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Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Quick Data Science Approach from Scratch is an innovatively structured course designed to introduce learners to the fascinating world of data science. The course commences with an enlightening introduction, setting the stage for a deep dive into the essence and significance of data science in the modern era. Learners are guided through a landscape of insights, where misconceptions about data science are addressed and clarified, paving the way for a clear and accurate understanding of the field. In the second section, the course shifts its focus to pivotal data science concepts. Beginning with an exploration of data types and variables, learners gain a solid foundation in handling various data formats. The journey then leads to mastering descriptive analysis, a critical skill for interpreting and understanding data trends. Learners will also navigate through the intricate processes of data cleaning and feature engineering, essential skills for refining and optimizing data for analysis. The concept of 'Data Thinking Development' is introduced, fostering a mindset that is crucial for effective data science practice. The final section offers an immersive experience in applying these skills to a real-world scenario. Here, learners engage in defining a problem, choosing suitable algorithms, and developing predictive models. This practical application is designed to cement the theoretical knowledge acquired and enhance problem-solving skills in data science. Learning Outcomes Build a foundational understanding of data science and its practical relevance. Develop proficiency in managing various data types and conducting descriptive analysis. Learn and implement effective data cleaning and feature engineering techniques. Cultivate a 'data thinking' approach for insightful data analysis. Apply data science methodologies to real-life problems using algorithmic and predictive techniques. Why choose this Quick Data Science Approach from Scratch course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Quick Data Science Approach from Scratch course for? Novices aiming to enter the data science field. Sector professionals integrating data science into their expertise. Academicians and learners incorporating data science in academic pursuits. Business strategists utilizing data science for enhanced decision-making. Statisticians and analysts broadening their expertise into the data science domain. Career path Entry-Level Data Scientist: £25,000 - £40,000 Beginner Data Analyst: £22,000 - £35,000 Emerging Business Intelligence Specialist: £28,000 - £45,000 Data-Focused Research Scientist: £30,000 - £50,000 Novice Machine Learning Practitioner: £32,000 - £55,000 Data System Developer (Starter): £26,000 - £42,000 Prerequisites This Quick Data Science Approach from Scratch does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Quick Data Science Approach from Scratch was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Course Overview & Introduction to Data Science Introduction 00:02:00 Data Science Explanation 00:05:00 Need of Data Science 00:02:00 8 Common mistakes by Aspiring Data Scientists/Data Science Enthusiasts 00:08:00 Myths about Data Science 00:03:00 Section 02: Data Science Concepts Data Types and Variables 00:04:00 Descriptive Analysis 00:02:00 Data Cleaning 00:02:00 Feature Engineering 00:02:00 Data Thinking Development 00:03:00 Section 03: A Real Life Problem Problem Definition 00:05:00 Algorithms 00:14:00 Prediction 00:03:00 Learning Methods 00:05:00 Assignment Assignment - Quick Data Science Approach from Scratch 00:00:00

Overview In the era where information is abundant and decisions are driven by data, have you ever pondered, 'what is machine learning?' or 'what is data science?' Dive into the realm of 'Data Science & Machine Learning with R from A-Z,' a comprehensive guide to unravel these complexities. This course effortlessly blends the foundational aspects of data science with the intricate depths of machine learning algorithms, all through the versatile medium of R. As the digital economy booms, the demand for machine learning jobs continues to surge. Equip yourself with the proficiency to navigate this dynamic field and transition from being an inquisitive