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Data Science SAS

Starts on : 28-07  2018

This is well defined Course content  by Top Level industrial experts and Senior  Professors .  Altogether 250 hours programme it takes around 3 months  with 15 + case studies  List of Case Studies 

Conditions : Trial Version available for 14 days 

 

Module1 1

Foundations of Date Science: Data Visualization and Interpretation

Part -1 Referential details for Data science Business Analytics

 Scope & Fact of Data Science and Business analytics

 SWOT Analysis of Data Science Business Analytics

 Introduction to Advanced Data Analytics

 Journey Mathematics-Statistics-Econometrics

 Flow chart for Data Science and Business Analytics

 Data wherehouse conceptual discussions

 Hadoop for Data Science

 OLTP OLAP for Data information

 Web Application report

 Part-2: Descriptive Statistics:

 Descriptive Statistical

 Inferential Statistics

 Types of Variables

 Measures of central tendency

 Data Viability Dispersion

 Five number Summary Analysis

 Data Distribution Techniques

 Exploration Techniques for Numerical data

 Exploration techniques for Character Data

 Visualization Exploration

 Summary Exploration

 Chebychev’s Inequality.

Part-3: Basic Probability for Business Issues:

 Simple Probability

 Marginal Probability

 Joint Probability

 Conditional probability (linked with decision Tress Algorithms)

 Bayes’ Theorem probability (linked with Naïve Bayes Algorithms)

