Data Science Course brochure

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Course in Data Science

Contact: +917095167689

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 / Class Room trainings are available.

Class Room Training Venue:

 KPHB. For more details, call on +917095167689.

Faculty Details:

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Module:1 -

Descriptive & Inferential Statistics:(20 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

3. Sampling Distributions

 Central Limit Theorem

 Sampling Distributions for Sample Proportion, p-hat

 Sampling Distribution of the Sample Mean, x-bar

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

6. Comparing Two Groups

 Comparing Two Groups

 Comparing Two Independent Means, Proportions

 Pairs wise testing for Means  Two Variances Test(F-Test)

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

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Module:2 -

Applied Regression Methods(20Hrs)

1. Simple Linear Regression(SLR)

 Prerequisite Mathematics

 The Simple Linear Regression Model  What is The Common Error Variance?  The Coefficient of Determination  Hypothesis Test for the Population

Correlation Coefficient

2. SLR Model Evaluation

 Inference for the Population Intercept and Slope

 The Analysis of Variance (ANOVA) table and the F-test

 Equivalent linear relationship tests  Decomposing the Error

 The Lack of Fit F-test

3. SLR Estimation & Prediction

 Confidence Interval for the Mean

Response

 Prediction Interval for a New Response  Using Software-Real Time Problems

4. SLR Model Assumptions

 Model Assumptions Diagnostics  Using Software-Real Time Problems

5. Multiple Linear

Regression(MLR)

 The Multiple Linear Regression Model  Using Software-Real Time Problems

6. MLR Model Evaluation

 The General Linear Test

 Sequential (or Extra) Sums of Squares  The Hypothesis Tests for the Slopes  Partial R-squared

 Lack of Fit Testing in the Multiple Regression Setting

7. MLR Estimation, Prediction &

Model Assumptions

 Confidence Interval for the Mean

Response

 Prediction Interval for a New Response  Model Assumptions Diagnostics  Using Software-Real Time Problems

8. Categorical Predictors

 Coding Qualitative Variables  Additive Effects

 Interaction Effects

9. Data Transformations

10. Model Building

 Forward Selection/Backward Elimination  Stepwise Regression

 Adjusted R-Sq, Mallows Cp, PRESS, AIC, BIC, SBC, AICC

 Outliers and Influential Data Points  Cooks Distance/DIFBETAS/DFFITS  Using Software-Real Time Problems

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Module:3 -

Applied Time Series Analysis(10Hrs)

1. Time Series Basics

 Overview

 ACF and AR(1) Model

2. MA Models, PACF

 Moving Average Models (MA models)  PACF

3. ARIMA models

 Non-seasonal ARIMA  Diagnostics

 Forecasting

4. Seasonal Models

 Seasonal ARIMA

 Identifying Seasonal Models

5. Smoothing and Decomposition

Methods

 Decomposition Models  Smoothing Time Series

6. Periodogram

 Periodogram

7. Regression with ARIMA errors;

CCF; 2 Time Series

 Linear Regression Models with

Autoregressive Errors

 CCF and Lagged Regressions  Using Software-Real Time Problems

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Module:4 –

Applied Multivariate Analysis (20hrs)

1. Measures of Central Tendency,

Dispersion and Association

 Measures of Central Tendency/

Measures of Dispersion

2. Graphical Display of Multivariate

Data

 Graphical Methods

3. Multivariate Normal Distribution

 Exponent of Multivariate Normal

Distribution

 Multivariate Normality and Outliers  Eigenvalues and Eigenvectors  Spectral Value Decomposition  Single Value Decomposition

4. Sample Mean Vector and

Sample Correlation

 Distribution of Sample Mean Vector  Interval Estimate of Population Mean  Inferences for Correlations

5. Principal Components Analysis

(PCA)

 Principal Component Analysis (PCA) Procedure

6. Factor Analysis

 Principal Component Method  Communalities

 Factor Rotations

7. Discriminant Analysis

 Bayes Rule and Classification Problem  Discriminant Analysis (Linear/Quadratic)  Estimating Misclassification Probabilities  Using Software-Real Time Problems

8. Cluster Analysis

 Agglomerative Hierarchical Clustering  K-Means Procedure

 Meloid Cluster Analysis

9. MANOVA

 MANOVA

 Test Statistics for MANOVA  Hypothesis Tests

 MANOVA table

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Module:5 -

Machine Learning(30hrs)

1. Introduction

 Application Examples  Supervised Learning  Unsupervised Learning

2. Regression Shrinkage Methods

 Ridge Regression

 Lasso

3. Classification

 Logistic Regression  Discriminant Analysis  Nearest-Neighbor Methods

4. Tree-based Methods

 The Basics of Decision Trees  Regression Trees

 Classiﬁcation Trees  Ensemble Methods

 Bagging, Boosting, Bootstrap, Random Forests

5. Neural Networks

 Introduction

 Single Layer Perceptron  Multi-layer Perceptron

 Forward Feed and Backward Propagation  Using Software-Real Time Problems

6. Support Vector Machine

 Support Vector Classiﬁer  Support Vector Machine

 SVMs with More than Two Classes  Using Software-Real Time Problems

7. Dimension Reduction Methods

 Principal Components Regression (PCR)  Partial Least Squares (PLS)

8. Association rules

 Market Basket Analysis

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Module:6 -

SAS/R Programming (20hrs)

1. Base SAS

 Working with SAS program syntax  Examining SAS data sets

 Accessing SAS libraries  Producing Detail Reports  Sorting and grouping report data  Enhancing reports

 Formatting Data Values  Creating user-defined formats  Reading SAS Data Sets  Customizing a SAS data set  Handling missing data  Manipulating Data

 Combining SAS Data Sets  Creating Summary Reports  Controlling Input and Output  Summarizing Data

 Reading Raw Data Files  Data Transformations  Debugging Techniques  Using the PUTLOG statement  Processing Data Iteratively  Restructuring a Data Set

 Creating and Maintaining Permanent Formats

2. SAS SQL

 Basic Queries  Sub-Queries  Joins (SQL)  Operators

 Creating Tables and Views  Managing Tables

3. SAS Macros

 Macro Variables  Definitions

 Data Step and SQL Interfaces

4. R Programming

 RCMDR Package  Rattle Package