mind to a sought-after professional in the space of data science and machine learning with R. Learning Outcomes: Understand the foundational concepts of data science and machine learning. Familiarise oneself with the R environment and its functionalities. Master data types, structures, and advanced techniques in R. Acquire proficiency in data manipulation and visual representation using R. Generate comprehensive reports using R Markdown and design web applications with R Shiny. Gain a thorough understanding of machine learning methodologies and their applications. Gain insights into initiating a successful career in the data science sector. Why buy this Data Science & Machine Learning with R from A-Z course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Data Science & Machine Learning with R from A-Z there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Data Science & Machine Learning with R from A-Z course for? This course is ideal for Individuals keen on exploring the intricacies of machine learning and data science. Aspiring data analysts and scientists looking to specialise in Machine Learning with R. IT professionals aiming to diversify their skill set in the emerging data-driven market. Researchers seeking to harness the power of R for data representation and analysis. Academics and students aiming to bolster their understanding of modern data practices with R. Prerequisites This Data Science & Machine Learning with R from A-Z does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Data Science & Machine Learning with R from A-Z was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Scientist - Average salary range: £35,000 - £70,000 Per Annum Machine Learning Engineer - Average salary range: £50,000 - £80,000 Per Annum Data Analyst - Average salary range: £28,000 - £55,000 Per Annum R Developer - Average salary range: £30,000 - £60,000 Per Annum R Shiny Web Developer - Average salary range: £32,000 - £65,000 Per Annum Machine Learning Researcher - Average salary range: £40,000 - £75,000 Per Annum Course Curriculum Data Science and Machine Learning Course Intro Data Science and Machine Learning Introduction 00:03:00 What is Data Science 00:10:00 Machine Learning Overview 00:05:00 Who is This Course for 00:03:00 Data Science and Machine Learning Marketplace 00:05:00 Data Science and Machine Learning Job Opportunities 00:03:00 Getting Started with R Getting Started 00:11:00 Basics 00:06:00 Files 00:11:00 RStudio 00:07:00 Tidyverse 00:05:00 Resources 00:04:00 Data Types and Structures in R Unit Introduction 00:30:00 Basic Type 00:09:00 Vector Part One 00:20:00 Vectors Part Two 00:25:00 Vectors - Missing Values 00:16:00 Vectors - Coercion 00:14:00 Vectors - Naming 00:10:00 Vectors - Misc 00:06:00 Creating Matrics 00:31:00 List 00:32:00 Introduction to Data Frames 00:19:00 Creating Data Frames 00:20:00 Data Frames: Helper Functions 00:31:00 Data Frames Tibbles 00:39:00 Intermediate R Intermediate Introduction 00:47:00 Relational Operations 00:11:00 Conditional Statements 00:11:00 Loops 00:08:00 Functions 00:14:00 Packages 00:11:00 Factors 00:28:00 Dates and Times 00:30:00 Functional Programming 00:37:00 Data Import or Export 00:22:00 Database 00:27:00 Data Manipulation in R Data Manipulation in R Introduction 00:36:00 Tidy Data 00:11:00 The Pipe Operator 00:15:00 The Filter Verb 00:22:00 The Select Verb 00:46:00 The Mutate Verb 00:32:00 The Arrange Verb 00:10:00 The Summarize Verb 00:23:00 Data Pivoting 00:43:00 JSON Parsing 00:11:00 String Manipulation 00:33:00 Web Scraping 00:59:00 Data Visualization in R Data Visualization in R Section Intro 00:17:00 Getting Started 00:16:00 Aesthetics Mappings 00:25:00 Single Variable Plots 00:37:00 Two Variable Plots 00:21:00 Facets, Layering, and Coordinate Systems 00:18:00 Styling and Saving 00:12:00 Creating Reports with R Markdown Creating with R Markdown 00:29:00 Building Webapps with R Shiny Introduction to R Shiny 00:26:00 A Basic R Shiny App 00:31:00 Other Examples with R Shiny 00:34:00 Introduction to Machine Learning Machine Learning Part 1 00:22:00 Machine Learning Part 2 00:47:00 Starting A Career in Data Science Starting a Data Science Career Section Overview 00:03:00 Data Science Resume 00:04:00 Getting Started with Freelancing 00:05:00 Top Freelance Websites 00:05:00 Personal Branding 00:05:00 Importance of Website and Blo 00:04:00 Networking Do's and Don'ts 00:04:00 Assignment Assignment - Data Science & Machine Learning with R 00:00:00

