979 Data Science courses delivered Online

Overview This comprehensive course on Quick Data Science Approach from Scratch will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Quick Data Science Approach from Scratch 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 Quick Data Science Approach from Scratch. It is available to all students, of all academic backgrounds. Requirements Our Quick Data Science Approach from Scratch 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 • 15 lectures • 01:00:00 total length •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 •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 •Problem Definition: 00:05:00 •Algorithms: 00:14:00 •Prediction: 00:03:00 •Learning Methods: 00:05:00 •Assignment - Quick Data Science Approach from Scratch: 00:00:00

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.

Get ready for an exceptional online learning experience with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Big Data Analysis, Data Science, Fintech & Python for Data Analyst is a dynamic package, that blends the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analysis package has something for everyone. As part of the Big Data Analysis, Data Science, Fintech & Python for Data Analyst package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analysis bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This bundle comprises the following courses: CPD Quality Standards Courses: Big Data Analytics with PySpark Power BI and MongoDB Big Data Analytics with PySpark Tableau Desktop and MongoDB Building Big Data Pipelines with PySpark MongoDB and Bokeh Develop Big Data Pipelines with R & Sparklyr & Tableau Develop Big Data Pipelines with R, Sparklyr & Power BI Learn Python, JavaScript, and Microsoft SQL for Data Science SQL for Data Science, Data Analytics, and Data Visualization Excel Data Analysis Introduction to Data Analytics with Tableau Business and Data Analytics for Beginners Google Data Studio: Data Analytics Basic Data Analysis FinTech Learning Outcome: Gain comprehensive insights into multiple fields. Foster critical thinking and problem-solving skills across various disciplines. Understand industry trends and best practices through the Data Analysis Bundle. Develop practical skills applicable to real-world situations. Enhance personal and professional growth with Data Analysis. Build a strong knowledge base in your chosen course via Data Analysis. Benefit from the flexibility and convenience of online learning. With the Data Analysis package, validate your learning with a CPD certificate. Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst, a rich anthology of 15 diverse courses. Each course in the Data Analysis bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisBig Data Analysis, Data Science, Fintech & Python for Data Analyst bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis package your trusted companion in your lifelong learning journey. CPD 35 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Career path Unleash your potential with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Data Analysis bundle package. Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - Included You will get a complimentary Hard Copy Certificate.

Become a data scientist with the in-demand skills of Python, JavaScript, and Microsoft SQL. This comprehensive course will teach you the essential skills you need to succeed.

Are you ready to embark on an enlightening journey of wisdom with the Data Science bundle, and pave your way to an enriched personal and professional future? If so, then Step into a world of knowledge with our course bundle - Data Science with Python. Delve into eight immersive CPD Accredited courses, each a standalone course: Python from Scratch Quick Data Science Approach from Scratch Python Data Science with Numpy, Pandas and Matplotlib Complete Python Machine Learning & Data Science Fundamentals SQL for Data Science, Data Analytics and Data Visualization Data Science & Machine Learning with R from A-Z Statistics & Probability for Data Science & Machine Learning R Programming for Data Science Traverse the vast landscapes of theory, unlocking new dimensions of understanding at every turn. Let the Data Science with Python bundle illuminate your path to wisdom. The Data Science with Python bundle offers a comprehensive exploration into a rich tapestry of vast knowledge across eight carefully curated courses. The journey is designed to enhance your understanding and critical thinking skills. Each course within the bundle provides a deep-dive into complex theories, principles, and frameworks, allowing you to delve into the nuances of the subject matter at your own pace. In the framework of the Data Science with Python package, you are bestowed with complimentary PDF certificates for all the courses included in this bundle, all without any additional charge. Adorn yourself with the Data Science bundle, empowering you to traverse your career trajectory or personal growth journey with self-assurance. Register today and ignite the spark of your professional advancement! So, don't wait further and join the Data Science with Python community today and let your voyage of discovery begin! Learning Outcomes: Attain a holistic understanding in the designated areas of study with the Data Science bundle. Establish robust bases across each course nestled within the Data Science bundle. Decipher intricate concepts through the articulate content of the Data Science bundle. Amplify your prowess in interpreting, scrutinising, and implementing theories. Procure the capacity to engage with the course material on an intellectual and profound level. Become proficient in the art of problem-solving across various disciplines. Stepping into the Data Science bundle is akin to entering a world overflowing with deep theoretical wisdom. Each course within this distinctive bundle is an individual journey, meticulously crafted to untangle the complex web of theories, principles, and frameworks. Learners are inspired to explore, question, and absorb, thus enhancing their understanding and honing their critical thinking skills. Each course invites a personal and profoundly enlightening interaction with knowledge. The Data Science bundle shines in its capacity to cater to a wide range of learning needs and lifestyles. It gives learners the freedom to learn at their own pace, forging a unique path of discovery. More than just an educational journey, the Data Science bundle fosters personal growth, enabling learners to skillfully navigate the complexities of the world. The Data Science bundle also illuminates the route to a rewarding career. The theoretical insight acquired through this bundle forms a strong foundation for various career opportunities, from academia and research to consultancy and programme management. The profound understanding fostered by the Data Science bundle allows learners to make meaningful contributions to their chosen fields. Embark on the Data Science journey and let knowledge guide you towards a brighter future. CPD 80 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals keen on deepening their firm understanding in the respective fields. Students pursuing higher education looking for comprehensive theory modules. Professionals seeking to refresh or enhance their knowledge. Anyone with a thirst for knowledge and a passion for continuous learning. Career path Armed with the Data Science bundle, your professional journey can reach new heights. The comprehensive theoretical knowledge from this bundle can unlock diverse career opportunities across several fields. Whether it's academic research, consultancy, or programme management, the Data Science bundle lays a solid groundwork. Certificates CPD Certificate Of Completion Digital certificate - Included 8 Digital Certificates Are Included With This Bundle CPD Quality Standard Hardcopy Certificate (FREE UK Delivery) Hard copy certificate - £9.99 Hardcopy Transcript - £9.99

