Data Science


Hyderabad   26 February 2018

Data science Course Content Data science Introduction •Data Science motivating examples -- Nate Silver, Netfilx, Money ball, okcupid, LinkedIn, •Introduction to Analytics, Types of Analytics, •Introduction to Analytics Methodology •Analytics Terminology, Analytics Tools •Introduction to Big Data •Introduction to Machine Learning R software: 1.Introduction and Overview of R Language : •Origin of R, Interface of R,R coding Practices •R Downloading and Installing R •Getting Help on a function •Viewing Documentation 2 Data Inputting in R Data Types •Data Types, Data Objects, Data Structures •Creating a vector and vector operations •Sub-setting •Writing data •Reading tabular data files •Reading from csv files •Initializing a data frame •Selecting data frame cols by position and name •Changing directories •Re-directing R output 3 Data Manipulation in R •Appending data to a vector •Combining multiple vectors •Merging data frames •Data transformation •Control structures •Nested Loops splitting •Strings and dates •Handling NAs and Missing Values •Matrices and Arrays •The str Function •Logical operations •Relational operators •generating Random Variables •Accessing Variables •Matrix Multiplication and Inversion •Managing Subset of data •Character manipulation •Data aggregation •Subscripting Functions and Programming in R •Flow Control: For loop •If condition •While conditions and repeat loop •Debugging tools •Concatenation of Data •Combining Vars, cbind, rbind •sapply, lapply, tapply functionsBasic Statistics in R : Part-I Session 1 •Descriptive Statistics Introduction to Advanced Data Analytics •Statistical inferences for various Business problems •Types of Variables, measures of central tendency and dispersion •Variable Distributions and Probability Distributions •Normal Distribution and Properties •Computing basic statistics •Comparing means of two samples •Testing a correlation for significance •Testing a proportion •Classical tests (t,z,F) •ANOVA •Summarizing Data •Data Munging Basics Part-I Session 2 •Test of Hypothesis Null/Alternative Hypothesis formulation 7 •One Sample, two sample (Paired and Independent) T/Z Test •P Value Interpretation •Analysis of Variance (ANOVA) •Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.) Part-I Session 3 •Introduction to Correlation - Karl Pearson •Spearman Rank Correlation Advanced Analytics with real world examples (Mini Projects)Part-II Session 1 • Regression Theory • Linear regression • Logistic Regression Non Linear Regressions using Link functions • Logit Link Function • Binomial Propensity Modeling • Training-Validation approach Part-II Session 2 • Factor Analysis Introduction to Factor Analysis – PCA • Reliability Test 4 • KMO MSA tests, Eigen Value Interpretation • Factor Rotation and Extraction Part-II Session 3 • Cluster Analysis Introduction to Cluster Techniques • Distance Methodologies • Hierarchical and Non-Hierarchical Procedures • K-Means clustering • Wards Method Time Series AnalysisPart-III Session 1 •Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis •Decomposition of Time Series •Trend and Seasonality detection and forecasting •Exponential Smoothing (Single, double and triple) Part-III Session 2 •ARIMA Modeling Box - Jenkins Methodology •Introduction to Auto Regression and Moving Averages, ACF, PACF Data Mining : Machine learning with R:Part IV Session 1 •Introduction to Machine learning and various machine learning techniques •Introduction to Data Mining •Introduction to Text Mining •Text analytic Process •Sentiment Analysis Part IV •Statistical Analysis & Data Mining/Machine Learning •Cluster Analysis using R-Rattle •Association Rule Mining •Predictive Modeling using Decision Trees •Supervised learning •Un- Supervised learning •Reinforcement learning •Neural Network •Support Vector machine Part IV Session 3 •Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data •ROC, Sensitivity, Specificity, Lift charts, Error Matrix •Deploying models using Score options •Opening and Saving models using Rattle Analytics in Excel - 3 days •Data Preparation and Data Exploration in Excel •Network Analysis using NodeXL Data Visualization in R •Creating a bar chart, dot plot •Creating a scatter plot, pie chart •Creating a histogram and box plot •Other plotting functions •Plotting with base graphics •Plotting with Lattice graphics •Plotting and coloring in R Tableau with Case studiesSAS E Miner with use cases Benefits of Online Training :- •Training improves your skill, but online Training improve your skill and gives a flexible platform to learn. •A Learner with good internet connection, laptop & head phones with mike will help you to learn from anywhere on the globe. • If a learner misses a class, he can go through the recording of the And we do market your profile at no additonal charges. 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