# Data Science Online Training Institute In Hyderabad

### Data Science Course Modules :-

Module-1: Introduction Data science & Business Analytics

Module-2: Descriptive Statistics

Module-3: Basic Probability for Business issues:

Module-4: Basic Distributions:

Module-5: Sampling Technique Big Data

Module-6: Data Validation & Data Normality

Module-7: Data cleaning process Quality check

Module-8: Data Imputation and outlier treatment

Module-9: Test of Hypothesis

Module-10: Data Transformation

Module-11: Predictive modeling & Diagnostics

Module-12: Logistic Regression Analysis

Module-13: Big Data Analytics

Module-14: Cluster Analysis and Methods

Module-15: Data Mining Machine Learning and Artificial Intelligence

Module-16: Time series

Module-17: Model Validation and Testing

Note : Open source and commercial Tools is a part of training.

### 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
• Centroid distance Methods
• 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