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

This bundle course has been specially designed to equip our learners with a more in-depth understanding of lettings negotiating by providing them with essential functional maths skills required for this role. By combining these related courses, learners will be able to apply their practical knowledge in the real world, to take their professionalism to the next level. Take steps to become a certified lettings negotiator and be equipped with the skills and practical knowledge to advise clients and support landlords in renting their properties with the Lettings negotiator Diploma. This beginners training program is designed to provide an insight into lettings management, exploring the role of a lettings negotiator and lettings agent in detail. You will learn about the different types of agents, how to carry out a property valuation, build a relationship with clients and much more. Possessing excellent maths skills is essential for anyone who wishes to work in the property market. The Functional Maths Training Course is designed to provide aspiring lettings negotiators with the necessary skills to master everyday maths problems related to lettings management. You will learn how to calculate overall profit and loss, develop crucial problem-solving skills and gain confidence in working with decimals, fractions and percentages. This training program includes practical exercises that can be applied in real-world scenarios and is an opportunity to learn valuable transferable skills. Learning Outcomes: Learn the fundamental principles and practices of lettings management Familiarise with the lettings agency agreements guideline Understand the differences between a lettings agent and estate agent Explore the day-to-day role and responsibilities of a lettings agent Develop a solid understanding of the property valuation process Gain advanced knowledge of functional maths and apply your new skills to everyday situations Develop your skills in logical thinking and problem-solving Learn how to work with decimals, percentages, fractions and much more Gain a solid understanding of ratios and proportion through practical exercises Understand how to calculate overall profit and loss Who is This Course for? This bundle course is ideal for aspiring lettings negotiators, or lettings agents who are new to the field and wish to gain the relevant skills and knowledge to take their career to the next level. This course is also ideal for anyone who works in the real estate industry and wishes to develop their skills in everyday maths. Method of Assessment: Upon completion of the course, you will be required to sit for an online multiple-choice quiz based assessment, which will determine whether you have passed the course (60% pass mark). The test will be marked immediately and results will be published instantly. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career Path This bundle course is the ultimate package training certification for those who are interested in a career in property. On successful completion, you will have the opportunity to work in a wide range of related industries, in roles such as: Lettings Negotiator Lettings Agent Manager Estate Agent or Lettings Manager Course Curriculum **Letting Agent** Lettings Management Introduction to Lettings Management 00:20:00 Necessary Documents for Lettings Management 01:30:00 Lettings Agency Agreements Guideline 00:15:00 Types of Agents 00:15:00 Knowing Your Clients 00:30:00 Lettings Management Agent Services 00:20:00 Property Valuation 00:30:00 Property Negotiation 00:30:00 Property Management 00:30:00 Lettings Management Laws 00:30:00 Letting Agent Code of Practice Introduction to Letting Agent Code of Practice 00:20:00 Overarching Standards of Practice 00:30:00 Lettings 00:30:00 Management and Maintenance 00:20:00 Ending the Tenancy and Communications & Resolving Complaints 00:15:00 Arrangements 00:15:00 Being a Lettings Negotiator Home Loans 00:15:00 Knowing When You're Ready To Buy 00:15:00 Is Your Lake Home For Sale? 01:00:00 Legal System & Lettings Management UK An Introduction to the UK legal system 00:15:00 Different Types of Investment Structures 