Hyderabad | Added on 26 Feb, 2018 | Ad ID: 21150
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Data science Course Content Data science Introduction ï¿½Data Science motivating examples -- Nate Silver, Netfilx, Money ball, okcupid, LinkedIn, ï¿½Introduction to Analytics, Types of Analytics, ï¿½Introduction to Analytics Methodology ï¿½Analytics Terminology, Analytics Tools ï¿½Introduction to Big Data ï¿½Introduction to Machine Learning R software: 1.Introduction and Overview of R Language : ï¿½Origin of R, Interface of R,R coding Practices ï¿½R Downloading and Installing R ï¿½Getting Help on a function ï¿½Viewing Documentation 2 Data Inputting in R Data Types ï¿½Data Types, Data Objects, Data Structures ï¿½Creating a vector and vector operations ï¿½Sub-setting ï¿½Writing data ï¿½Reading tabular data files ï¿½Reading from csv files ï¿½Initializing a data frame ï¿½Selecting data frame cols by position and name ï¿½Changing directories ï¿½Re-directing R output 3 Data Manipulation in R ï¿½Appending data to a vector ï¿½Combining multiple vectors ï¿½Merging data frames ï¿½Data transformation ï¿½Control structures ï¿½Nested Loops splitting ï¿½Strings and dates ï¿½Handling NAs and Missing Values ï¿½Matrices and Arrays ï¿½The str Function ï¿½Logical operations ï¿½Relational operators ï¿½generating Random Variables ï¿½Accessing Variables ï¿½Matrix Multiplication and Inversion ï¿½Managing Subset of data ï¿½Character manipulation ï¿½Data aggregation ï¿½Subscripting Functions and Programming in R ï¿½Flow Control: For loop ï¿½If condition ï¿½While conditions and repeat loop ï¿½Debugging tools ï¿½Concatenation of Data ï¿½Combining Vars, cbind, rbind ï¿½sapply, lapply, tapply functionsBasic Statistics in R : Part-I Session 1 ï¿½Descriptive Statistics Introduction to Advanced Data Analytics ï¿½Statistical inferences for various Business problems ï¿½Types of Variables, measures of central tendency and dispersion ï¿½Variable Distributions and Probability Distributions ï¿½Normal Distribution and Properties ï¿½Computing basic statistics ï¿½Comparing means of two samples ï¿½Testing a correlation for significance ï¿½Testing a proportion ï¿½Classical tests (t,z,F) ï¿½ANOVA ï¿½Summarizing Data ï¿½Data Munging Basics Part-I Session 2 ï¿½Test of Hypothesis Null/Alternative Hypothesis formulation 7 ï¿½One Sample, two sample (Paired and Independent) T/Z Test ï¿½P Value Interpretation ï¿½Analysis of Variance (ANOVA) ï¿½Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.) Part-I Session 3 ï¿½Introduction to Correlation - Karl Pearson ï¿½Spearman Rank Correlation Advanced Analytics with real world examples (Mini Projects)Part-II Session 1 ï¿½ Regression Theory ï¿½ Linear regression ï¿½ Logistic Regression Non Linear Regressions using Link functions ï¿½ Logit Link Function ï¿½ Binomial Propensity Modeling ï¿½ Training-Validation approach Part-II Session 2 ï¿½ Factor Analysis Introduction to Factor Analysis ï¿½ PCA ï¿½ Reliability Test 4 ï¿½ KMO MSA tests, Eigen Value Interpretation ï¿½ Factor Rotation and Extraction Part-II Session 3 ï¿½ Cluster Analysis Introduction to Cluster Techniques ï¿½ Distance Methodologies ï¿½ Hierarchical and Non-Hierarchical Procedures ï¿½ K-Means clustering ï¿½ Wards Method Time Series AnalysisPart-III Session 1 ï¿½Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis ï¿½Decomposition of Time Series ï¿½Trend and Seasonality detection and forecasting ï¿½Exponential Smoothing (Single, double and triple) Part-III Session 2 ï¿½ARIMA Modeling Box - Jenkins Methodology ï¿½Introduction to Auto Regression and Moving Averages, ACF, PACF Data Mining : Machine learning with R:Part IV Session 1 ï¿½Introduction to Machine learning and various machine learning techniques ï¿½Introduction to Data Mining ï¿½Introduction to Text Mining ï¿½Text analytic Process ï¿½Sentiment Analysis Part IV ï¿½Statistical Analysis & Data Mining/Machine Learning ï¿½Cluster Analysis using R-Rattle ï¿½Association Rule Mining ï¿½Predictive Modeling using Decision Trees ï¿½Supervised learning ï¿½Un- Supervised learning ï¿½Reinforcement learning ï¿½Neural Network ï¿½Support Vector machine Part IV Session 3 ï¿½Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data ï¿½ROC, Sensitivity, Specificity, Lift charts, Error Matrix ï¿½Deploying models using Score options ï¿½Opening and Saving models using Rattle Analytics in Excel - 3 days ï¿½Data Preparation and Data Exploration in Excel ï¿½Network Analysis using NodeXL Data Visualization in R ï¿½Creating a bar chart, dot plot ï¿½Creating a scatter plot, pie chart ï¿½Creating a histogram and box plot ï¿½Other plotting functions ï¿½Plotting with base graphics ï¿½Plotting with Lattice graphics ï¿½Plotting and coloring in R Tableau with Case studiesSAS E Miner with use cases Benefits of Online Training :- ï¿½Training improves your skill, but online Training improve your skill and gives a flexible platform to learn. ï¿½A Learner with good internet connection, laptop & head phones with mike will help you to learn from anywhere on the globe. ï¿½ 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 Supportwww.swarnaitsolutions.com | firstname.lastname@example.org For Escalations :- +91- 8106456699 ; 040 29701747
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