Data Science Course Content

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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
  • 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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