Requirements

An understanding of the fundamentals of Python programming

Basic knowledge of statistics
Description
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 COURSEOVERVIEW
 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, nonlinear 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 PandasIntro
 Python NumpyIntro
 Python SciPyIntro
 Python MatplotlibIntro
 Python Basics
 Python Data Structures
 Programming Fundamentals
 Working with data in Python
 Objectoriented 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 highlevel mathematical computing using the NumPy package and its vast library of mathematical functions
 Perform scientific and technical computing using the SciPy package and its subpackages 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 indepth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, KNN 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 indepth 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,
Who this course is for:
 For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field.
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Data Science with Python Complete Course
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