How to find a job with R skills

1. Programming for Data Science with R

Prepare for a data science career by learning the fundamental data programming tools: R, SQL, command line, and git...

2. Introduction to R: Basic R syntax

This guided project is for beginners interested in taking their first steps with coding in the statistical language R. It assumes no previous knowledge of R, introduces the RStudio environment, and covers basic concepts, tools, and general syntax. By the end of the exercise, learners will build familiarity with RStudio and the fundamentals of the statistical coding language R...

3. Advanced R

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This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries.  After that we introduce the necessary skills for profiling your R code. We then move into C++ and Rcpp, and we show how to write super fast C++ parallel code that uses OpenMP. Understanding and mastering Rcpp will allow you to push your R skills to another dimension. When your colleagues are writing R functions, you will be able to get Rcpp+OpenMP equivalent code running 4-8X times faster. We then move into Python and Java, and show how these can be called from R and vice-versa. This will be really helpful for writing code that leverages the excellent object oriented features from this pair of languages. You will be able to build your own classes in Java or Python that store the data that you get from R. Since the Python community is growing so fast, and producing so wonderful packages, it's great to know that you will be able to call any function from any Python package directly from R. We finally explain how to use sqldf, which is a wonderful package for doing serious, production grade data processing in R. Even though it has its limitations, we will be able to write SQL queries directly in R. We will certainly show how to bypass those limitations, such as its inability to write full joins using specific tricks.  All the code (R, JAVA, C++,. csv) used in this course is available for download, and all the lectures can be downloaded as well. Our teaching strategy is to present you with examples carrying the minimal complexity, so we hope you can easily follow each lecture. In case you have doubts or comments, feel free to send us a message...

4. R Programming

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Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of "R for Data Science" is to help you learn the most important tools in R that will allow you to do data science. After going through this course, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R. What you will learnData science is a huge field, and there's no way you can master it by going through a single course. The goal of this course is to give you a solid foundation in the most important toolsFirst, you must import your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it! Once you've imported your data, it is a good idea to tidy it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions. Once you have tidy data, a common first step is to transform it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called wrangling, because getting your data in a form that's natural to work with often feels like a fight! Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualization and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times. Prerequisites: You should be generally numerically literate, and it's helpful if you have some programming experience already. Testimonials: i really need this type of teaching style.. this is superb ~ Nitish kumar giriIt dives right into advanced R concepts related to Data Science ~ Rainer RodriguesI am into revision.. its good. ~ Jagannath ChaudharyHonestly it's a good match for me and I'm hoping to know more ~ Salim AdamsIt was a good experience. Find it really helpful. ~ Shafia AminIts is really helpful for R programming building. ~ Muhammad Nazimclass was really informative and got new learning experience. ~ Gayathry Harilal...

5. Data Analysis with R

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis...

6. Data Analysis with R

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM...

7. Data Visualization with R

In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web. You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Watch the videos, work through the labs, and watch your data science skill grow. Good luck! NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills...

8. R Crash Course - Learn R-programming in 2 hours: R & RStudio

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'R Crash Course - a short and concise introduction to R and R Studio, R-programming for the Beginners'This is an R crash course for anyone who previously had no or very little contact with script-based programming in R. The main goal is to establish the basic understanding needed for more advanced courses that use the R language, RStudio, and R-programming for example, for data science, machine learning, or statistical analysis in R. This is also a baseline course that I will recommend to my students to take to refresh their knowledge on learning R-programming language for my upcoming data science courses in R. The best about this course that is in a very concise manner (2 hours!) you will be able to learn all the fundamentals of R-programming that will enable you to get started with R! What will you learn in this course:§ Package Management§ Calculate with R§ Variables§ Vectors§ Matrices§ Lists§ Data frames§ Missing values§ Functions§ Control Structures§ For loopsAll the R-scripts used in this course will be also provided to you. The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field. This course is NOT for you if you an intermediate or advanced user of R and don't need an introduction to R programming! Let's get started!...

