Full Stack Data Science With Python, Numpy And R Programming

Full Stack Data Science With Python, Numpy And R Programming
Last updated 5/2022
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Learn data science with R programming and Python. Use NumPy, Pandas to manipulate the data and produce outcomes | R

What you’ll learn
Learn R programming without any programming or data science experience. R programming, full stack data science, full stack data science with python numpy and r
If you are with a computer science or software development background you might feel more comfortable using Python for data science. R programming, full stack
In this course you will learn R programming, Python and Numpy from the beginning. R programming, full stack data science, full stack data science with python
Learn Fundamentals of Python for effectively using Data Science
Fundamentals of Numpy Library and a little bit more. R programming, full stack data science, full stack data science with python numpy and r programming
Data Manipulation with python, python data science, python machine learning, python pandas, data analysis, machine learning a-z
Learn how to handle with big data, python machine learning, python data science, r programming and python
Learn how to manipulate the data, data science, python machine learning, numpy python, numpy, python numpy,
Learn how to produce meaningful outcomes, r programming, data science, r python, python r, python and r programming, data science, python r
Learn Fundamentals of Python for effectively using Data Science
Learn Fundamentals of Python for effectively using Numpy Library
Numpy arrays with python
Numpy functions
Linear Algebra
Combining Dataframes, Data Munging and how to deal with Missing Data
Also, why you should learn Python and Pandas Library
Learn Data Science with Python
Examine and manage data structures
Create, subset, convert or change any element within a vector or data frame
Most importantly you will learn the Mathematics beyond the Neural Network
The most important aspect of Numpy arrays is that they are optimized for speed. We’re going to do a demo where I prove to you that using a Numpy.
You will learn how to use the Python in Linear Algebra, and Neural Network concept, and use powerful machine learning algorithms
Use the “tidyverse” package, which involves “dplyr”, and other necessary data analysis package
OAK offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies
Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you.
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets.
Data science is the key to getting ahead in a competitive global climate.
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available.
Data science requires lifelong learning, so you will never really finish learning.
It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available
Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree.
A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science.
The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers.
The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation.
R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R
Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts
What is Python? Python is a general-purpose, object-oriented, high-level programming language.
Python vs. R: what is the Difference? Python and R are two of today’s most popular programming tools.
What does it mean that Python is object-oriented? Python is a multi-paradigm language, which means that it supports many programming approaches.
What are the limitations of Python? Python is a widely used, general-purpose programming language, but it has some limitations.
How is Python used? Python is a general programming language used widely across many industries and platforms.
What jobs use Python? Python is a popular language that is used across many industries and in many programming disciplines.
How do I learn Python on my own? Python has a simple syntax that makes it an excellent programming language for a beginner to learn.
What is machine learning? Machine learning describes systems that make predictions using a model trained on real-world data.
Requirements
No prior python and r knowledge is required
Free software and tools used during the course
Basic computer knowledge
Desire to learn data science
Curiosity for r programming
Desire to learn Python
Desire to work on r and python
Desire to learn machine learning a-z, numpy python, data analysis, python pandas, pandas
Description
Overview

Section 1: Data Science: Python Setup

Lecture 1 Installing Anaconda Distribution For MAC: R programming, Numpy, Full stack

Lecture 2 Installing Anaconda Distribution For Windows: Numpy Python
Lecture 3 Installing Python and PyCharm For MAC: Full stack data science

Lecture 4 Installing Python and PyCharm For Windows: R programming, Numpy python
Lecture 5 Installing Jupyter Notebook For MAC

Lecture 6 Installing Jupyter Notebook For Windows: Full Stack Data Science, R programming
Lecture 7 Project Files and Course Documents: R programming, Data science, Numpy

