Certified Data Science Coder and Engineer (CDSCE)

سرفصل های دوره

Graduates or Freshers Unlock the Power of Data Science: Engineer Your Way to Informed Decisions and Data-Driven Success

1. Introduction to R

1. Course philosophy and introduction to R

2. Introduction to R

3. Introduction to R (Continued)

4. Variables and datatypes in R

5. Data frames

6. Recasting and joining of dataframes

7. Arithmetic,Logical and Matrix operations in R

8. Advanced programming in R Functions

9. Advanced Programming in R Functions (Continued)

10. Control structures

11. Data visualization in R Basic graphics

2. Understanding Linear algebra for data science

1. Linear Algebra for Data science

2. Solving Linear Equations

3. Solving Linear Equations

4. Solving Linear Equations ( Continued )

5. Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors

6. .Linear Algebra - Distance,Hyperplanes And Halfspaces,Eigenvalues,Eigenvectors-II

7. 16.Linear Algebra-Distance,Hyperplanes And Halfspaces,Eigenvalues,Eigenvectors-III

8. Linear Algebra Distance,Hyperplanes And Halfspaces,Eigenvalues,Eigenvectors-IV

3. Deep Dive into Statistics

1. Statistical Modelling

2. Random Variables and Probability Mass Density Functions

3. Random Variables and Probability Mass Density Functions

4. Hypothesis Testing

4. Understanding Optimization Principles for Data Science

1. Optimization for Data Science

2. Unconstrained Multivariate Optimization

3. Unconstrained Multivariate Optimization ( Continued )

4. Gradient ( Steepest ) Descent ( OR ) Learning Rule

5. Optimization and Typology of data science problems solution framework

1. Multivariate Optimization With Equality Constraints

2. Multivariate Optimization With Inequality Constraints

3. .Introduction to Data Science

4. Solving Data Analysis Problems - A Guided Thought Process

6. Simple and Multivariate linear regression

1. Module Predictive Modelling

2. Linear Regression

3. Model Assessment

4. Diagnostics to Improve Linear Model Fit

5. Simple Linear Regression Model Building

6. Simple Linear Regression Model Assessment

7. Simple Linear Regression Model Assessment (Continued)

8. Muliple Linear Regression

7. Classification using logistic regression

1. Cross Validation

2. Multiple Linear Regression Modelling Building and Selection

3. Classification

4. Logisitic Regression

5. Logisitic Regression Contiinued

6. Performance Measures

7. Logisitic Regression Implementation in R

8. Classification using kNN and k-means clustering

1. .K - Nearest Neighbors (kNN)

2. .K - Nearest Neighbors implementation in R

3. K - means Clustering

4. K - means implementation in R

5. Data Science for engineers - Summary

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تولید کننده: Udemy-Training