00:06:00 Dummy variable creation in R 00:05:00 The Three classification models Three Classifiers and the problem statement 00:03:00 Why can't we use Linear Regression? 00:04:00 Logistic Regression Logistic Regression 00:08:00 Training a Simple Logistic model in R 00:03:00 Results of Simple Logistic Regression 00:05:00 Logistic with multiple predictors 00:02:00 Training multiple predictor Logistic model in R 00:01:00 Confusion Matrix 00:03:00 Evaluating Model performance 00:07:00 Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00 Linear Discriminant Analysis Linear Discriminant Analysis 00:09:00 Linear Discriminant Analysis in R 00:09:00 K-Nearest Neighbors Test-Train Split 00:09:00 Test-Train Split in R 00:08:00 K-Nearest Neighbors classifier 00:08:00 K-Nearest Neighbors in R 00:08:00 Comparing results from 3 models Understanding the results of classification models 00:06:00 Summary of the three models 00:04:00 Simple Decision Trees Basics of Decision Trees 00:10:00 Understanding a Regression Tree 00:10:00 The stopping criteria for controlling tree growth 00:03:00 The Data set for this part 00:03:00 Importing the Data set into R 00:06:00 Splitting Data into Test and Train Set in R 00:05:00 Building a Regression Tree in R 00:14:00 Pruning a tree 00:04:00 Pruning a Tree in R 00:09:00 Simple Classification Tree Classification Trees 00:06:00 The Data set for Classification problem 00:01:00 Building a classification Tree in R 00:09:00 Advantages and Disadvantages of Decision Trees 00:01:00 Ensemble technique 1 - Bagging Bagging 00:06:00 Bagging in R 00:06:00 Ensemble technique 2 - Random Forest Random Forest technique 00:04:00 Random Forest in R 00:04:00 Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques 00:07:00 Gradient Boosting in R 00:07:00 AdaBoosting in R 00:09:00 XGBoosting in R 00:16:00 Maximum Margin Classifier Content flow 00:01:00 The Concept of a Hyperplane 00:05:00 Maximum Margin Classifier 00:03:00 Limitations of Maximum Margin Classifier 00:02:00 Support Vector Classifier Support Vector classifiers 00:10:00 Limitations of Support Vector Classifiers 00:01:00 Support Vector Machines Kernel Based Support Vector Machines 00:06:00 Creating Support Vector Machine Model in R The Data set for the Classification problem 00:01:00 Importing Data into R 00:08:00 Test-Train Split 00:09:00 Classification SVM model using Linear Kernel 00:16:00 Hyperparameter Tuning for Linear Kernel 00:06:00 Polynomial Kernel with Hyperparameter Tuning 00:10:00 Radial Kernel with Hyperparameter Tuning 00:06:00 The Data set for the Regression problem 00:03:00 SVM based Regression Model in R 00:11:00 Assessment Assessment - Machine Learning Masterclass 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Overview This comprehensive course on Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning with Python comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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 Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning with Python 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 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 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Is the thrill of solving mathematical conundrums your thing? Are you adept at distinguishing between polar coordinates and hyperbolic functions, or vectors and matrices? If so, our Edexcel accredited Further Mathematics A-Level online course beckons you. With unwavering support from your personal tutor, you'll develop the ability to construct and present mathematical arguments via diagrams, graphs, and symbols. Moreover, you'll refine your understanding of modelling assumptions, conquer quadratic equations with real coefficients, and broaden your mathematical horizons. Course Benefits: Get access to a new course aligned with the latest specifications, enriched with interactive and engaging content Avail of the Fast track option for exams in 2022 Access to partnership exam centres, ensuring a guaranteed exam venue Unlimited tutor support to help craft a study plan and assist throughout your learning journey Exam pass guarantee (we've got your back for the next exam if you don’t pass the first time). Awarding Body: The Edexcel, our awarding body, is the UK's largest awarding organisation that has been helping individuals achieve academic and vocational qualifications in schools, colleges, and workplaces in the UK and beyond for nearly two decades. Course code: X922 Qualification code: 9FMO Official Qualification Title: Further Maths A-Level ⏱ Study Hours: Allocate between 300 and 360 hours of study time, plus additional time for completing assignments. 👩‍🏫 Study Method: Experience a dynamic and engaging learning process via our online learning platform. If needed, the learning resources, which include videos, quizzes, and interactive activities, can be printed for offline study. 📆 Course Duration: The course will span up to 24 months from the date of enrolment. All your learning materials will be accessible on our MyOxbridge portal. 📋 Assessment: Enrolments are now open for Summer 2022 examinations. The course necessitates the completion of four standard A-level exams and various assignments. While the assignments don't contribute to the final grade, they allow you to get tutor feedback and help in monitoring your progress. We also provide a guaranteed exam space in our nationwide exam centres. 👩‍🎓 Course Outcomes: Successful completion earns you an A-Level in Further Mathematics, issued by Edexcel. This certificate holds the same value as that issued to students in any other educational institution. ℹ️ Additional Information: Official Qualification Title - Further Maths A-Level Level - Advanced (Level 3) Course Content: Our course curriculum includes but is not limited to Core Mathematics 1 & 2, Further Pure Mathematics 1 & 2, Statistics 1 & 2, Mechanics 1 & 2, and Decision 1 & 2. These units cover a wide array of topics, including proof, complex numbers, matrices, algebra and functions, calculus, vectors, polar co-ordinates, hyperbolic functions, differential equations, groups, number theory, sequences and series, discrete probability distributions, hypothesis testing, central limit theorem, chi squared testing, linear regression, continuous probability distributions, correlation, combination of random variables, confidence intervals, moments and impulse, work, energy, and power, elastic springs and strings, elastic collisions, motion in a circle, centre of mass, further dynamics, further kinematics, algorithms, graphs, critical path analysis, linear programming, transportation problems, allocation problems, flows in networks, dynamic programming, game theory, recurrence relationship, and decision analysis.

