Register on the R Programming for Data Science today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The R Programming for Data Science is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The R Programming for Data Science Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) 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 Certification 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. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the R Programming for Data Science, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content 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? 00:01:00 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
About Course Data Science and Data Analytics with Python: A Comprehensive Course for Beginners Unlock the power of data and gain insights that drive informed decisions with this comprehensive course on data science and data analytics with Python. This course is designed for beginners of all skill levels, with no prior programming experience required. You will learn the essential skills to embark on your data-driven journey, including: Data manipulation with NumPy and Pandas Data visualization with Matplotlib and Seaborn Statistical analysis with Python Machine learning and artificial intelligence You will also gain hands-on experience with real-world data projects, allowing you to apply your newfound knowledge to solve real-world problems. By the end of this course, you will be able to: Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems This course is the perfect launchpad for your data science journey. Whether you are looking to pivot your career, enhance your skill set, or simply quench your curiosity, this course will give you the foundation you need to succeed. Enroll today and start exploring the fascinating world of data science together! What Will You Learn? Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems Course Content Introduction to Python Data Science Introduction to Python Data Science Environment Setup Data Cleaning Packages Working with the Numpy package Working with Pandas Data science package Data Visualization Packages Working with Matplotlib Data Science package (Part - 1) Working with Matplotlib Data Science (Part - 2) A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsBeginners level knowledge for working with Data .Programming knowledge not required. Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics
A course by Sekhar Metla IT Industry Expert RequirementsNo programming experience needed. You will learn everything you need to knowNo software is required in advance of the course (all software used in the course is free)No pre-knowledge is required - you will learn from basic Audience Beginner JavaScript, Python and MSSQL developers curious about data science development Anyone who wants to generate new income streams Anyone who wants to build websites Anyone who wants to become financially independent Anyone who wants to start their own business or become freelance Anyone who wants to become a Full stack web developer Audience Beginner JavaScript, Python and MSSQL developers curious about data science development Anyone who wants to generate new income streams Anyone who wants to build websites Anyone who wants to become financially independent Anyone who wants to start their own business or become freelance Anyone who wants to become a Full stack web developer
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Career path Some of the career paths related to this field are: Data Analyst: £25,000 - £45,000 per year Business Intelligence Analyst: £30,000 - £55,000 per year Data Scientist: £40,000 - £70,000 per year Analytics Manager: £45,000 - £80,000 per year Statistical Analyst: £30,000 - £55,000 per year Market Research Analyst: £25,000 - £45,000 per year Data Consultant: £35,000 - £70,000 per year Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - £9.99 Unlock your potential and showcase your accomplishments with our CPD Quality Standards certificates! Upon successful completion of the course, learners can obtain a CPD Quality Standards PDF certificate for Statistics absolutely free! Upon finishing Data Analytics and Business Intelligence Analyst, you'll have the opportunity to obtain valuable proof of your achievement. 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