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Self Paced Certificate Course on Data Science





Data science is a rapidly evolving field that plays a crucial role in various industries. It is essential for professionals to stay updated on the latest trends and technologies to harness the full potential of data science in their work.

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 practice problems, real-life case study descriptions and project ideas for doing research.




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