9. R Programming - R Language for Absolute Beginners

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So, you've decided that you want to learn R or you want to get familiar with it, but don't know where to start? Or are you a data/business analyst or data scientist that wants to have a smooth transition into R programming?Then, this course was designed just for you! This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups: Introductory slides lectures with the most well-known commands for each type of R object. Code along lectures where you will see how we can implement the stuff we will learn! Test your knowledge with questions and practical exercises with different levels of difficulty! Analyze real datasets and understand the thought process from question to R code solution! This course was designed to be focused on the practical side of coding in R - instead of teaching you every function and method out there, I'll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects.  At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis. Here are some examples of things you will be able to do after finishing the course: Load CSV and Excel files into R;Do interesting line plots that enable you to draw conclusions from data. Plot histograms of numerical data. Create your own functions that will enable you to reutilize code. Slice and dice Data Frames, subsetting data for specific domains. Join thousands of professionals and students in this R journey and discover the amazing power of this statistical open-source language. This course will be constantly updated based on students feedback...

10. R For Beginners: Learn R Programming from Scratch

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Hi there, Welcome to my "R For Beginners: Learn R Programming from Scratch" course. R, r programming, r language, data science, machine learning, r programming language, r studio, data analytics, statistics, data science, data mining, machine learningR Programming in R and R Studio, analyze data with R (programming language) and become professional at data miningMachine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated OAK Academy's instructor will give you a leg up in either industry. R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know. In this course, you will learn how to code with R Programming Language, manage and analyze data with R programming and report your findings. R programming language is a leading data mining technology. To learn data science, if you don't know which high return programming language to start with. The answer is R programming. This R programming course is for: Students in statistical courses R (programming language), Analysts who produce statistical reports, Professional programmers on other languages, Academic researchers developing the statistical methodology, Specialists in the various area who need to develop sophisticated graphical presentations of data, and anyone who is particularly interested in big data, machine learning and data intelligence. No Previous Knowledge is needed! This course will take you from a beginner to a more advanced level. If you are new to data science, no problem, you will learn anything you need to start with R. If you are already used to r statics and you just need a refresher, you are also in the right place. Here is the list of what you'll learn by the end of the course,· Installation for r programming language· R Console Versus R Studio· R and R Studio Installation in r shiny· Basic Syntax in r statistics· Data Types in R shiny· Operators and Functions in R· R Packages in data analytics· Managing R Packages in r language· Data Management in R· Getting Data into R in machine learning· Computation and Statistics in data science· Hands-on Projects Experimental Learning in r programmingR programming languageRR languageAfter every session, you will have a strong set of skills to take with you into your Data Science career. So, This is the right course for anyone who wants to learn R from scratch or for anyone who needs a refresher. Fresh ContentWhat is R and why is it useful?The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation. R is also a popular language for data science projects. Much of the data used for data science can be messy and complex. The programming language has features and libraries available geared toward cleaning up unorganized data and making complex data structures easier to handle that can't be found in other languages. It also provides powerful data visualization tools to help data scientists find patterns in large sets of data and present the results in expressive reports. Machine learning is another area where the R language is useful. R gives developers an extensive selection of machine learning libraries that will help them find trends in data and predict future events. What careers use R?R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R language to glean answers from data. R is also widely used in market research and advertising to analyze the results of marketing campaigns and user data. The language is used in quantitative analysis, where its data analysis capabilities give financial experts the tools they need to manage portfolios of stocks, bonds, and other assets. Data scientists use R in many industries to turn data into insights and predict future trends with its machine learning capabilities. Data analysts use R to extract data, analyze it, and turn it into reports that can help enterprises make better business decisions. Data visualization experts use R to turn data into visually appealing graphs and charts. Is R difficult to learn?Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts who may have no coding experience. For some beginning users, it is relatively simple to learn R. It can have a learning curve if you are a business analyst who is only familiar with graphical user interfaces since R is a text-based programming language. But compared to other programming languages, users usually find R easier to understand. R also may have an unfamiliar syntax for programmers who are used to other programming languages, but once they learn the syntax, the learning process becomes more straightforward. Beginners will also find that having some knowledge of mathematics, statistics, and probabilities makes learning R easier. It's no secret how technology is advancing at a rapid rate. New tools are released every day, and it's crucial to stay on top of the latest knowledge. You will always have up-to-date content to this course at no extra charge. What is Python?Python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks. Python vs. R: what is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R, you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance. Video and Audio Production QualityAll our contents are created/produced as high-quality video/audio to provide you with the best learning experience. You will be,· Seeing clearly· Hearing clearly· Moving through the course without distractionsYou'll also get: Lifetime Access to The CourseFast & Friendly Support in the Q & A sectionUdemy Certificate of Completion Ready for DownloadDive in now! R For Beginners: Learn R Programming from ScratchWe offer full support, answering any questions. See you in the course!...