00:15:00 Title & Registration 00:15:00 Finance in Lettings Management 00:15:00 Investment Purchase Procedure 00:15:00 Acquisition Costs 00:15:00 Land Usage 00:15:00 Planning & Environment 00:15:00 Investment Structure & Key Legislations 00:15:00 **Functional Skills (Maths)** Integers ( Directed Numbers) Lecture 1 Introduction 00:02:00 Lecture 2 Multiplying any digit number by 11, 22, and 33 00:09:00 Lecture 3 Integers and ordering of integers 00:06:00 Lecture 4 Addition and Subtractions of Integers 00:07:00 Lecture 5 Operations on Integers 00:04:00 Lecture 6 Multiplication and Division of Integers 00:04:00 Lecture 7 Powers of Integers (Exponents) 00:04:00 Factors and Multiples Lecture 8 Complete knowledge about factors and multiples 00:06:00 Lecture 9 Divisibility tests for 2,3,4,5,6 00:07:00 Lecture 10 Divisibility rules 7,8,9,10,11 00:06:00 Lecture 11 Prime Factorization 00:06:00 Lecture 12 Highest Common Factor HCF 00:10:00 Lecture 13 Least Common Multiple LCM 00:16:00 Lecture 14 Relation between LCM and HCF 00:04:00 Fractions Lecture 15 Classification of Fractions 00:04:00 Lecture 16 Convert mixed to improper and improper to mixed fractions 00:03:00 Lecture 17 Equivalent Fraction 00:04:00 Lecture 18 Comparing Fractions after converting to like fractions 00:09:00 Lecture 19 Reducing a fraction to lowest term 00:04:00 Lecture 20 Addition and subtraction OF fractions 00:08:00 Lecture 21 Multiplication and division of fractions 00:11:00 Lecture 22 Word problems on fractions 00:16:00 Simplification Rule : BODMAS Lecture 23 BODMAS and rules to remove brackets 00:03:00 Lecture 24 Simplify expressions using BODMAS rules 00:05:00 Lecture 25 Simplify expressions involving fractions using BODMAS 00:07:00 Decimal numbers Lecture 26 Decimal numbers in relation to fractions 00:08:00 Lecture 27 Like and unlike Decimals 00:05:00 Lecture 28 Ordering of Decimal numbers 00:10:00 Lecture 29 Addition and subtraction of Decimal 00:07:00 Lecture 30 Multiplication of Decimal 00:03:00 Lecture 31 Division of Decimal 00:05:00 Rational Numbers Lecture 32 Rational number 00:02:00 Lecture 33 Representation of Rational Numbers on Number line 00:04:00 Lecture 34 Determining which fraction is terminating or non terminating 00:05:00 Lecture 35 shortcut method of writing decimal expansion of fraction to decimal 00:08:00 Approximation Lecture 36 Rounding to whole number 00:06:00 Lecture 37 Rounding to required number of decimals (1 d.p. ) 00:05:00 Lecture 38 rounding to significant digits 00:07:00 Lecture 39 Practice question on three methods of approximation 00:05:00 Estimation and Scientific Notation or Standard form Lecture 40 Estimation 00:05:00 Lecture 41 Scientific Notation 00:07:00 Lecture 42 Operation on numbers when they are in scientific notation 00:06:00 Percentage Lecture 43 Percentage to fraction and fraction to percentage 00:06:00 Lecture 44 Percentage of a quantity and conversation to decimal 00:06:00 Lecture 45 Expressing one quantity as percentage of another quantity 00:06:00 Lecture 46 Finding increase decrease percent 00:05:00 Lecture 47 Uses of percentages-Word Problems 00:09:00 Ratio and Proportion Lecture 48 Ratio- How to express as simple ratio 00:09:00 Lecture 49 How to compare Ratios 00:04:00 Lecture 50 Word problems on finding ratios 00:07:00 Lecture 51 To divide a given quantity into a given Ratio 00:11:00 Lecture 52 Proportion 00:04:00 Lecture 53 Practice problems on Proportion 00:06:00 Lecture 54 Continued proportion 00:06:00 Unitary Method and its Applications Lecture 55 Direct Variation ( or proportion) 00:04:00 Lecture 56 Problems based on Direct proportion 00:10:00 Lecture 57 Inverse Variation ( or proportion) 00:10:00 Lecture 58 Multiple Ratios 00:10:00 Profit , Loss, discount and Tax Lecture 59 Basics - Profit and Loss 00:04:00 Lecture 60 More practice problems on Profit & Loss 00:06:00 Lecture 61 Selling price formula and Problems 00:04:00 Lecture 62 Cost price formula and Problems 00:03:00 Lecture 63 Higher problems on Profit and Loss 00:08:00 Lecture 64 Basics - Discount 00:05:00 Lecture 65 Practice problems on Discount 00:04:00 Lecture 66 Tax 00:06:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Improve your essential skills with our Functional Skills Maths & English course. Build a strong foundation in numeracy and literacy to excel in both work and life.

