Reachout Analytics

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Data Science

Businesses are increasingly looking to take advantage of Big Data to be competitive. In addition to Data Scientists, organizations need data-savvy business leaders who can identify opportunities to solve business problems using advanced analytics and who have the expertise to lead an analytical team.

This course gives business leaders the skills and knowledge to better manage such analytical efforts. It 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.

Starts on : 25-07  2018

Data Science Content

Data Visualization and Summarization

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,
  • 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

Course Curriculum

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