Business Analytics

Fact Data Science and Business analytic
SWOT analysis of Data Science
Journey of mathematics-Statistics –Econometric
SQL data for Data science
No SQL data for Data Science
OLTP OLAP for Data information
Web Application report
Difference of Data mining Data science
DM-CRISP Model Processing

 

Data Type

Continues
Discrete
Nominal
Ordinal
Binary

Measure s of central tendency

Mean
Mode
Median
Geo-mean
Har-mean
Trimmed Mean
Weighted d mean
95% U mean
95% L mean

Data Viability Dispersion

⦁ Std
⦁ Variance
⦁ Coefficient Of Variance
⦁ Range
⦁ Min
⦁ Max
⦁ Skewed
⦁ Kurtosis
⦁ Std Error
⦁ IQR
⦁ Five Numbers Summary
⦁ Q0 Min
⦁ Q1 25%
⦁ Q2 50% Median
⦁ Q3 75%
⦁ Q4 100%

 

Data Visualization & Visual Data Validation

⦁ Pie chart
⦁ Area plot
⦁ Scatter plot
⦁ Surface
⦁ Stock plot
⦁ Radar
⦁ Tree map
⦁ Waterfall
⦁ Heat map
⦁ Bubble chart
⦁ Line chart 12.Histogram 13.Standardized plot
⦁ Stem leaf
⦁ Box plot

Sampling Techniques Big Data

⦁ Sampling Distributions
⦁ Simple Random
⦁ Skewed Std. Error
⦁ Kurtosis Std. Error
⦁ Central Lit Theorem,
⦁ Sampling from Infinity
⦁ Sampling Distributions for Mean
⦁ Sampling Distributions for proportions

Probability

⦁ Simple Probability
⦁ Marginal Probability
⦁ Joint Probability
⦁ Conditional probability (Bayes’ Theorem probability
⦁ Discrete Distributions
⦁ Binomial Distribution
⦁ Expected Mean
⦁ Variance
⦁ Bi variate destruction
⦁ Covariance
⦁ Hypergeometric Distributions
⦁ Poisson Distribution
⦁ Continuous Distributions
⦁ Random Sample
⦁ Simple Random sample

⦁ Stratified Random sample
⦁ Systematic Random sample
⦁ Cluster random sample

Data Validation Data Normality

⦁ Uni variate normality techniques
⦁ Bi variate techniques
⦁ Multivariate techniques
⦁ Q-Q probability plots
⦁ PP plot
⦁ Cumulative frequency
⦁ Steam and leaf analysis
⦁ Histogram Box plot, Z Score test.
⦁ Shapiro-Wilk Test for Normality
⦁ Anderson-Darling Normality

Data Cleaning outlier treatment

⦁ Outlier treatment with robust measurements
⦁ Outlier treatment with central tendency Mean
⦁ Outlier with Min Max Likelihood methods
⦁ Outlier with Residual Analysis
⦁ Data Imputation with series Central Tendency

Test of Hypothesis

⦁ Null Hypothesis formulation
⦁ Alternative Hypothesis
⦁ One tail Test ,Two tail Test
⦁ One Sample T-TEST
⦁ Paired T-TEST
⦁ Independent Sample T-TEST
⦁ Analysis of Variance (ANOVA),
⦁ ANOVA
⦁ MANOVA
⦁ Chi-square Pearson
⦁ Kendall Chi-square
⦁ Wald Chi-square
⦁ Kruskal-Wallis Rank Test Chi Square
⦁ Mann-Whitney, Chi Square
⦁ McNemar test Chi Square
⦁ Nagelkerke Chi-square
⦁ Data Transformation

Data transformation

⦁ Sqrt Transformation
⦁ Log transformation
⦁ Arcsine transformation
⦁ Box- Cox transformation
⦁ Square root transformation
⦁ Inverse transformation

⦁ Min Max Data normalization Re-scaling
⦁ PCA Transformation

Supervised Learning Linear Regression (Functional Models)

