BEST DATA SCIENCE TRAININGS

Course in Data Science

About the Course:
In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like R Programming, SAS, MINITAB and EXCEL.
Course features:
140+ hours of teaching
Exam on every weekend
Exclusive doubt clarification session on every weekend
Real Time Case Study driven approach
Live Project
Placement assistance
Qualification
Any Graduate. No programming and statistics knowledge or skills required
Duration of the course:
3 months (Every day 2 hours of teaching).
Classes on week days.
Mode of course delivery
Online Training
Faculty Details:
A team of faculty having an average 20 + years experience in the data analysis across various industries and training.


Module:1 - Descriptive & Inferential Statistics:(30 Hrs)

1. Turning Data into Information
Data Visualization
Measures of Central Tendency
Measures of Variability
Measures of Shape
Covariance, Correlation
Using Software-Real Time Problems
2. Probability Distributions
Probability Distributions: Discrete Random Variables
Mean, Expected Value
Binomial Random Variable
Poisson Random Variable
Continuous Random Variable
Normal distribution
Using Software-Real Time Problems
3. Sampling Distributions
Central Limit Theorem
Sampling Distributions for Sample Proportion, p-hat
Sampling Distribution of the Sample Mean, x-bar
Using Software-Real Time Problems
4. Confidence Intervals
Statistical Inference
Constructing confidence intervals to estimate a population Mean, Variance, Proportion
Using Software-Real Time Problems 5. Hypothesis Testing
Hypothesis Testing
Type I and Type II Errors
Decision Making in Hypothesis Testing
Hypothesis Testing for a Mean, Variance, Proportion
Power in Hypothesis Testing
Using Software-Real Time Problems
6. Comparing Two Groups
Comparing Two Groups
Comparing Two Independent Means, Proportions
Pairs wise testing for Means
Two Variances Test(F-Test)
Using Software-Real Time Problems
7. Analysis of Variance (ANOVA)
One-Way and Two-way ANOVA
ANOVA Assumptions
Multiple Comparisons (Tukey, Dunnett)
Using Software-Real Time Problems
8. Association Between Categorical Variables
Two Categorical Variables Relation
Statistical Significance of Observed Relationship / Chi-Square Test
Calculating the Chi-Square Test Statistic
Contingency Table
Using Software-Real Time Problems


Module:2 Prediction Analytics (25Hrs)
1. Simple Linear Regression
Simple Linear Regression Model
Least-Square Estimation of the Parameters
Hypothesis Testing on the Slope and Intercept
Coefficient of Determination
Estimation by Maximum Likelihood
Using Software-Real Time
2. Multiple Regression
Multiple Regression Models
Estimation of Model Parameters
Hypothesis Testing in Multiple Linear Regression
Multicollinearity
Using Software-Real Time Problems
3. Model Adequacy Checking
Residual Analysis
The PRESS Statistic
Detection and Treatment of Outliers
Lack of Fit of the Regression Model
Using Software-Real Time Problems
4. Transformations
Variance-Stabilizing Transformations
Transformations to Linearize the Model
Analytical Methods for selecting a Transformation
Generalized and Weighted Least Squares
Using Software-Real Time Problems
5. Multiple Linear Regression
The Multiple Linear Regression Model
Using Software-Real Time Problems 6. Diagnostics for Leverage and Influence
Leverage/ Cook s D /DFFITS/DFBETAS
Treatment of Influential Observations
Using Software-Real Time Problems
7. Polynomial Regression
Polynomial Model in One/ Two /More Variable
Orthogonal Polynomials
Using Software-Real Time Problems
8. Dummy Variables
The General Concept of Indicator Variables
Using Software-Real Time Problems
9. Variables Selection and Model Building
Forward Selection/Backward Elimination
Stepwise Regression
Using Software-Real Time Problems
10. Generalized Linear Models
Concept of GLM
Logistic Regression
Poisson Regression
Negative Binomial Regression
Exponential Regression
11. Autocorrelation
Regression Models with Autocorrelation Errors

Module:3 Applied Multivariate Analysis (25hrs)
1. Measures of Central Tendency, Dispersion and Association
Measures of Central Tendency/ Measures of Dispersion
Using Software-Real Time Problems
2. Multivariate Normal Distribution
Exponent of Multivariate Normal Distribution
Multivariate Normality and Outliers
Eigenvalues and Eigenvectors
Spectral Value Decomposition
Single Value Decomposition
Using Software-Real Time Problems
3. Sample Mean Vector and Sample Correlation
Distribution of Sample Mean Vector
Interval Estimate of Population Mean
Inferences for Correlations
Using Software-Real Time Problems
4. Principal Components Analysis (PCA)
Principal Component Analysis (PCA) Procedure
Using Software-Real Time Problems
5. Factor Analysis
Principal Component Method
Communalities
Factor Rotations
Varimax Rotation
Using Software-Real Time Problem
6. Discriminant Analysis
Discriminant Analysis (Linear/Quadratic)
Estimating Misclassification Probabilities
Using Software-Real Time Problems
7. MANOVA
MANOVA
Test Statistics for MANOVA
Hypothesis Tests
MANOVA table
Using Software-Real Time Problems

Module:4 - Machine Learning(30hrs)