Certified Data Science Coder And Engineer (Cdsce)

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

What you'll learn

R as a programming language and Develop R codes for data science solutions

The mathematical foundations required for data science

The first level data science algorithms

A data analytics problem solving framework and Assess the solution approach

Construct use cases to validate approach and identify modifications required

Requirements

Any interested learner, Computer and MS Excel , R Programming

Description

Certified Data Science for Engineers & Professionals (CDSEP)Course Description: Are you an engineer or a technical professional looking to enhance your data science skills? Look no further! The Certified Data Science for Engineers and Professionals course is here to provide you with the knowledge and tools necessary to excel in the field of data science. This comprehensive course is designed to bridge the gap between engineering principles and data analysis, empowering you to leverage data-driven insights for informed decision-making and problem-solving.Throughout this course, you will embark on an exciting journey through various key topics in data science. In Session 1, we will delve into the course philosophy and introduce you to the powerful programming language R, a staple in the data science toolkit.Session 2 focuses on Linear Algebra for data science. You will explore the algebraic and geometric views of vectors and matrices, understanding concepts such as product of matrix and vector, rank, null space, solution of over-determined sets of equations, and pseudo-inverse. Additionally, you will dive into the geometric interpretation of vectors, distance, projections, and eigenvalue decomposition.Statistics takes center stage in Session 3, where you will learn about descriptive statistics, the notion of probability, and various distributions. You will gain insights into mean, variance, covariance, and covariance matrix, along with a solid understanding of univariate and multivariate normal distributions. An introduction to hypothesis testing and confidence interval estimation will equip you with essential statistical tools.Session 4 brings you to the realm of Optimization, exploring techniques to maximize or minimize objective functions. You will learn about different optimization algorithms and how to apply them to real-world scenarios.In Session 5, we delve deeper into Optimization and introduce you to the typology of data science problems. By understanding different types of problems, you'll develop a solution framework to tackle them effectively.Session 6 is dedicated to Regression Analysis. You will start with Simple Linear Regression, examining assumptions used in linear regression models. Then, you will progress to Multivariate Linear Regression, where you'll explore model assessment, the importance of different variables, and subset selection techniques.Classification takes the spotlight in Session 7. You will learn about Logistic Regression, a powerful method for binary classification tasks. We'll dive into the intricacies of logistic regression, understanding how to apply it to real-world problems.Session 8 concludes the course by introducing Classification using k-Nearest Neighbors (kNN) and k-means Clustering. You will gain proficiency in these fundamental machine learning techniques and understand how they can be utilized to solve classification problems.By the end of this course, you will have acquired a solid foundation in data science for engineering applications. With the Certified Data Science for Engineers and Professionals course, you'll be equipped with the skills and knowledge to leverage data effectively, make informed decisions, and propel your career to new heights. Enroll now and embark on your journey to become a certified data scientist!

Overview

Section 1: Introduction to R

Lecture 1 Course philosophy and introduction to R

Lecture 2 Introduction to R

Lecture 3 Introduction to R (Continued)

Lecture 4 Variables and datatypes in R

Lecture 5 Data frames

Lecture 6 Recasting and joining of dataframes

Lecture 7 Arithmetic,Logical and Matrix operations in R

Lecture 8 Advanced programming in R Functions

Lecture 9 Advanced Programming in R Functions (Continued)

Lecture 10 Control structures

Lecture 11 Data visualization in R Basic graphics

Section 2: Understanding Linear algebra for data science

Lecture 12 Linear Algebra for Data science

Lecture 13 Solving Linear Equations

Lecture 14 Solving Linear Equations

Lecture 15 Solving Linear Equations ( Continued )

Lecture 16 Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors

Lecture 17 .Linear Algebra - Distance,Hyperplanes & Halfspaces,Eigenvalues,Eigenvectors-II

Lecture 18 16.Linear Algebra-Distance,Hyperplanes & Halfspaces,Eigenvalues,Eigenvectors-III

Lecture 19 Linear Algebra Distance,Hyperplanes & Halfspaces,Eigenvalues,Eigenvectors-IV

Section 3: Deep Dive into Statistics

Lecture 20 Statistical Modelling

Lecture 21 Random Variables and Probability Mass Density Functions

Lecture 22 Random Variables and Probability Mass Density Functions

Lecture 23 Hypothesis Testing

Section 4: Understanding Optimization Principles for Data Science

Lecture 24 Optimization for Data Science

Lecture 25 Unconstrained Multivariate Optimization

Lecture 26 Unconstrained Multivariate Optimization ( Continued )

Lecture 27 Gradient ( Steepest ) Descent ( OR ) Learning Rule

Section 5: Optimization and Typology of data science problems solution framework

Lecture 28 Multivariate Optimization With Equality Constraints

Lecture 29 Multivariate Optimization With Inequality Constraints

Lecture 30 .Introduction to Data Science

Lecture 31 Solving Data Analysis Problems - A Guided Thought Process

Section 6: Simple and Multivariate linear regression

Lecture 32 Module Predictive Modelling

Lecture 33 Linear Regression

Lecture 34 Model Assessment

Lecture 35 Diagnostics to Improve Linear Model Fit

Lecture 36 Simple Linear Regression Model Building

Lecture 37 Simple Linear Regression Model Assessment

Lecture 38 Simple Linear Regression Model Assessment (Continued)

Lecture 39 Muliple Linear Regression

Section 7: Classification using logistic regression

Lecture 40 Cross Validation

Lecture 41 Multiple Linear Regression Modelling Building and Selection

Lecture 42 Classification

Lecture 43 Logisitic Regression

Lecture 44 Logisitic Regression Contiinued

Lecture 45 Performance Measures

Lecture 46 Logisitic Regression Implementation in R

Section 8: Classification using kNN and k-means clustering

Lecture 47 .K - Nearest Neighbors (kNN)

Lecture 48 .K - Nearest Neighbors implementation in R

Lecture 49 K - means Clustering

Lecture 50 K - means implementation in R

Lecture 51 Data Science for engineers - Summary

Engineers: The course is primarily designed for engineers who want to incorporate data science techniques into their work,Engineering Managers: Engineering managers who want to understand and utilize data science concepts to guide their teams and make data-driven decisions can benefit from this course.,Data Analysts: If you are a data analyst working in an engineering context, this course can provide you with a deeper understanding of engineering principles and challenges.,Technical Professionals: Professionals with a technical background who are interested in exploring the field of data science and its applications in engineering can attend this course.,Graduates and Students: Recent engineering graduates or students who want to develop a competitive edge in the job market can find value in this course