⦁ Correlation – Pearson, Kendall, Wilcox
⦁ SLR Regression
⦁ MLR Regression
⦁ Examination Residual analysis
⦁ Residual QQ plot
⦁ Residual EDA Analysis
⦁ Residual Standardized
⦁ Auto Correlation
⦁ Test of ANOVA Significant
⦁ VIF Analysis
⦁ Test of T-test Significant
⦁ CP Indexing
⦁ Excluding Constant, and excluding constant
⦁ Homo-scedasticity
⦁ Hetero-scedasticity
⦁ Stepwise regression
⦁ Forward Regression
⦁ Backward Regression
⦁ Multicollinearity
⦁ Cross validation

⦁ MAPE
⦁ Check prediction accuracy
⦁ Standardized regression
⦁ Quadrant Regression
⦁ Transformed Regression
⦁ Dummy Variables Regression

Logistics Regression (Classification Models)

⦁ Logit regression
⦁ Binary Regression Analysis
⦁ Probit regression
⦁ Ordinal Regression
⦁ Multinomial Regression
⦁ Stepwise Regression
⦁ Backward Regression
⦁ Forward Regression
⦁ Discriminate Regression Analysis
⦁ Multiple Discriminant Analysis
⦁ Test of Associations
⦁ Chi-square strength of association
⦁ Wald Test statistics for Model
⦁ Hosmer Lemshow
⦁ Pseudo R square
⦁ Maximum likelihood estimation
⦁ Model Fit
⦁ Model cross validation
⦁ AIC

⦁ AICC
⦁ BIC (Bayesian information criterion)

Timeseries (Forecasting Models)

⦁ Navie model
⦁ Moving Averages
⦁ Weighted Moving Averages
⦁ Exponential Smoothing

Decision Tree

⦁ GINI Entropy
⦁ CHAID
⦁ CART
⦁ Prunned /Unpruned Tree (Weka)
⦁ Random Forestry
⦁ Boosting bagging
⦁ Ensemble Models

Naive Bayes

⦁ KNN
⦁ SVM

PCA/Dimension Reduction Analysis (Un-Supervised Learning )

⦁ Factor Analysis
⦁ Principle component analysis
⦁ Reliability Test
⦁ KMO MSA tests,
⦁ Rotation
⦁ Future Extraction for regression

Cluster Analysis

⦁ Hierarchical clustering
⦁ K Means clustering
⦁ Wards Methods,
⦁ Linkage Methods
⦁ Euclidean distance
⦁ Dendogram

Neural Network

⦁ 1 ANN
⦁ 2 CNN
⦁ 3 RNN

Semi Supervised Learning

⦁ Aprior algorithm
⦁ Association Mining MBA
⦁ Recommendation System

ModelValidation

⦁ Model Validation and Testing
⦁ Kappa Statistics
⦁ AIC
⦁ BIC
⦁ Error/ Confusion matrices AIC
⦁ ROC
⦁ APE
⦁ MAPE
⦁ Lift Curve,
⦁ Sensitivity
⦁ Mis-classification Rating
⦁ Specificity
⦁ Maximum Absolute Error

Text mining

⦁ NLP
⦁ Sentiment Analysis

Model Deployment

⦁ Microsoft Azure
⦁ Google Clod
⦁ Amazon WNS

Big Data and data warehouse architecture

⦁ Data Integration
⦁ ETL transformation
⦁ Data deployment

 

Python Introduction

⦁ python data types
⦁ python strings
⦁ string formatting
⦁ python operators
⦁ control statements
⦁ simple if
⦁ loops
⦁ while
⦁ for
⦁ functions
⦁ lambda functions
⦁ generators
⦁ Exception handling
⦁ classes/objects
⦁ inheritance
⦁ File handling in python
⦁ Regex python
⦁ json parsing in python
⦁ Numpy
⦁ pandas
⦁ Matplotlib
⦁ Functional programming in python
⦁ Map
⦁ Reduce
⦁ Filter