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
- Data Type
- Continues
- Discrete
- Nominal
- Ordinal
- Bina
- Measures of central tendency
- Mean
- Median
- Mode
- Geomean
- Harman
- TrimmedMean
- Weighted Mean
- 95% CI L mean
- 95% CI U mean
- Data Viability Dispersion
- Std
- Variance
- Coefficient Of Variance
- Range
- Min
- Max
- skewed
- kurtosis
- std Error
- std Skewed
- Error Kurtosis
- IQR
- Five Number Summary
- Q0 Min
- Q1 25%
- Q2 50% median
- Q3 75%
- Q4 100%
- 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
- 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
- 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
Part-10: Un Supervised Learning
- 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
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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