Introduction to Data Science with Practice Problems and Project Ideas

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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 assignments and tests. Materials on every topic will be emailed which will be usable even after you end the subscription. However you will miss the updates and the new real-life case studies that will be added regularly to the course if you terminate your subscription.

Course Contents :

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

Statistical Techniques :

Outlier Detection:

Chauvenet Method
Dixon Thompson Method
Rosner's Method
Ten Most Recognizable Case Studies of Using Outlier Detection for Model Development

Trend Detection

What is a Trend and How do you detect a trend in a set of data?
Runs Test

Correlation Determination

Auto and Cross-Correlation

Risk and Uncertainty

What is Risk and Vulnerability?
Weibull’s Method
Numerical Analysis
Uncertainty Analysis

Linear Regression

Non-Linear Regression

Artificial Intelligence

Fundamentals

What is Artificial Neural Network(ANN)?
Parameter Estimation
Training Algorithms
Testing of the Network
Validation of the Networks
Example Problems

Training Algorithms

Quick Propagation
Conjugate Gradient Descent
Newtons Method
Quasi Newton
Levenberg Marquardt

Advanced ANN

Polynomial Neural Networks
Group Method of Data Handling

Testing ANN Performance

ERROR IDENTIFICATION METRICS
REGRESSION ERROR
CLASSIFICATION ERROR
CORRELATION IDENTIFICATION METRICS
EFFICIENCY IDENTIFICATION METRICS

Applications in Case Studies

Example Application in Water Resource Development

Software

No Code Software for ANN Application

Multi-criteria Decision Making Methods(MCDM)

Basics of MCDM

What is MCDM?
Working Principle of MCDM

Compensatory MCDM Methods

Tutorial on Analytical Hierarchy Process
Tutorial on Analytical Network Process
Tutorial on Multi-Attribute Utility Theory
Tutorial on EVAMIX

Outranking MCDM Methods

Tutorial on ELECTRE I
Tutorial on PROMETHEE I and II

Applications in Case Studies

Applications of MCDM in Water Resource Development
More Applications

Optimization Technique

Fundamentals

Fundamentals of Optimization Technique
Constraints
Classification of Optimization Techniques

Classical Optimization Techniques

Linear Programming
Dynamic Programming
Quadratic Programme
KKT Conditions
Dynamic Programming
Recursive Equation

Nature-Inspired Optimization Techniques

Particle Swarm Optimization
Ant Colony Optimization
Genetic Algorithm
Glowworm Optimization
FireFly Optimization
Mine Burst Optimization

Duration: One year and more if required

Scope of Publication: Yes

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

Terms and Conditions /Privacy Policy /Cancellation and Refund Policy

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