Level 7 QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Overview This comprehensive course on Functional Skills - Fitness will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Functional Skills - Fitness comes with accredited certification from CPD, 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 Functional Skills - Fitness. It is available to all students, of all academic backgrounds. Requirements Our Functional Skills - Fitness 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 4 sections • 10 lectures • 01:01:00 total length •Introduction To Functional Fitness Training: 00:01:00 •What Is Functional Fitness: 00:08:00 •Benefits Of Functional Fitness: 00:08:00 •Functional Fitness & Strength: 00:07:00 •Functional Fitness & Other Exercises: 00:08:00 •Common Mistakes With Functional Fitness: 00:07:00 •Functional Fitness & Power: 00:08:00 •Functional Fitness & Range Of Motion: 00:06:00 •Functional Fitness & Balance & Endurance: 00:07:00 •Conclusion: 00:01:00

Overview This comprehensive course on Statistics & Probability for Data Science & Machine Learning will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Statistics & Probability for Data Science & Machine Learning comes with accredited certification from CPD, 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 Statistics & Probability for Data Science & Machine Learning. It is available to all students, of all academic backgrounds. Requirements Our Statistics & Probability for Data Science & Machine Learning 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 10 sections • 89 lectures • 11:27:00 total length •Welcome!: 00:02:00 •What will you learn in this course?: 00:06:00 •How can you get the most out of it?: 00:06:00 •Intro: 00:03:00 •Mean: 00:06:00 •Median: 00:05:00 •Mode: 00:04:00 •Mean or Median?: 00:08:00 •Skewness: 00:08:00 •Practice: Skewness: 00:01:00 •Solution: Skewness: 00:03:00 •Range & IQR: 00:10:00 •Sample vs. Population: 00:05:00 •Variance & Standard deviation: 00:11:00 •Impact of Scaling & Shifting: 00:19:00 •Statistical moments: 00:06:00 •What is a distribution?: 00:10:00 •Normal distribution: 00:09:00 •Z-Scores: 00:13:00 •Practice: Normal distribution: 00:04:00 •Solution: Normal distribution: 00:07:00 •Intro: 00:01:00 •Probability Basics: 00:10:00 •Calculating simple Probabilities: 00:05:00 •Practice: Simple Probabilities: 00:01:00 •Quick solution: Simple Probabilities: 00:01:00 •Detailed solution: Simple Probabilities: 00:06:00 •Rule of addition: 00:13:00 •Practice: Rule of addition: 00:02:00 •Quick solution: Rule of addition: 00:01:00 •Detailed solution: Rule of addition: 00:07:00 •Rule of multiplication: 00:11:00 •Practice: Rule of multiplication: 00:01:00 •Solution: Rule of multiplication: 00:03:00 •Bayes Theorem: 00:10:00 •Bayes Theorem - Practical example: 00:07:00 •Expected value: 00:11:00 •Practice: Expected value: 00:01:00 •Solution: Expected value: 00:03:00 •Law of Large Numbers: 00:08:00 •Central Limit Theorem - Theory: 00:10:00 •Central Limit Theorem - Intuition: 00:08:00 •Central Limit Theorem - Challenge: 00:11:00 •Central Limit Theorem - Exercise: 00:02:00 •Central Limit Theorem - Solution: 00:14:00 •Binomial distribution: 00:16:00 •Poisson distribution: 00:17:00 •Real life problems: 00:15:00 •Intro: 00:01:00 •What is a hypothesis?: 00:19:00 •Significance level and p-value: 00:06:00 •Type I and Type II errors: 00:05:00 •Confidence intervals and margin of error: 00:15:00 •Excursion: Calculating sample size & power: 00:11:00 •Performing the hypothesis test: 00:20:00 •Practice: Hypothesis test: 00:01:00 •Solution: Hypothesis test: 00:06:00 •T-test and t-distribution: 00:13:00 •Proportion testing: 00:10:00 •Important p-z pairs: 00:08:00 •Intro: 00:02:00 •Linear Regression: 00:11:00 •Correlation coefficient: 00:10:00 •Practice: Correlation: 00:02:00 •Solution: Correlation: 00:08:00 •Practice: Linear Regression: 00:01:00 •Solution: Linear Regression: 00:07:00 •Residual, MSE & MAE: 00:08:00 •Practice: MSE & MAE: 00:01:00 •Solution: MSE & MAE: 00:03:00 •Coefficient of determination: 00:12:00 •Root Mean Square Error: 00:06:00 •Practice: RMSE: 00:01:00 •Solution: RMSE: 00:02:00 •Multiple Linear Regression: 00:16:00 •Overfitting: 00:05:00 •Polynomial Regression: 00:13:00 •Logistic Regression: 00:09:00 •Decision Trees: 00:21:00 •Regression Trees: 00:14:00 •Random Forests: 00:13:00 •Dealing with missing data: 00:10:00 •ANOVA - Basics & Assumptions: 00:06:00 •One-way ANOVA: 00:12:00 •F-Distribution: 00:10:00 •Two-way ANOVA - Sum of Squares: 00:16:00 •Two-way ANOVA - F-ratio & conclusions: 00:11:00 •Wrap up: 00:01:00 •Assignment - Statistics & Probability for Data Science & Machine Learning: 00:00:00

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions.