Data Science

Data Science

This course gives business leaders the skills and knowledge to better manage such analytical efforts. This course describes how to get started and what is required to effectively run projects which leverage Big Data analytics.

Specifically, it addresses: deriving business value from Big Data, leading Data Science projects using a data analytics lifecycle, developing Data Science teams and driving innovation using analytics.




This course gives business leaders the skills and knowledge to better manage such analytical efforts. This course describes how to get started and what is required to effectively run projects which leverage Big Data analytics.

 

Data Virtualization and summarization

 

MODULE – 1

Part-1 Descriptive Statistics:

  • Introduction to Advanced Data Analytics
  • Statistical inferences Types of Variables
  • Measures of central tendency
  • Dispersion
  • Variable Distributions
  • Probability
  • Distributions
  • Normal Distribution and Properties

Part-2: Data quality outlier

  • Robust measurements
  • Outlier treatment with central tendency
  • Replacing with series means or median values
  • Z score Calculation
  • Data Normalization
  • Sampling and estimation

Part-3: Test of Hypothesis

  • Null/Alternative Hypothesis formulation
  • Type I and Type II errors
  • One Sample TTEST
  • Paired TTEST
  • Independent Sample TTEST
  • ANOVA,
  • MANOVA
  • Chi Square Test
  • Kruskal-Wallis,Mann-Whitney,
  • Wilcoxon,
  • McNemar test

Data preparation and Quality check

Module -2

Part-4: Data Validation & Imputation

  • Univariate procedure
  • Q-Q probability plots
  • Cumulative frequency (P P) plots
  • Explorer analysis
  • Steam and leaf analysis
  • Kolmogorov Smirnov test
  • Shapiro Wilks test

Part-5: Data Transformation

  • Log transformation (s)
  • Arcsine transformation
  • Box- Cox transformation
  • Square root transformation
  • Log transformation (s)
  • Inverse transformation
  • Min- Max Normalization

Predictive Analytics

Module – 3

Part-6: Predictive modeling & Diagnostics

  • Correlation – Pearson, Kendall
  • SLR Regression
  • MLR Regression
  • Residual analysis
  • Auto Correlation
  • VIF Analysis
  • Indexing Eigen Value interpretation
  • Homoscedasticity
  • Homogeneity
  • Stepwise regression
  • Transformation of variables

Part-7 Logistic Regression Analysis

  • Discriminant and Logit Analysis
  • Multiple Discriminant Analysis
  • Stepwise Discriminant Analysis Binary
  • Logit Regression
  • Estimation of probability using logistic regression, Wald Test
  • Hosmer Lemshow

Advanced Analysis

Module – 4

Part-8: Factor Analysis

  • Introduction to Factor Analysis – PCA
  • Reliability Test
  • KMO MSA tests, Eigen Value Interpretation
  • Rotation and Extraction
  • Varimix Models
  • Principle component analysis
  • Conformity Factor Analysis
  • Exploitary Factor Analysis

Part-9: Cluster Analysis

  • Introduction to Cluster Techniques
  • Distance Methodologies,
  • Hierarchical and Non-Hierarchical Procedures K Means clustering
  • Wards Method

Part- 10: Conjoint Analysis

  • Statistics and terms Association with Conjoint Analysis
  • Assumption and limitation of conjoint analysis
  • Hybrid Conjoint Analysis

Part –11: Time Series Forecasting

  • Smoothing and annual Time series
  • Time series forecasting for seasonal data
  • Multiplicative Models
  • Additive Models

Data Mining for Business Intelligence

Part -12: Data Mining

  • Data partition (Training, Validating Testing)
  • Data Explore
  • Data Testing
  • Data Transform
  • Linear Model
  • SVM Model
  • Tree Analysis
  • RandomForest Analysis
  • Model Evaluation
  • ROC
  • Lift Curve
  • Sensitivity
  • Error/ Confusion matrices

Part -13: Business Intelligence

  • Data Warehousing for Data Modeling
  • Data Warehousing for Report Building
  • Stars Schemes for Data Marts
  • Multi dimensional summarization (OLAP)
  • Web analytics (Concepts)

Big Data Analysis

Part -14 Hadoop

  • Introduction to big data
  • Sources of big data
  • Hadoop distributed file system
  • Employing Hadoop MapReduce
  • Statistical Analysis of Big Data

 

  • Can I get recorded sessions of a live class?

    Yes, this can be done. Moreover, this ensures that when you will start with your batch, the concepts explained during the classes will  be recorded and available to you .

  • How will I execute the Practicals?

    We will help you to setup the required environment for practicals.

  • I have a windows system. Can that be used to work on the assignments?

    Yes, One can always use Windows to work on assignments. Our 24*7 team support will guide you to get the set-up ready.

Vidhyalive certified ‘Data Science Expert’ based on your project performance, reviewed by our expert panel.

CONTACT US

Online Classroom

25th June
Sat
9PM-10PM EST
$1200
27th June
Mon-Friday
9PM-10PM EST
$1200

Course Feature

Online Classes: 60 Hrs

60 live classes of 1 hrs each by Industry practitioners

Assignments: 60 HRS

Personal assistance/installation guides for setting up the required environment for Assignments / Projects

Project: 60 HRS

Live project based on any of the selected use cases, involving Big Data Analytics , leading Data Science projects using a data analytics lifecycle, developing Data Science teams and driving innovation using analytics.

Lifetime Access: Life time

Lifetime access to the learning management system including Class recordings, presentations, sample code and projects

24 x 7 Support

Lifetime access to the support team (available 24/7) in resolving queries during and after the course completion

Get Certified

Vidhyalive certified ‘Data science Expert’ based on your project performance, reviewed by our expert panel