100% Discount || Data Science with Python Complete Course

Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more.

As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning?

DATA SCIENCE COURSE-OVERVIEW

• Getting Started with Data Science

• Define Data
  • Why Data Science?
  • Who is a Data Scientist?
  • What does a Data Scientist do?
  • The lifecycle of Data Science with the help of a use case
  • Job trends
  • Data Science Components
  • Data Science Job Roles

• Math Basics
  • Multivariable Calculus
  • Functions of several variables
  • Derivatives and gradients
  • Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
  • Cost function
  • Plotting of functions
  • Minimum and Maximum values of a function

• Linear Algebra
  • Vectors
  • Matrices
  • Transpose of a matrix
  • The inverse of a matrix
  • The determinant of a matrix
  • Dot product
  • Eigenvalues
  • Eigenvectors

• Optimization Methods
  • Cost function/Objective function
  • Likelihood function
  • Error function
  • Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm)

• Programming Basics

• R Programming for Data Science
  • History of R
  • Why R?
  • R Installation
  • Installation of R Studio
  • Install R Packages.
  • R for business
  • Features of R
  • Basic R syntax
  • R programming fundamentals
  • Foundational R programming concepts such as data types, vectors arithmetic, indexing, and data frames
  • How to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2
  • Understand and use the various graphics in R for data visualization.
  • Gain a basic understanding of various statistical concepts.
  • Understand and use hypothesis testing method to drive business
  • decisions.
  • Understand and use linear, non-linear regression models, and
  • classification techniques for data analysis.
  • Working with data in R
  • Master R programming and understand how various statements are executed in R.