Data Science
Data Science
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$550.00 $400.00
Duration: 60 Hrs 
Data Science Course Content (R, Python, ML)
Getting started with Data Science and Recommender Systems
 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.
Reasons to Use Data Science – Project Life cycle
 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.
Data Conversion
 Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data
 Terms of Statistics
 Descriptive statistics, Let us understand some terms in statistics, Variable
 Plots
 Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots
 Set & rules of probability, Bayes Theorem
 What is probability?, Set & rules of probability, Bayes Theorem
 Distributions
 Probability Distributions, Few Examples, Student T Distribution, Sampling Distribution, Student t Distribution, Poison distribution
 Sampling
 Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling
 Tables & Analysis
 Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( ChiSquare ), Analysis of Variance, Correlation between Nominal variables
 Acquiring Data
 Boxplot in R programming, understanding distribution and percentile, identifying outliers, Rstudio Tool, various types of distribution like Normal, Uniform and Skewed
R Programming Course Content
Introduction to R
 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, lzearn about Rbind.
RPackages
 R Functions, code compilation and data in welldefined format called RPackages, learn about RPackage structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.
Sorting Dataframe
 R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.
Matrices and Vectors
 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.
 Reading data from external files
 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.
 Generating plots
 Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.
Analysis of Variance (ANOVA)
 Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.
Kmeans Clustering
 KMeans 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 cooccurrence relationships.
Association Rule Mining
 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.
Regression in R
 Understanding what is Simple Linear Regression, the various equations of Line, Slope, YIntercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.
 Analyzing Relationship with Regression
 Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit
 Advance Regression
 Deep understanding of the measure of variation, the concept of coefficient of determination, FTest, the test statistic with an Fdistribution, advanced regression in R, prediction linear regression.
 Logistic Regression
 Logistic Regression Mean, Logistic Regression in R.
 Advance Logistic Regression
 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 tradeoffs for a binary classifier.
Receiver Operating Characteristic (ROC)
 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 HosmerLemeshow test for checking whether the observed event rates match the expected event rates.
 Kolmogorov Smirnov Chart
 Data analysis with R, understanding the WALD test, MC Fadden’s pseudo Rsquared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is nonparametric test of one dimensional probability distribution.
Database connectivity with R
 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.
 Integrating R with Hadoop
 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.
Python Programming Course Content
 Learn the Basics
 Hello, World!, Variables and Types, Lists, Basic Operators, String Formatting, Basic String Operations, Conditions, Loops, Functions, Classes and Objects, Dictionaries, Modules and Packages
 Data Science Tutorials
 Numpy Arrays, Pandas Basics
 Advanced Tutorials
 Generators, List Comprehensions, Multiple Function Arguments, Regular Expressions, Exception Handling, Sets, Serialization, Partial functions, Code Introspection, Closures, Decorators
Machine Learning in Data Science
 Deploying machine learning for data analysis, solving business problems, using algorithms for searching patterns in data, relationship between variables, multivariate analysis, interpreting correlation, negative correlation.
Deep dive into Data Transformation
 Data Transformation key phases Data Mapping and Code Generation, Data Processing operation, data patterns, data sampling, sampling distribution, normal and continuous variable, data extrapolation, regression, linear regression model.
Data Testing and Assessment
 Data analysis, hypothesis testing, simple linear regression, Chisquare for assessing compatibility between theoretical and observed data, implementing data testing on data warehouse, validating data, checking for accuracy, data operational monitoring capabilities.
Data Model, Algorithms & Prediction
 Various techniques of data modelling and generating algorithms, methods of business prediction, prediction approaches, data sampling, disproportionate sampling, data modelling rules, data iteration, and deploying data for missioncritical applications.
Data Segmentation and Analysis
 Working with large data sets in data warehouses, data clustering, grouping, horizontal & vertical slicing, data sharing in partitioning, clustering algorithms, Kmeans Clustering for analysis and data mining, exclusive clustering, hierarchy clustering, Mahout Clustering algorithm and Probabilistic Clustering, nearest neighbor search, pattern recognition, and statistical classification.
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