11. Data Science with R and Python R Programming

4.4

(121)

Welcome to Data Science with R and Python R Programming course. Python and r, r and python, python, r programming, python data science, data science, data science with r, r python, python r, data science with r and python, data science course, Python and R programming! Learn data science with R & Python all in one course. You'll learn NumPy, Pandas, and moreOAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you're interested in machine learning, data mining, or data analysis, Udemy has a course for you. Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate. python programming, oak academy, data literacy, python and r programming, data science python, python r data, data science r, python and r for data science, data transformation, python & r, python data science, python for data science, python r programming, data science python, pandas, r data science, r and python programming, r course, data science r and python, NumPy, python r data science, data science in r, data science with python and r, python with r, r studio, programming, r courses, programming for data sciencePython instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels. Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks. Machine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated OAK Academy instructor will give you a leg up in either industry. R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know. Ready for a Data Science career? Are you curious about Data Science and looking to start your self-learning journey into the world of data?Are you an experienced developer looking for a landing in Data Science! In both cases, you are at the right place! The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open-source. R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential. With my full-stack Data Science course, you will be able to learn R and Python together. If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming. But do not worry! In this course, you will have a chance to learn both and will decide to which one fits your niche! Throughout the course's first part, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulating it, and producing meaningful outcomes. Throughout the course's second part, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Python for Data Science course. We will open the door of the Data Science world and will move deeper.  You will learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step. Then, we will transform and manipulate real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages. At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, and group by and summarize your data simultaneously. In this course you will learn;How to use Anaconda and Jupyter notebook, Fundamentals of Python such asDatatypes in Python, Lots of datatype operators, methods, and how to use them, Conditional concept, if statementsThe logic of Loops and control statementsFunctions and how to use themHow to use modules and create your own modulesData science and Data literacy conceptsFundamentals of Numpy for Data manipulation such asNumpy arrays and their featuresHow to do indexing and slicing on ArraysLots of stuff about Pandas for data manipulation such asPandas series and their featuresDataframes and their featuresHierarchical indexing concept and theoryGroupby operationsThe logic of Data MungingHow to deal effectively with missing data effectivelyCombining the Data FramesHow to work with Dataset filesAnd also you will learn fundamentals thing about the Matplotlib library such asPyplot, Pylab and Matplotlb conceptsWhat Figure, Subplot, and Axes areHow to do figure and plot customizationExamining and Managing Data Structures in RAtomic vectors Lists ArraysMatricesData framesTibblesFactorsData Transformation in RTransform and manipulate a deal dataTidyverse and morePython and rR programmingdata sciencedata science with rr pythondata science with r and pythonpython r programmingnumpy pythonpython r data sciencepython data scienceAnd we will do many exercises.  Finally, we will also have 4 different final projects covering all of Python subjects. What is data science?We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science python uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Python data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science using python includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a python for data science, it progresses by creating new algorithms to analyze data and validate current methods. What does a data scientist do?Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production. What are the most popular coding languages for data science?Python for data science is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up. How long does it take to become a data scientist?This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, How can I gauge whether I know enough to become a data scientist? Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field. How can ı learn data science on my own?It is possible to learn data science projects on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Udemy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated. Does data science require coding?The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree. A lot of algorithms have been developed and optimized in the field. You could argue that it is more important to understand how to use the algorithms than how to code them yourself. As the field grows, more platforms are available that automate much of the process. However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills. The data scientist role is continuing to evolve, so that might not be true in the future. The best advice would be to find the path that fits your skillset. What skills should a data scientist know?A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python - although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings. Is data science a good career?The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science from scratch is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds. What is python?Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks. Python vs. R: What is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R in data science , you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance. What does it mean that Python is object-oriented?Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping. What are the limitations of Python?Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant. How is Python used?Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library. What jobs use Python?Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems. How do I learn Python on my own?Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy's online courses are a great place to start if you want to learn Python on your own. What is R and why is it useful?The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation. R is also a popular language for data science projects. Much of the data used for data science can be messy and complex. The programming language has features and libraries available geared toward cleaning up unorganized data and making complex data structures easier to handle that can't be found in other languages. It also provides powerful data visualization tools to help data scientists find patterns in large sets of data and present the results in expressive reports. Machine learning is another area where the R language is useful. R gives developers an extensive selection of machine learning libraries that will help them find trends in data and predict future events. What careers use R?R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R language to glean answers from data. R is also widely used in market research and advertising to analyze the results of marketing campaigns and user data. The language is used in quantitative analysis, where its data analysis capabilities give financial experts the tools they need to manage portfolios of stocks, bonds, and other assets. Data scientists use R in many industries to turn data into insights and predict future trends with its machine learning capabilities. Data analysts use R to extract data, analyze it, and turn it into reports that can help enterprises make better business decisions. Data visualization experts use R to turn data into visually appealing graphs and charts. Is R difficult to learn?Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts who may have no coding experience. For some beginning users, it is relatively simple to learn R. It can have a learning curve if you are a business analyst who is only familiar with graphical user interfaces since R is a text-based programming language. But compared to other programming languages, users usually find R easier to understand. R also may have an unfamiliar syntax for programmers who are used to other programming languages, but once they learn the syntax, the learning process becomes more straightforward. Beginners will also find that having some knowledge of mathematics, statistics, and probabilities makes learning R easier. Python vs. R: What is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R, you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance. What does it mean that Python is object-oriented?Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping. The concept of combining data with functionality in an object is called encapsulation, a core concept in the object-oriented programming paradigm. Why would you want to take this course?Our answer is simple: The quality of teaching. When you enroll, you will feel the OAK Academy's seasoned instructors' expertise. Fresh Content It's no secret how technology is advancing at a rapid rate and it's crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest data science trends. Video and Audio Production QualityAll our content is created/produced as high-quality video/audio to provide you the best learning experience. You will be, Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get: Lifetime Access to The CourseFast & Friendly Support in the Q & A sectionUdemy Certificate of Completion Ready for DownloadDive in now! Data Science with R and Python R ProgrammingWe offer full support, answering any questions. See you in the course!...

