Data Science course will allow the students to learn, in detail, the fundamentals of the R language and additionally master some of the most efficient libraries for data visualization in chart, graph, and map formats.
The R Project for Statistical Computing, or simply named R, is a free software environment for statistical computing and graphics. It is also a programming language that is widely used among statisticians and data miners for developing statistical software and data analysis. Over the last few years, they were joined by enterprises who discovered the potential of R, as well as technology vendors that offer R support or R-based products.
Although there are other programming languages for handling statistics, R has become the de facto language of statistical routines, offering a package repository with over 6400 problem-solving packages. It also offers versatile and powerful plotting. It also has the advantage of treating tabular and multidimensional data as a labeled, indexed series of observations. This is a game changer over typical software which is just doing 2D layout, like Excel.
In this module we will be introduced to R and R RStudio.
This module will teach you fundamental techniques for how to use the readr package to load external data from a CSV file into R, the dplyr package to massage and manipulate data, and ggplot2 to visualize data. You’ll also learn how to install and the tidyverse ecosystem of packages and load the packages into the RStudio environment.
In this module, we’ll examine a number of functions that can be used to read data. There are a number of common data formats that can be read into and out of R. This includes text files in formats such as csv, txt, html, and json. It also includes files output from statistical applications including SAS and SPSS. Online resources including web services and HTML pages can also be read into R. Finally, relational and non-relational database tables can be read as well. There are a number of functions provided by R and Tidyverse which will enable you to read these various sources.
Before a dataset can be analyzed in R it often needs to be manipulated or transformed in various ways. The dplyr package, part of the larger tidyverse package, provides a set of functions that allow you to transform a dataset in various ways. The dplyr package is a very important part of tidyverse since the functions provided through this package are used so frequently to transform data in different ways prior to doing more advanced data exploration, visualization, and modeling.
Many datasets require some sort of tidying before you can begin your analysis. The first step is to figure out what the variables and observations are for the dataset. This will facilitate your understanding of what the columns and rows should be. In addition, you will also need to resolve one or two common problems. You will need to figure out if one variable is spread across multiple columns, and you will need to figure out if one observation is scattered across multiple rows. These concepts are known as gathering and spreading. We’ll examine these concepts further in the exercises in this module.
Data can generally be divided into categorical or continuous types. Categorical variables consist of a small set of values, while continuous variables have a potentially infinite set of ordered values. Categorical variables are often visualized with bar charts, and continuous variables with histograms. Both categorical and continuous data can be represented through various charts created with R. In this module, we will study about basic data exploration techniques in R.
The ggplot2 package is a library that enables the creation of many types of data visualization including various types of charts and graphs. This library was first created by Hadley Wickham in 2005 and is an R implementation of Leland Wilkinson’s Grammar of Graphics. The idea behind this package is to specify plot building blocks and then combine them to create a graphical display. Building blocks of ggplot2 include data, aesthetic mapping, geometric objects, statistical transformations, scales, coordinate systems, position adjustments, and faceting. In this module, you will learn about the basic data visualization techniques.
The ggmappackage enables the visualization of spatial data and spatial statistics in a map format using the layered approach of ggplot2. This package also includes basemaps that give your visualizations context including Google Maps, Open Street Map, Stamen Maps, and CloudMade maps. In addition, utility functions are provided for accessing various Google services including Geocoding, Distance Matrix, and Directions.
In this module, we will learn how to visualize geographic data with ggmap.
R Markdown is an authoring framework for data science that combines code, results, and commentary. Output formats include PDF, Word, HTML, slideshows, and more. An R Markdown document essentially serves three purposes: communication, collaboration, and as a modern-day lab environment that captures not only what you did, but also what you were thinking. From a communication perspective it enables decision makers to focus more on the results of your analysis rather than the code. However, because it enables you to also include the code, it functions as a means of collaboration between data scientists.
In this module, you will learn everything there is to know about R Markdown.
Structure your learning and get a certificate to prove it.
Today, data science is an indispensable tool for any organization, allowing for the analysis and optimization of decisions and strategy. R has become the preferred software for data science, thanks to its open-source nature, simplicity, applicability to data analysis, and the abundance of libraries for any type of algorithm.
