Quick Stats
Analytics industry is a volatile industry and picks up fast on technology. We have seen an upward trend for R and Python as a skill set in combination with SAS as a tool & Certification
The universities and the start-ups are adapting to R and Python. Hence a huge scope in research-oriented jobs as a practitioner.
There is a dearth of data scientist across the globe where R & Python as a skill set is picking up fast to fill up the gap. Early entries will scoop big is the prediction of the Data Pundits
Benefits
R +Python as a tool
R as a tool is the major component of today's data science. Python training along with fills the gap for the machine and deep learning. This is an added upskill in the skill list and will help you up the success ladder
Project Driven approach
There will be no run through ppt and decks as we don't believe in such methods of learning and spreading education. It would be hands-on and classes would actually be workshops with more focus on the application part
R-Data Science Python -Machine Learning
Data Scientists are betting heavy on R as a soul ingredient for data science. Machine Learning is in these days for Python. The world is moving away from contemporary languages to data-driven languages and R, Python is holding a good place as far as data science is concerned.
Who Should Attend
- Final Year Students who are looking for research-oriented jobs curriculum abroad
- Freshers out of the campus who are looking for jobs in start-ups
- Experienced folks who are in the data industry and look forward to adapting R, Python
- Experienced professionals who look forward to switching the jobs/domain to data science & Machine Learning
Course Outcome
- Understand the outlining features of R, Python
- Strong foundation with more hold on basics
- Data Extraction and Analysis with Machine Learning
- In-depth knowledge of R & Python as a tool and the applied language
- Proper application as to where and where not to apply
Curriculum
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Understanding the R environment:Enroll NowSetting up the machine and installing RSetting up the R environment for the smooth usage of RUnderstanding the various IDEs for R developmentInstallation/Removal of packages
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Importing raw dataEnroll NowReading csv files into RReading json files into RReading txt files into RReading sas7bdat files into R
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Data structuresEnroll NowUnderstanding homogeneous and heterogeneous form atomic vectors in R including Dataframes, List, Vectors, Factors and Matrices in RAtomic vectors in RDataframes, List, VectorsFactors and Matrices in R
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Style GuideEnroll NowCoding standards-Know HowNotation and NamingFilenamesObject namesSyntaxCurly BracesSpacingLine lengthIndentationAssignmentCommenting Guidelines
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Loops and vectorizationEnroll NowWriting For and while loops in RUnderstanding if loops are really a necessary in RUnderstand the apply family in R
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Functions and conditionalsEnroll NowWriting functions in RUnderstanding if...then...else in RUnderstanding pure functions in R and understanding the purrr package in R
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summarizationEnroll NowSummarization with the help of Base R functionsHow to summarize data in R using dplyrBasics of data.table package
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ClusteringEnroll NowUnderstand the meaning of clusteringHierarchical clustering in RK means clustering in R
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GLM FamilyEnroll NowLinear regression in RMulticollinearity and calculation of VIFRoot mean squared error, t statistic, p-value and confidence intervalLogistic regression in RRoC, TPR, Lift, Gain and KS statistic in RInterpreting the Linear and logistic ModelUnderstanding ridge and lasso in linear and logistic model
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Decision TreesEnroll NowDecision Trees in RUnderstanding Ginni IndexKnowing the difference between CART and CHAIDWorking with Random Forest
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Optimization in REnroll NowWhat is optimization?Working with opimization packages in R (e.g Optimx)Constrained optimization ( Lagrange multipliers )
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Bonus TopicEnroll NowBasics of GGPLOT2Scatterplots in RHistograms and density plots in RUnderstanding basics of "Grammar of Graphics" concept
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Introduction to MLEnroll NowWhat is LearningComponents of learningA simple learning modelTypes of LearningWhat is machine learningApplication of ML
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Getting started with PythonEnroll NowWhat is PythonOrigins and versions of PythonWays to run a Python programSetting up Python environmentBasic file operations with PythonBasic data operations with PythonBasic data visualization with PythonHands on with Python
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Process in any MLEnroll NowBasic process flow of any machine learningTerminologies used in MLEvaluation metrics used in MLComparison of ML and Statistical learning
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RegressionEnroll NowSimple Linear RegressionMultiple Linear RegressionConsideration in RegressionAccessing the accuracy of Coefficient estimatesAccessing the accuracy of ModelFun example and workshop!
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ClassificationEnroll NowAn overview of classificationWhy not linear regressionLogistic ModelLinear discriminant AnalysisComparing classificationFun example and workshop!
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Support Vector machinesEnroll NowIntroductionGeneral conceptsComponents of SVMRelationship with Logistic regressionFun example and workshop!
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Decision TreesEnroll NowIntroductionBasics of a treeClassification with treesAdvantages / disadvantages of tree
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Random ForestEnroll NowIntroductionHow Random forest worksDifferent parameters in RFFun example and workshop!
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Gradient boosting machinesEnroll NowIntroductionHow GBM worksDifferent parameters in GBMFun example and workshop!
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ClusteringEnroll NowIntroductionK- Means clusteringHierarchical ClusteringPractical issues in clustering
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Natural Language processingEnroll NowGeneral text processingTokenizingStop wordStemmingPOS taggingChunking/chinkingLemmatizingCorporaBuilding text only modelsFun example and workshop!
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Neural NetworkEnroll NowIntroductionImportant concepts in NNBuilding NN for classificationFun example and workshop!
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Building a recommender SystemBuilding a sales prediction modelRetrieving twitter feeds and sentiment analysis
FAQS
Is this a classroom programme
Yes it is