12. R Level 1 - Data Analytics with R

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Are you new to R? Do you want to learn more about statistical programming? Are you in a quantitative field? You just started learning R but you struggle with all the free but unorganized material available elsewhere? Do you want to hack the learning curve and stay ahead of your competition? If your answer is YES to some of those points - read on! This Tutorial is the first step - your Level 1 - to R mastery. All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered. While planing this course I used the Pareto 80/20 principle. I filtered for the most useful items in the R language which will give you a quick and efficient learning experience. Learning R will help you conduct your projects. On the long run it is an invaluable skill which will enhance your career. Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used for looping. In the graphics section you will learn how to create and tailor your graphs. As an example we will create boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation. With the R Commander you will also learn about an alternative to RStudio. Especially for classic hypthesis tests the R Coomander GUI can save you some time. According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. Furthermore you can also check out the r-tutorials R exercise database over at our webpage. In the database you will find more exercises on the topics of this course. You can download the code pdf of every section to try the presented code on your own. This tutorial is your first step to benefit from this open source software. What R you waiting for? Martin...

13. Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques...

14. Data Science: Foundations using R

Ask the right questions, manipulate data sets, and create visualizations to communicate results.\n\nThis Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.\n\nThe five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning...

15. Data Visualization & Dashboarding with R

This Specialization is intended for learners seeking to develop the ability to visualize data using R. Through five courses, you will use R to create static and interactive data visualizations and publish them on the web, which will you prepare you to provide insight to many types of audiences...

16. Mastering Software Development in R

R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.\n\nThis Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.\n\nThis Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts...

17. Advanced Data Visualization with R

Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. This course is the third in the Specialization "Data Visualization and Dashboarding in R." Learners come into this course with a foundation using R to make many basic kinds of visualization, primarily with the ggplot2 package. Accordingly, this course focuses on expanding the learners' inventory of data visualization options. Drawing on additional packages to supplement ggplot2, learners will made more variants of traditional figures, as well as venture into spatial data. The course ends make interactive and animated figures. To fill that need, this course is intended for learners who have little or no experience with R but who are looking for an introduction to this tool. By the end of this course, students will be able to import data into R, manipulate that data using tools from the popular tidyverse package, and make simple reports using R Markdown. The course is designed for students with good basic computing skills, but limited if any experience with programming...

18. Data Analysis with R Programming

This course is the seventh course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. This course will also cover the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, you will: - Examine the benefits of using the R programming language. - Discover how to use RStudio to apply R to your analysis. - Explore the fundamental concepts associated with programming in R. - Explore the contents and components of R packages including the Tidyverse package. - Gain an understanding of dataframes and their use in R. - Discover the options for generating visualizations in R. - Learn about R Markdown for documenting R programming...

19. Exploratory Data Analysis in R

In this 1-hour long project-based course, you will learn how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...