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1:Introduction Data science & Business Analytics
- Data Science and Business analytics
- Introduction to Advanced Data Analytics
- Charts for Data Science and Business Analytics üHadoop for Data Science
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 and Character data
- Summary and Visualization Exploration
3.Basic Probability for Business issues
- Simple
- Marginal
- Joint
- Conditional
- Bayes’ Theorem
4:Basic Distributions
- Discrete
- Binomial
- Hyper geometric
- Poisson
- Continuous
- Normal
- Scandalized
5.Sampling Technique Big Data
- Sampling Distributions
- Simple Random
- Systematic Sample
- Cluster Sample
- Standard Error of the Mean
- Skewed Std. Error
- Kurtosis Std. Error
- Sampling from Infinity
- Sampling Distributions for Mean
- Sampling Distributions for proportions Theorem’s
6:Data Validation & Data Normality
- Steam and leaf analysis
- Unvariate normality techniques
- Multivariate techniques
- Q-Q probability plots
- Cumulative frequency
- Explorer analysis
- Histogram
- Box plot
- Scores for Normality Check
- Testing
7: Data cleaning process Quality check
- PCA for Big Data Analysis or Unsupervised data üPCA Regression Scores for Supervised data üNoise Data detecting
- Data cleaning with Regression Residual üData scrubbing with statistical sense
8:Data Imputation and outlier treatment
- Outlier treatment with central tendency Mean
- Outlier with Min Max
- Outlier Detection
- Visualize Outlier Treatment
- Summarized Outlier Treatment
- Outlier with Residual Analysis
- Outlier Detection with PCA Analysis
- Data Imputation with series Central Tendency
9: Test of Hypothesis
- Null Hypothesis formulation
- Alternative Hypothesis
- Type I and Type II errors
- Power Value
- One tail and two tail
- T-TEST’s
- ANOVA
- MANOVA
- Chi Square Test
- Kendall Chi Square
- Kruskal-Wallis Rank Test Chi Square
- Mann-Whitney, Chi Square
- Wilcoxon, Chi Square
10: Data Transformation
- Log, Arcsine, Box- Cox, Square root Inverse and Data normalization
11:Predictive modeling & Diagnostics
- Correlation üRegression
- Examination Residual analysis üAuto Correlation
- Test of ANOVA Significant üHomoscedasticity üHeteroskedasticity üMulticollinearity
- Cross validation
- Check prediction accuracy.
12:Logistic Regression Analysis
- Logistic Regression
- Discriminate Regression Analysis Multiple Discriminate Analysis Stepwise Discriminate Analysis Logic function
- Test of Associations
- Chi-square strength of association,Binary Regression Analysis
- Estimation of probability using logistic regression,Hosmer Lemeshow
- nagelkerke R square
- Pseudo R square
- Model Fit
- Model cross validation
- Discrimination functions
13: Big Data Analytics
- Introduction to Factor Analysis
- Principle component analysis
- Reliability Test
- KMO MSA tests, etc..
- Rotation and Extraction steps
- Conformity Factor Analysis
- Exploratory Factor Analysis
- Factor Score for Regression
14:Cluster Analysis and Methods
- Introduction to Cluster Techniques
- Hierarchical clustering
- K Means clustering
- Wards Methods
- Aglomerative Clustering
- Variation Methods
- Maximum distance Linkage Methods
- Centroid distance Methods
- Minimum distance Linkage Method
- Cluster Dendrogram
- Euclidean distance
15:Data Mining Machine Learning and Artificial Intelligence
- Prediction
- Support Vector Machines
- Gaussian Models
- Neural Network
- Classification Models
- Ordinal Regression
- Multinomial Regression
- Discriminate analysis
- Simple Cluster
- Hierarchical Cluster
16:Time series
- Auto Regression, Moving Average, Multiplicative, ARMA, Additive Model
17:Model Validation and Testing
- AIC, BIC, Kappa Statistics, ROC, APE, MAPE, Lift Curve, Errors
18: Hadoop Ecosystem
- Pig,Hive,Map Reduce,NoSQL,etc
Note : Open source and commercial Tools is a part of training.
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