University of Emerging Technologies’ Data Science with R - Level 1 (Essentials) Course will allow the students to learn, in detail, the fundamentals of the R language and additionally master some of the most efficient libraries for data visualization in chart, graph, and map formats. The students will learn the language and applications through real-life examples and practice.
Total Duration of the course is 160 hours
University of Emerging Technologies provides you with Role based education, experiential learning, live classes, 24*7 live labs and live support, personalized machines, real life projects, industry oriented, job focused content along with career prep support.
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
Developers aspiring to be a 'Data Scientist'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'R' professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies
Students/Freshers who want make their career in Data Science
Using the tidyverse package for data loading, transformation, and visualization
Get a tour of the most important data structures in R
Learn techniques for importing data, manipulating data, performing analysis, and producing useful data visualization
Data visualization techniques with ggplot2
Geographic visualization and maps with ggmap
urning your analyses into high quality documents, reports, and presentations with R Markdown.
Hands on case studies designed to replicate real world projects
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges
There is no specific pre-requisite for Data Science Training. However, a basic understanding of R can be beneficial.
University of Emerging Technologies' Data Science With R Course Completion Certificate is awarded by The Emerging Tech Foundation, an Independent Not-for profit organisation globally recognised for the emerging technologies.
You will be working on the virtual live lab environment that we provide which will give you the access to all the tools and softwares required for this particular course. The stepwise guide for accessing these services will be available in the LMS and University of Emerging Technologies support team will assist you 24*7 in case you have any doubts.
This course includes eight assignment projects which will hone your skills as per current industry standards and prepare you for your future career needs.
The 2 industry-based certification projects will test your ability to work with real-world data set.
Your certification project is an opportunity for you to explore an interesting problem of your choice in the context of a real-world data set. Projects can be done by you as an individual, or in teams of 2-4 students. Educators and Academic Enablers will consult with you on your ideas, but of course the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 20% of your final class grade, and will have 4 deliverables:
Proposal: 1 page (10%)
Midway Report: 3-4 pages (20%)
Final Report: 5-6 pages (40%)
Poster Presentation: (30%)
In this course, you will learn about scenario-based examples and have hands-on experience to be able to utilize the tools and prompts.
Any computer with standard Windows and or Mac with at least 2 GB RAM and a Core-I3 processor
Total duration of this course is 160 hours divided over a period of 7-8 weeks. Out of 160 hours, 60-80 hours are dedicated for online sessions and remaining for live practical sessions where you will be working on real life industry focused projects.
You will be spending a minimum of 12 hours for online sessions every week.
Using your LMS, you will always have access to the recorded sessions. And you can also make a special request to attend the live session in some other batch (on the basis of availability).
Virtual Lab is a cloud-based environment where you can execute all your practicals and assignments, work on real-life projects effortlessly.
Using these virtual labs, students can avail the various tools for learning, including additional resources and environment for the course. This will save students from all the hassle of downloading and maintaining these softwares in their own machine.
You’ll be able to access the virtual lab via your browser which requires minimum hardware configurations. If you are stuck somewhere, our support team is available 24*7 to help you out.
All the details to access virtual labs are available on you LMS.
You can interact with the educator during the class using the chat feature.
We provide 24*7 live support to all our students via live chat feature and email. Our academic enablers are always available to help you throughout the course.
Yes, you can interact with other students enrolled in the same course using the course forum where you can discuss about the class and the course material. In case you want to interact with students enrolled in some other course, you can do that using the common forum available for all. University of Emerging Technologies believes in community building and social learning by connecting learners to each other so that they can discuss concepts, work on projects, solve problems and share innovative ideas.
Yes, we have group projects so that students can engage with each other and share ideas.
You will be graded on the basis of weekly quizzes, assignments, lab engagements, midterm and final exams.
Our online classes are Instructor paced.
Yes, the course material is accessible to the students even after the course is over in the form or PDF documents and recorded lectures.
Yes, you will get assistance for job interviews. We have a dedicated team for career guidance and counselling.
Enrollment is a commitment between you and us in which you promise to be a successful learner and we promise to provide you with the best possible learning environment. Our sessions consist of online interactive live classes, live labs and 24*7 live support along with career prep support. Enroll with us and experience the complete learning environment instead of just a demo session.