Overview This comprehensive course on R Programming for Data Science will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This R Programming for Data Science 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 R Programming for Data Science. It is available to all students, of all academic backgrounds. Requirements Our R Programming for Data Science 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 23 sections • 129 lectures • 06:25:00 total length •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?: 00:01:00 •Engine and coding environment: 00:03:00 •Installing R and RStudio: 00:04:00 •RStudio: A quick tour: 00:04:00 •Arithmetic with R: 00:03:00 •Variable assignment: 00:04:00 •Basic data types in R: 00:03:00 •Creating a vector: 00:05:00 •Naming a vector: 00:04:00 •Vector selection: 00:06:00 •Selection by comparison: 00:04:00 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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 •Mathematical functions: 00:05:00 •Data Utilities: 00:08:00 •Additional Materials: 00:00:00 •grepl & grep: 00:04:00 •Metacharacters: 00:05:00 •sub & gsub: 00:02:00 •More metacharacters: 00:04:00 •Additional Materials: 00:00:00 •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 •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 •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 •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 - R Programming for Data Science: 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

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

Overview This comprehensive course on Data Science & Machine Learning with R will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with R 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 Data Science & Machine Learning with R. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with R 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 10 sections • 69 lectures • 22:07:00 total length •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: 00:11:00 •Basics: 00:06:00 •Files: 00:11:00 •RStudio: 00:07:00 •Tidyverse: 00:05:00 •Resources: 00:04:00 •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 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 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 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 with R Markdown: 00:29:00 •Introduction to R Shiny: 00:26:00 •A Basic R Shiny App: 00:31:00 •Other Examples with R Shiny: 00:34:00 •Machine Learning Part 1: 00:22:00 •Machine Learning Part 2: 00:47:00 •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

Description Data Science Diploma Introducing the Data Science Diploma, an online course tailored for those eager to step into the dynamic world of data science. This comprehensive programme ensures participants grasp the essence of contemporary data science techniques, tools, and theories. At the core of this Data Science Diploma is the module titled Foundations of Data Science. It sets the groundwork by instilling fundamental principles, thereby preparing learners to navigate the expansive sea of data efficiently and effectively. As one progresses, the intricate elements of Data Engineering and Big Data come into play, elucidating how vast amounts of data are managed, stored, and processed. An essential aspect of data science lies in understanding uncertainty and making informed decisions. To this end, Probability and Statistics in Data Science offers learners the tools to decipher patterns, predict trends, and make data-driven decisions. Following closely, Clustering and Classification Techniques provide a deep understanding of how to categorise data into specific groups based on inherent characteristics, paving the way for more precise analysis. But what's data science without the necessary mathematical prowess? The Advanced Mathematical Modeling module hones this skill, enabling learners to craft intricate models that can simulate real-world scenarios. Such models act as the backbone of various data analyses and offer a detailed understanding of the underlying processes. The saying, 'A picture is worth a thousand words,' holds especially true in data science. With the Data Visualisation Principles and Design module, learners are equipped with the knowledge to translate complex data into visually compelling stories. This understanding is further solidified with the Web-Based Data Visualisation Tools, offering hands-on experience in using cutting-edge tools to portray data visually. The course recognises the growing demand for intuitive dashboards that provide real-time insights. The Dashboard Design and Mapping module aids participants in creating interactive and user-friendly dashboards, ensuring stakeholders get a clear and concise view of the data. Yet, as one manoeuvres through these diverse modules, a foundational understanding of computing becomes paramount. Hence, Computing for Data Science takes centre stage, familiarising learners with the computational aspects of data analysis, from algorithms to data structures. Concluding the Data Science Diploma is the module on Domain-Specific Data Science Applications. This segment offers a glimpse into how data science principles are applied across different sectors, from healthcare to finance. It accentuates the versatility of data science, proving its indispensable nature in today's digitised world. To sum up, this online Data Science Diploma ensures a holistic understanding of data science. By intertwining theory with practical application, it equips learners with the skill set required to thrive in the data-driven industries of tomorrow. So, if the realm of data beckons you, this diploma is your gateway to excellence. What you will learn 1:Foundations of Data Science 2:Data Engineering and Big Data 3:Probability and Statistics in Data Science 4:Clustering and Classification Techniques 5:Advanced Mathematical Modeling 6:Data Visualisation Principles and Design 7:Web-Based Data Visualisation Tools 8:Dashboard Design and Mapping 9:Computing for Data Science 10:Domain-Specific Data Science Applications 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.