Data Science with R

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Price: 199.99$

A warm welcome to the Data Science with R course by Uplatz. Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques. A mix of all these fields help us in discovering the visions or designs from raw data which can be of major use in the formation of big business decisions. As a Data scientist it’s your role to inspect which questions want answering and where to find the related data. A data scientist should have business insight and analytical services. One also needs to have the skill to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. R is a commanding language used extensively for data analysis and statistical calculating. It was developed in early 90s. R is an open-source software. R is unrestricted and flexible because it’s an open-source software. R’s open lines permit it to incorporate with other applications and systems. Open-source soft wares have a high standard of quality, since multiple people use and iterate on them. As a programming language, R delivers objects, operators and functions that allow employers to discover, model and envision data. Data science with R has got a lot of possibilities in the commercial world. Open R is the most widely used open-source language in analytics. From minor to big initiatives, every other company is preferring R over the other languages. There is a constant need for professionals with having knowledge in data science using R programming. Uplatz provides this comprehensive course on Data Science with R covering data science concepts implementation and application using R programming language. Data Science with R – Course Syllabus1. Introduction to Data Science1.1 The data science process1.2 Stages of a data science project1.3 Setting expectations1.4 Summary2. Loading Data into R2.1 Working with data from files2.2 Working with relational databases2.3 Summary3. Managing Data3.1 Cleaning data3.2 Sampling for modeling and validation3.3 Summary4. Choosing and Evaluating Models4.1 Mapping problems to machine learning tasks4.2 Evaluating models4.3 Validating models4.4 Summary5. Memorization Methods5.1 Using decision trees 1275.2 Summary6. Linear and Logistic Regression6.1 Using linear regression6.2 Using logistic regression6.3 Summary7. Unsupervised Methods7.1 Cluster analysis7.2 Association rules7.3 Summary8. Exploring Advanced Methods8.1 Using bagging and random forests to reduce training variance8.2 Using generalized additive models (GAMs) to learn nonmonotone relationships8.3 Using kernel methods to increase data separation8.4 Using SVMs to model complicated decision boundaries9. Documentation and Deployment9.1 The buzz dataset9.2 Using knitr to produce milestone documentation