# Introduction to Data Science

A self-paced certificate course on Data Science. Topics like performance metrics, MultiCriteria Decision Making, Artificial Neural Networks, Conventional and Nature-Based Optimization Techniques will be presented with real-life case studies. Practice problems will also be discussed and Weekly Assignments and Monthly Tests will be conducted online.

This self-paced course will also award a certificate on successful completion of all the assignment and tests. Materials on every topic will be emailed which will be usable even after you end the subscription. Although you will miss the updates and the new real-life case studies that will be added in a regular manner to the course if you terminate your subscription.

**Course Contents : **

Find below the topics which are included in this self-paced certificate course:

#### Statistical Techniques :

1)Outliers Detection Techniques

2)Auto Correlation

3)Cross-Correlation

4)Cross Regression Model

5)Distribution Function

6)Probability of Failure

5)Uncertainty Analysis

6)Reliability Analysis

7)Graph Theory

#### Multi-Criteria Decision Making Methods(MCDM)

1) Introduction

2)Classification of MCDM Techniques

3) Basic Working Principle

4) Compensatory Techniques :

a) Analytical Hierarchy Process

b)Analytical Networking Process

c) Weighted Sum Method

d)Weighted Product Method

e)MACBETH

5) Outranking Techniques

a)ELECTRE

b)PROMETHEE

#### Artificial Neural Network

1)Introduction

2)Classification of Neural Networks

3)Training Algorithms

a)Quick Propagation

b)Conjugate Gradient Descent

c)Levenberg Marquadart

d)Quasi-Newton

e)Newtons Method

4)Topology Optimization

a)Genetic Algorithm

b)Trial and Error

5) Polynomial Neural Network

#### Optimization Techniques

1)Introduction to Optimization Technique

2)Difference between Simulation, Prediction and Optimization

3)Classification of Optimization Techniques

4)Particle Swarm Optimization

5)Artificial Neural Network

6)Polynomial Neural Network

7)Ant Colony Optimization

8)Linear Programming

9)Dynamic Programming

and many more...

**Duration**: One year and more if required

**Scope of Publication**: Yes

Each material will also have example problems and real-life case study descriptions

**Introduction to Data Science**

A self-paced certificate course on Data Science. Topics like performance metrics, MultiCriteria Decision Making, Artificial Neural Networks, Conventional and Nature-Based Optimization Techniques will be presented with real-life case studies. Practice problems will also be discussed and Weekly Assignments and Monthly Tests will be conducted online.

This self-paced course will also award a certificate on successful completion of all the assignment and tests. Materials on every topic will be emailed which will be usable even after you end the subscription. Although you will miss the updates and the new real-life case studies that will be added in a regular manner to the course if you terminate your subscription.

Course Contents :Find below the topics which are included in this self-paced certificate course:

## Statistical Techniques :

1)Outliers Detection Techniques

2)Auto Correlation

3)Cross-Correlation

4)Cross Regression Model

5)Distribution Function

6)Probability of Failure

5)Uncertainty Analysis

6)Reliability Analysis

7)Graph Theory

## Multi-Criteria Decision Making Methods(MCDM)

1) Introduction

2)Classification of MCDM Techniques

3) Basic Working Principle

4) Compensatory Techniques :

a) Analytical Hierarchy Process

b)Analytical Networking Process

c) Weighted Sum Method

d)Weighted Product Method

e)MACBETH

5) Outranking Techniques

a)ELECTRE

b)PROMETHEE

## Artificial Neural Network

1)Introduction

2)Classification of Neural Networks

3)Training Algorithms

a)Quick Propagation

b)Conjugate Gradient Descent

c)Levenberg Marquadart

d)Quasi-Newton

e)Newtons Method

4)Topology Optimization

a)Genetic Algorithm

b)Trial and Error

5) Polynomial Neural Network

## Optimization Techniques

1)Introduction to Optimization Technique

2)Difference between Simulation, Prediction and Optimization

3)Classification of Optimization Techniques

4)Particle Swarm Optimization

5)Artificial Neural Network

6)Polynomial Neural Network

7)Ant Colony Optimization

8)Linear Programming

9)Dynamic Programming

and many more...

Duration: One year and more if required

Scope of Publication: YesEach material will also have example problems and real-life case study descriptions

#### Monthly

#### Every 6 months

#### Yearly

Self Explanatory Presentations along with example problems.Weekly Assignments and End of Month Tests and lastly on successfully clearing the tests : Certificate of Completion.

##### Course Duration

One Year but can be extended##### Mean,Media and Mode covered ?

Yes##### Statistical Tests ?

Yes##### Linear and Nonlinear Optimization ?

Yes##### ANN and PNN ?

Yes##### Particle Swarm Optimization ?

Yes##### How many examples provided

Minimum 10 per topic but regularly updated##### What I will miss if I terminate the subscription ?

Regular updates in Examples, Case Studies and New Materials

- Self Explanatory Presentations along with example problems.Weekly Assignments and End of Month Tests and lastly on successfully clearing the tests : Certificate of Completion.
- Course Duration
**One Year but can be extended** - Mean,Media and Mode covered ?
**Yes** - Statistical Tests ?
**Yes** - Linear and Nonlinear Optimization ?
**Yes** - ANN and PNN ?
**Yes** - Particle Swarm Optimization ?
**Yes** - How many examples provided
**Minimum 10 per topic but regularly updated** - What I will miss if I terminate the subscription ?
**Regular updates in Examples, Case Studies and New Materials**