 Discrete Distributions

 Binomial Distribution

 Hypergeomatric Distributions

 Poisson Distribution

 Continuous Distributions

 Normal Distribution and Properties

 Scandalized Distributions

Part-4: Sampling Techniques Big Data

 Sampling Distributions

 Simple Random

 Systematic Sample

 Stratified sample

 Cluster Sample

 Standard Error of the Mean

 Skewed Std. Error

 Kurtosis Std. Error

 Central Limit Theorem,

 Sampling from Infinity

 Sampling Distributions for Mean

 Sampling Distributions for proportions

Module 2

Data Preprocessing and Imputation

Part-5: Data Validation Data Normality

 Unvariate normality techniques

 Bivariate techniques

 Multivariate techniques

 Q-Q probability plots

 Cumulative frequency

 Explorer analysis

 Steam and leaf analysis

 Histogram

 Box plot

 Scores for Normality Check

 Kolmogorov Smirnov test

 Shapiro Wilks test

 Anderson darling test

Part – 6 Data Cleaning process Quality check

 PCA for Big Data Analysis or Unsupervised data

 PCA Regression Scores for Supervised aata

 Noise Data detecting

 Data cleaning with Regression Residual

 Data Scrubbing with statistical sense

Part-7: Data Imputation and outlier treatment

 Outlier treatment with robust measurements

 Outlier treatment with central tendency Mean

 Outlier with Min Max Likelihood methods

 Outlier Detection with Density Based

 Visualize Outlier Treatment

 Outlier with Residual Analysis

 Outlier Detection with PCA Analysis

 Data Imputation with series Central Tendency

Part-8: Test of Hypothesis

 Null Hypothesis formulation

 Alternative Hypothesis

 Type I and Type II errors

 Power Value

 One tail and Two tail

 One Sample T-TEST

 Paired T-TEST

 Independent Sample T-TEST

 Analysis of Variance ( ANOVA),

 MANOVA

 Chi Square Test

 Kendall Chi Square

 Kruskal-Wallis Rank Test Chi Square

 Mann-Whitney, Chi Square

 Wilcoxon, Chi Square

 McNemar test Chi Square

 Part-9: Data Transformation

 Log transformation

 Box- Cox transformation

 Square root transformation

 Inverse transformation

 Min Max Data normalization

 Module 3

Predictive Analytics: Supervised Learning Algorithms

Part-10: Predictive modeling & Diagnostics

 Correlation

 SLR Regression

 MLR Regression

 Examination Residual analysis

 Auto Correlation

 Test of ANOVA Significant

 VIF Analysis

 Test of Ttest Significant

 CP Indexing

 Eigen Value for PCA Analysis

 Homoscedasticity

 Heteroskedasticity

 Stepwise regression

 Forward Regression

 Backward Regression

 Multicollinearity

 Cross validation

 MAPE

 Check prediction accuracy

 Standized regression

 Quadraint Regression

 Transformed Regression

 Dummy Variables Regression

Part-11 Logistic Regression Analysis

 Logistic Regression

 Discriminate Regression Analysis

 Multiple Discriminant Analysis

 Stepwise Discriminant Analysis

 Logit function

 Test of Associations

 Chi-square strength of association

 Binary Regression Analysis

 Profit and Logit Models

 Estimation of probability using logistic regression,

 Wald Test statistics for Model

 Hosmer Lemshow

 Nagurkake R square

 Pseudio R square

 Maximum likelihood estimation

 Model Fit

 Model cross validation

 Discrimination functions

 AIC

 BIC (Bayesian information criterion)

 Kappa Statistics

 AIC

 BIC

 Error/ Confusion matrices

 ROC

 APE

 MAPE

 Lift Curve

 Sensitivity

 Misclassification Rating

 Specificity

 Maximum Absolute Error

 Recall

 Miss classification

 Root Final Prediction Error

 Gini Coefficient

 Schwarz’s Bayesian Criterion

 Module 4

(Advanced Analytics 1) unsupervised Learning Algorithms

 Part-12: Dimension Reduction Analysis

 Introduction to Factor Analysis

 Principle component analysis

 Reliability Test

 KMO MSA tests, Eigen Value Interpretation,

 Rotation and Extraction steps

 Varmix Models

 Conformity Factor Analysis

 Exploitary Factor Analysis

 Factor Score for Regression

Part-13: Cluster Analysis

 Introduction to Cluster Techniques

 Hierarchical clustering

 K Means clustering

 Wards Methods

 Agglomerative Clustering

 Variation Methods

 Maximum distance Linkage Methods

 Centroid distance Methods

 Minimum distance Linkage Method

 Cluster Dengogram,

 Ecludin distance method s

Module 5

 Forecasting and Operations  Analytics

NAvie Forecsting

Moving Average

Exponecial smoothing

ARIMA

REfere Time series ppt

Auto-Regressive Integrated

Moving Average (ARIMA) models,

ARIMAX.

Conjoint analysis,

Discriminant analysis.

 Module 5

(Advanced Analytics 3) Machine Learning Algorithms

 Prediction

 Support Vector Machines (SVM)

 Binary Regression/Logit Model

 Probit Model

 Na¨ıve Bayes

 Na¨ıve Bayes Multinomial

 Ordinal Regression

 Multinomial Regression

 k-Nearest Neighbor Classification

 Decision Stump

 CHAID Analysis

Recommender Systems,

Collaborative Filtering

Advanced recommender system.

Bootstrap Aggregating (Bagging),

Random forest,

Adaptive boosting,

gradient boosting

Support vector machine

Neural Network

 C4.5 / C5.0

 J48 Pruning, Uprunning

 Decision trees

 

Module 6

(Advanced Analytics 4) Artificial Intelligence (3 Days)

Introduction to neural networks; rule

based expert systems

Introduction to artificial neural

networks (ANN); Neuron as

computing element; Perceptron:

McCullogh-Pitts model; Backpropagation

algorithm; Multi-layer

Neural Networks

Deep learning algorithms:

Convolutional networks; Recurrent

nets; Auto-encoders;

Deep Learning Platform: H2O.ai;

Dato GraphLab; Tensor Flow

Module 7

(Advanced Analytics 5)

Suvervial Analysis

 Mantel—Haenszel Test

 Kaplan-Meier (Product- Limit) Estimator

 Cox’s Proportional Hazards Model

 Cox—Snell Residual

 Hazard Functions

 Proportional Hazards Assumption

Module 8

(Advanced Analytics 2)

Big Data Analytics

Introduction to BigData

sources of Big Data

Big Data technologies: Hadoop distributed

file system; Employing Hadoop

Statistical Analysis of Big Data.

Pig

Hive

MapReduce

NoSQL

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