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Data Science

Hyderabad  |  Added on 26 Feb, 2018 |  Ad ID: 21150

Views : 7


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. Regards, Swarna IT Solutions Online Training & Support Online Training | Corporate Training | Job | For Escalations :- +91- 8106456699 ; 040 29701747


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