Advanced Data Science Training

Advanced Data Science Course Content

Module1: Introduction of Data Science

Part -1: Data Science Business Analytics

• Fact of Data Science and Business analytics
• SWOT Analysis of Data Science
• Journey Mathematics-Statistics-Econometrics
• SQL data for Data Science
• NoSQL data for Data Science
• OLTP OLAP for Data information
• Web Application report
• Difference of Machine Meaning AI
• Difference of Data mining Data Science

Module 2: Visualization & Summarization

Part-2: Exploratory Data Analysis

1. Data Type

• Continues
• Discrete
• Nominal
• Ordinal
• Bina

2. Measures of central tendency

• Mean
• Median
• Mode
• Geomean
• Harman
• TrimmedMean
• Weighted Mean
• 95% CI L mean
• 95% CI U mean

3. Data Viability Dispersion

• Std
• Variance
• Coefficient Of Variance
• Range
• Min
• Max
• skewed
• kurtosis
• std Error
• std Skewed
• Error Kurtosis
• IQR

4. Five Number Summary

• Q0 Min
• Q1 25%
• Q2 50% median
• Q3 75%
• Q4 100%

5. Data Visualization & Visual Data Validation

• Bar chart
• Pie chart
• Area plot
• Scatter plot
• Surface
• Stock plot
• Radar
• Tree map
• Waterfall
• Heatmap
• Bubble chart
• Line chart
• Histogram
• Standardized plot
• Stem leaf
• Boxplot
• Skewed plot
• Lipto kurtic plot
• Plato kurticplot
• Masso kurtic plot
• PP plot
• QQ plot

Part-3: Sampling Techniques Big Data

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

Part-4: Probability

• Simple Probability
• Marginal Probability
• Joint Probability
• Conditional probability (Bayes’ Theorem probability
• Discrete Distributions
• Binomial Distribution
• Expected Mean
• Variance
• Bivariate destruction
• Covariance
• Hypergeometric Distributions
• Poisson Distribution
• Continuous Distributions
• Random Sample
• Simple Random sample
• Stratified Random sample
• Systematic Random sample
• Cluster random sample

Module 3: Data Validation Normality

Part-5: Data Validation Data Normality

• Univariate normality techniques
• Bivariate 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

Part – 6: 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

Part-7: 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),
• ANCOVA
• 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

Part- 8 Data Transformation

• Sqrt Transformation
• Log transformation
• Arcsine transformation
• Box- Cox transformation
• Square root transformation
• Inverse transformation
• Min Max Data normalization Rescaling
• PCA Transformation

Module 4 Machine Learning AI

Part- 9: Supervised Learning

1. Linear Regression (Functional Models)

• Correlation – Pearson, Kendall, Wilcox
• SLR Regression
• MLR Regression
• Examination Residual analysis
• Residual QQ plot
• Residual EDA Analysis
• Residual Stdadised
• Auto Correlation
• Test of ANOVA Significant
• VIF Analysis
• Test of T-test Significant
• CP Indexing
• Excluding Constant, and excluding constant
• Homoscedasticity
• Heteroscedasticity
• Stepwise regression
• Forward Regression
• Backward Regression
• Multicollinearity
• Cross validation
• MAPE
• Check prediction accuracy
• Standardized regression
• Quadrant Regression
• Dummy Variables Regression

2. 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)

3. Timeseries (Forecasting Models)

• Navie model
• Moving Averages
• Weighted Moving Averages
• Exponential Smoothing

4. Decision Tree

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

5. Naive Bayes

6. KNN

7. SVM

Part-10: Un Supervised Learning

8. PCA/Dimension Reduction Analysis (Un Supervised Learning )

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

9. Cluster Analysis

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

Part-11: Deep Learning

• Neural Network
• ANN
• CNN
• RNN

Part-12: Semi Supervised Learning

• Aprior algorithm
• Association Mining MBA
• Recommendation System

Part -13: Model Validation

• Model Validation and Testing
• Kappa Statistics
• AIC,
• BIC
• Error/ Confusion matrices
• ROC
• APE
• MAPE
• LiftCurve,
• Sensitivity
• Misclassification Rating
• Specificity
• Maximum Absolute Error

Part -14 Text mining

• NLP
• Sentiment Analysis

Part -15 Model Deployment

• Microsoft Azure
• Google Clod
• Amazon WNS

Part-16 Big Data and data warehouse architecture

• Data Integration
• ETL transformation
• Data deployment

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