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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:
    شناسه: 16226
    حجم: 13067 مگابایت
    مدت زمان: 1264 دقیقه
    تاریخ انتشار: ۲۵ تیر ۱۴۰۲
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    139,000 تومان
    افزودن به سبد خرید