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

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. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Course Introduction Introduction 00:03:00 Part 1: Forecasting Basics of Forecasting 00:05:00 Creating Linear Model with Trendlines 00:08:00 1.1 Getting Data Ready For Regression Model Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Univariate analysis and EDD 00:03:00 Discriptive Data Analytics in Excel 00:10:00 Outlier Treatment 00:04:00 Identifying and Treating Outliers in Excel 00:04:00 Missing Value Imputation 00:03:00 Identifying and Treating missing values in Excel 00:04:00 Variable Transformation in Excel 00:03:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy Variable Creation in Excel 00:07:00 Correlation Analysis 00:09:00 Creating Correlation Matrix in Excel 00:08:00 1.2 Forecasting Using Regression Model The Problem Statement 00:01:00 Basic Equations and Ordinary Least Squares (OLS) method 00:08:00 Assessing accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy: RSE and R squared 00:07:00 Creating Simple Linear Regression model 00:02:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting results of Categorical variables 00:05:00 Creating Multiple Linear Regression model 00:07:00 1.3 Handling Special Events Like Holiday Sales Forecasting in presence of special events 00:02:00 Excel: Running Linear Regression using Solver 00:08:00 Excel: Including the impact of Special Events 00:22:00 1.4 Identifying Seasonality & Trend for Forecasting Models to identify Trend & Seasonality 00:06:00 Excel: Additive model to identify Trend & Seasonality 00:09:00 Excel: Multiplicative model to identify Trend & Seasonality 00:06:00 Market Basket Analysis Market Basket and Lift - Introduction 00:08:00 Named Ranges - Excel 00:10:00 Indirect Function - Excel 00:05:00 2-way lift calculation in Excel 00:11:00 2-way lift calculation - Dynamic 00:07:00 2-way lift data table creation 00:07:00 3-way lift calculation 00:19:00 Store Layout optimization using Lift values 00:15:00 RFM (Recency, Frequency, Monetary) Analysis RFM (recency, frequency, monetary) Analysis 00:08:00 RFM Analysis in Excel- Part 1 00:16:00 RFM Analysis in Excel- Part 2 00:12:00 Part 2: Pricing Part 2: Pricing Steps of setting a Pricing policy 00:03:00 Different Pricing Objectives 00:07:00 2.1 Estimating Demand Estimating Demand 00:07:00 Forms of Demand Curve 00:02:00 Excel: Estimating Linear Demand Curve 00:08:00 Excel: Estimating Power Demand curve with Elasticity 00:05:00 Excel: Estimating Power Demand Curve with points 00:03:00 Subjective Demand curve 00:01:00 Excel: Estimating Subjective Demand Curve 00:02:00 2.3 Evaluating Pricing Strategies Price Bundling 00:07:00 Types of Bundling 00:08:00 The Bundling Problem 00:04:00 Excel: Solving Bundling problem Part 1 00:14:00 Excel: Solving Bundling problem Part 2 00:08:00 Non-Linear Pricing Strategies 00:03:00 Excel: Solving Bundling problem (Price Reversal) 00:08:00 3.1 Lifetime Customer Value Lifetime Customer Value - Key concepts 00:09:00 Lifetime Customer Value - Excel model 00:11:00 3.2 Variations And Sensitivity Analysis Sensitivity Analysis in Excel 00:07:00 Variations in finding customer value 00:07:00 Appendix 1: Excel Crash Course Basics 00:08:00 Worksheet Basics 00:16:00 Entering values and Formulas 00:07:00 Data Handling Basics - Cut, Copy and Paste 00:14:00 Saving and Printing - Basics 00:09:00 Basic Formula Operations 00:13:00 Mathematical Formulas 00:19:00 Textual Formulas 00:17:00 Logical Formulas 00:11:00 Date-Time Formulas 00:07:00 Lookup Formulas ( V Lookup, Hlookup, Index-Match ) 00:08:00 Data Tools 00:19:00 Formatting data and tables 00:18:00 Pivot Tables 00:08:00 Advance Excel- Solver, Data tables 00:15:00 Assessment Assessment - Retail Analytics In Microsoft Excel Diploma 00:10:00 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