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.

• Python for Data Science
  • Introduction to Python for Data Science
  • Introduction to Python
  • Python Installation
  • Python Environment Setup
  • Python Packages Installation
  • Variables and Datatypes
  • Operators
  • Python Pandas-Intro
  • Python Numpy-Intro
  • Python SciPy-Intro
  • Python Matplotlib-Intro
  • Python Basics
  • Python Data Structures
  • Programming Fundamentals
  • Working with data in Python
  • Object-oriented programming aspects of Python
  • Jupyter notebooks
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions
  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scraping using

Python

Integrate Python with MapReduce

• Data Basics

• Learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc.
• Learn how to clean data, impute data, scale data, import and export data, and scrape data from the internet.
• Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA).

• Probability and Statistics Basics

• Important statistical concepts used in data science
• Difference between population and sample
• Types of variables
• Measures of central tendency
• Measures of variability
• Coefficient of variance
• Skewness and Kurtosis
• Inferential Statistics
• Regression and ANOVA

• Exploratory Data Analysis

• Data visualization
• Missing value analysis

• Introduction to Big Data
• Introduction to Hadoop
• Introduction to Tableau
• Introduction to Business Analytics
• Introduction to Machine Learning Basics

• Supervised vs Unsupervised
• Time Series Analysis
• Text Mining

• Data Science Capstone Project

Science and Mechanical Data require in-depth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills,