Data Science with Python

Great skills are necessary to undertake an intensive and challenging Course that is taught and examined with Multiple Tools. To help students gain the necessary tool skills, we can provide Data Science course training with tools that can help them improve their course proficiency. Students can find course details here R-programming with hands on 15 + case studies List of Case Studies. Altogether 250+ hours of programme taught by expertise with 20 years of through experience in the field of data science.

odule1 1

Foundations of Date Science: Data Visualization and Interpretation

Part -1 Referential details for Data science Business Analytics

Scope & Fact of Data Science and Business analytics

SWOT Analysis of Data Science Business Analytics

Introduction to Advanced Data Analytics

Journey Mathematics-Statistics-Econometrics

Flow chart for Data Science and Business Analytics

Data wherehouse conceptual discussions

Hadoop for Data Science

OLTP OLAP for Data information

Web Application report

Part-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 data

Exploration techniques for Character Data

Visualization Exploration

Summary Exploration

Chebychev s Inequality.

Part-3: Basic Probability for Business Issues:

Simple Probability

Marginal Probability

Joint Probability

Conditional probability (linked with decision Tress Algorithms)

Bayes Theorem probability (linked with Na ve Bayes Algorithms)

Discrete Distributions

Binomial Distribution

Hypergeomatric Distributions

Poisson Distribution

Continuous Distributions

Normal Distribution and Properties

Scandalized Distributions

Part-4: Sampling Techniques Big Data

Sampling Distributions

Simple Random

Systematic Sample

Stratified sample

Cluster Sample

Standard Error of the Mean

Skewed Std. Error

Kurtosis Std. Error

Central Limit Theorem,

Sampling from Infinity

Sampling Distributions for Mean

Sampling Distributions for proportions

Module 2

Data Preprocessing and Imputation

Part-5: Data Validation Data Normality

Unvariate normality techniques

Bivariate techniques

Multivariate techniques

Q-Q probability plots

Cumulative frequency

Explorer analysis

Steam and leaf analysis

Histogram

Box plot

Scores for Normality Check

Kolmogorov Smirnov test

Shapiro Wilks test

Anderson darling test

Part 6 Data Cleaning process Quality check

PCA for Big Data Analysis or Unsupervised data

PCA Regression Scores for Supervised aata

Noise Data detecting

Data cleaning with Regression Residual

Data Scrubbing with statistical sense

Part-7: Data Imputation and outlier treatment

Outlier treatment with robust measurements

Outlier treatment with central tendency Mean

Outlier with Min Max Likelihood methods

Outlier Detection with Density Based

Visualize Outlier Treatment

Outlier with Residual Analysis

Outlier Detection with PCA Analysis

Data Imputation with series Central Tendency

Part-8: Test of Hypothesis

Null Hypothesis formulation

Alternative Hypothesis

Type I and Type II errors

Power Value

One tail and Two tail

One Sample T-TEST

Paired T-TEST

Independent Sample T-TEST

Analysis of Variance ( ANOVA),

MANOVA

Chi Square Test

Kendall Chi Square

Kruskal-Wallis Rank Test Chi Square

Mann-Whitney, Chi Square

Wilcoxon, Chi Square

McNemar test Chi Square

Part-9: Data Transformation

Log transformation

Box- Cox transformation

Square root transformation

Inverse transformation

Min Max Data normalization

Module 3

Predictive Analytics: Supervised Learning Algorithms

Part-10: Predictive modeling & Diagnostics

Correlation

SLR Regression

MLR Regression

Examination Residual analysis

Auto Correlation

Test of ANOVA Significant

VIF Analysis

Test of Ttest Significant

CP Indexing

Eigen Value for PCA Analysis

Homoscedasticity

Heteroskedasticity

Stepwise regression

Forward Regression

Backward Regression

Multicollinearity

Cross validation

MAPE

Check prediction accuracy

Standized regression

Quadraint Regression

Transformed Regression

Dummy Variables Regression

Part-11 Logistic Regression Analysis

Logistic Regression

Discriminate Regression Analysis

Multiple Discriminant Analysis

Stepwise Discriminant Analysis

Logit function

Test of Associations

Chi-square strength of association

Binary Regression Analysis

Profit and Logit Models

Estimation of probability using logistic regression,

Wald Test statistics for Model

Hosmer Lemshow

Nagurkake R square

Pseudio R square

Maximum likelihood estimation

Model Fit

Model cross validation

Discrimination functions

AIC

BIC (Bayesian information criterion)

Kappa Statistics

AIC

BIC

Error/ Confusion matrices

ROC

APE

MAPE

Lift Curve

Sensitivity

Misclassification Rating

Specificity

Maximum Absolute Error

Recall

Miss classification

Root Final Prediction Error

Gini Coefficient

Schwarz s Bayesian Criterion

Module 4

(Advanced Analytics 1) unsupervised Learning Algorithms

Part-12: Dimension Reduction Analysis

Introduction to Factor Analysis

Principle component analysis

Reliability Test

KMO MSA tests, Eigen Value Interpretation,

Rotation and Extraction steps

Varmix Models

Conformity Factor Analysis

Exploitary Factor Analysis

Factor Score for Regression

Part-13: Cluster Analysis

Introduction to Cluster Techniques

Hierarchical clustering

K Means clustering

Wards Methods

Agglomerative Clustering

Variation Methods

Maximum distance Linkage Methods

Centroid distance Methods

Minimum distance Linkage Method

Cluster Dengogram,

Ecludin distance method s

Module 5

Forecasting and Operations Analytics

NAvie Forecsting

Moving Average

Exponecial smoothing

ARIMA

REfere Time series ppt

Auto-Regressive Integrated

Moving Average (ARIMA) models,

ARIMAX.

Conjoint analysis,

Discriminant analysis.

Module 5

(Advanced Analytics 3) Machine Learning Algorithms

Prediction

Support Vector Machines (SVM)

Binary Regression/Logit Model

Probit Model

Na ve Bayes

Na ve Bayes Multinomial

Ordinal Regression

Multinomial Regression

k-Nearest Neighbor Classification



Decision Stump

CHAID Analysis

Recommender Systems,

Collaborative Filtering

Advanced recommender system.

Bootstrap Aggregating (Bagging),

Random forest,

Adaptive boosting,

gradient boosting

Support vector machine

Neural Network

C4.5 / C5.0

J48 Pruning, Uprunning

Decision trees



Module 6

(Advanced Analytics 4) Artificial Intelligence (3 Days)

Introduction to neural networks; rule

based expert systems

Introduction to artificial neural

networks (ANN); Neuron as

computing element; Perceptron:

McCullogh-Pitts model; Backpropagation

algorithm; Multi-layer

Neural Networks

Deep learning algorithms:

Convolutional networks; Recurrent

nets; Auto-encoders;

Deep Learning Platform: H2O.ai;

Dato GraphLab; Tensor Flow

Module 7

(Advanced Analytics 5)

Suvervial Analysis

Mantel Haenszel Test

Kaplan-Meier (Product- Limit) Estimator

Cox s Proportional Hazards Model

Cox Snell Residual

Hazard Functions

Proportional Hazards Assumption



Module 8

(Advanced Analytics 2)

Big Data Analytics



Introduction to BigData

sources of Big Data

Big Data technologies: Hadoop distributed

file system; Employing Hadoop

Statistical Analysis of Big Data.

Pig

Hive

MapReduce

NoSQL