Apply effective analytics and sophisticated business models on huge volumes of data using the concepts of R Programming and machine-learning algorithms with Mahout
This is a Combo Training Course that has been designed to help you work in the field of Data Science. You will be able to gain full proficiency in R programming and various Machine learning techniques with Apache Mahout. This Training Course will help you in statistical computing for data analysis and come up with machine learning algorithms.
R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.
R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.
R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.
Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.
Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.
Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.
K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.
Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.
Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.
Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit
Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.
Logistic Regression Mean, Logistic Regression in R.
Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.
Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.
Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.
Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.
Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.
Logistic Regression Case Study
In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
Multiple Regression Case Study
You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.
Receiver Operating Characteristic (ROC) case study
You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.
Classification and Recommendation, Clustering in Mahout, Pattern Mining, Understanding machine Learning, Using Model diagram to decide the approach, Data flow, Supervised and Unsupervised learning
Concept of Recommendation, Recommendations by E-commerce site, Comparison between User Recommendations and Item recommendation, Define recommenders and Classifiers, Process of Collaborative Filtering, Explaining Pearson coefficient algorithm, Euclidean distance measure, Implementing a recommender using map reduce
Defining Clustering, User-to-user similarity, Clustering Illustration, Euclidean distance measure, Distance measure vector, Understanding the process of Clustering, Vectorizing documents-Unstructured data
Document clustering, Sequence-to-sparse Utility, K-Mean Clustering
Terminology, Predictor and Target variable, Classifiable DataKey Challenges in Classification algorithm, Vectorizing Continuous data, Classification Examples, Logic Regression and its examples
Clustering, Clustering Process, Transaction Clustering, Different techniques of Vectorization, Distance measure, Clustering algorithm-K-MEAN, Clustering Application-1, Clustering Application-2, Sentiment Analyzer
Pearson Coefficient, Collaborative Filtering Process, Collaborative Filtering, Similarity Algorithms, Pearson Correlation, Euclidean Distance Measure -Frequent Pattern & Association rules, Frequent Pattern Growth
Introduction to Data Science, importance of Data Science, statistical and analytical methods, deploying Data Science for Business Intelligence, transforming data, machine learning and introduction to Recommender systems.
How Data Science solves real world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection, introduction to statistical packages, data visualization tools, R Programming, predictive modelling, machine learning, artificial intelligence and statistical analysis.
Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data
Descriptive statistics, Let us understand some terms in statistics, Variable
Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots
What is probability?, Set & rules of probability, Bayes Theorem
Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution
Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling
Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables
Project Title – Restaurant Revenue Prediction
Data set – Sales
Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.
Project 1 – Understanding Cold Start Problem in Data Science
Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the following:
Project 2 – Recommendation for Movie, Summary
Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provider data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:
The Market Basket Analysis (MBA) case study
This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
Intellipaat provides one of the best industry-led Data Science, R and Mahout training. This is a comprehensive course that includes mastering the skills needed to be a Data Scientist including learning R programming, machine learning techniques using Apache Mahout, gaining proficiency in data analysis, statistical computing. This training course is fully aligned towards clearing the CCP Data Scientist Cloudera certification (CCP:DS).
This is a completely career-oriented Data Scientist training and it is designed by industry experts. Your training program includes real time Data Science projects, R and Mahout projects, step-by-step assignments to evaluate your progress and specially designed quizzes for clearing the requisite certification exams.
Intellipaat also offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. All-in-one it is a one-time investment to become a successful Data Scientist and grab the best jobs at the best salaries in top MNCs around the world.
Intellipaat basically offers the self-paced training and online instructor-led training. Apart from that we also provide corporate training for enterprises. All our trainers come with over 12 years of industry experience in relevant technologies and also they are subject matter experts working as consultants. You can check about the quality of our trainers in the sample videos provided.
If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers. The best part is that you can contact Intellipaat even after completion of training to get support and assistance. There is also no limit on the number of queries you can raise when it comes to doubt clearance and query resolution.
The Intellipaat self-paced training is for people who want to learn at their own leisurely pace. As part of this program we provide you with one-on-one sessions, doubt clearance over email, 24/7 Live Support, lifetime LMS and upgrade to the latest version at no extra cost. The prices of self-paced training can be 75% lesser than online training. While studying should you face any unexpected challenges then we shall arrange a Virtual LIVE session with the trainer.
We provide you with the opportunity to work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready. These projects could be in exciting and challenging fields like banking, insurance, retail, social networking, ecommerce, marketing, sales, high technology and so on. The Intellipaat projects are equivalent to six months of relevant experience in the corporate world.
Yes, Intellipaat does provide you with placement assistance. We have tie-ups with 80+ organizations including Ericsson, Cisco, Cognizant, TCS, among others that are looking for skilled & quality professionals and we would be happy to assist you with the process of preparing yourself for the interview and the job.
Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.
Upon successful completion of training you have to take a set of quizzes, complete the projects and upon review and on scoring over 60% marks in the qualifying quiz the official Intellipaat verified certificate is awarded.The Intellipaat Certification is a seal of approval and is highly recognized in 80+ corporations around the world including many in the Fortune 500 list of companies.
This course is designed for clearing the certification exam CCP Data Scientist Cloudera certification (CCP:DS).
The entire training course content is in line with the certification program and helps you clear the requisite certification exam with ease and get the best jobs in the top MNCs.
As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Mahout & Intellipaat R Certification along with Intellipaat Course Completion certificate will be awarded on the completion of assignments and Project work (upon expert review) and on scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
You will get Lifetime access to high quality interactive tutorials along with life time access to complete Course Material .There will be 24/7 access to video tutorials with email support. If you stuck in any unexpected problem we will provide online interactive sessions with trainer for issue resolving.
We provide 24X7 support by email for issues or doubts clearance for Self-paced training.
In online Instructor led training, trainer will be available to help you out with your queries regarding the course. If required, the support team can also provide you live support by accessing your machine remotely. This ensures that all your doubts and problems faced during labs and project work are clarified round the clock.
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