Lecture 8 FAQ regarding Data Science: R and Python

Lecture 9 FAQ regarding Python and R

Section 3: Math is not so confusing with Python

Lecture 11 Numbers and Math Operators with example: Python data science

Section 4: Strings in Python Programming

Lecture 12 Strings and Operations

Lecture 13 Data type Conversion in Python

Lecture 14 Python: Exercise

Section 5: Conditionals in Python

Lecture 15 Conditionals in Python Programming

Lecture 16 Bool() Function in Python

Lecture 17 Comparison and logical Operators in Python

Lecture 18 If Statements in Python

Lecture 19 Exercise: Calculator in Python

Lecture 20 Exercise: User Login in Python

Section 6: Loops in Python

Lecture 21 Loops in Python

Lecture 22 While Loops in Python

Lecture 23 For Loops in Python

Lecture 24 Range Function in Python

Lecture 25 Control Statements in Python

Lecture 26 Exercise : Perfect Numbers in Python

Lecture 27 Exercise : User Login with Loops in Python

Section 7: Functions in Python Bootcamp

Lecture 28 Functions in Python Programming for Python Numpy

Lecture 29 Create A New Function and Function Calls for Python Numpy

Lecture 30 Return Statement in Python

Lecture 31 Lambda Functions in Python

Lecture 32 Exercise 9: Finding Prime Number in Python

Section 8: Modules in Python 3

Lecture 33 Logic of Using Modules in Python

Lecture 34 How It is Work in Python

Lecture 35 Create A New Module in Python

Lecture 36 Python Exercise: Number Game

Section 9: Lists in Python

Lecture 37 Lists and List Operations in Python

Lecture 38 List Methods in Python

Lecture 39 List Comprehensions in Python

Lecture 40 Exercise: Fibonacci Numbers in Python

Lecture 41 Exercise: Merging Name and Surname in Python

Section 10: Tuples in Python

Lecture 42 Tuples in Python

Section 11: Dictionaries in Python

Lecture 43 Dictionaries in Python

Lecture 44 Dictionary Comprehensions in Python

Lecture 45 Exercise : Letter Counter in Python

Lecture 46 Exercise : Word Counter in Python

Section 12: Exceptions in Numpy Python

Lecture 47 What is exception?

Lecture 48 Exception Handling in Python and R programming

Lecture 49 Python Exercise : if Number

Section 13: Files in Python

Lecture 50 Files in Python

Lecture 51 File Operations in Python

Lecture 52 Exercise : Team Building in Python

Lecture 53 Exercise : Overlap in Python

Section 14: Sets in Python

Lecture 54 Sets and Set Operations and Methods

Lecture 55 Set Comprehensions in Python

Section 15: Object Oriented Programming (OOP)

Lecture 56 Logic of OOP

Lecture 57 Constructer in Object Oriented Programming (OOP)

Lecture 58 Methods in Object Oriented Programming (OOP)

Lecture 59 Inheritance in Object Oriented Programming (OOP)

Lecture 60 Overriding and Overloading in Object Oriented Programming (OOP)

Section 16: Project Python

Lecture 61 Final Project: Remote Controller Application

Section 17: In Foreign Lands: Data Science

Lecture 62 What Is Data Science?

Lecture 63 Data literacy in Data Science

Section 18: Using Numpy for Data Manipulation

Lecture 64 What is Numpy?

Lecture 65 Why Numpy?

Lecture 66 Array and features in Numpy

Lecture 67 Array’s Operators in Numpy

Lecture 68 Numpy Functions in Numpy

Lecture 69 Indexing and Slicing in Numpy

Lecture 70 Numpy Exercises in Numpy

Lecture 71 Using Numpy in Linear Algebra in Numpy

Lecture 72 NumExpr Guide in Numpy

Lecture 73 Using Numpy with Creating Neural Network in Numpy

Section 19: Using Pandas for Data Manipulation

Lecture 74 What is Pandas?

Lecture 75 Series and Features

Section 20: Data Frame with Pandas

Lecture 79 Multi index in Pandas Python

Lecture 80 Groupby Operations in Pandas Python

Lecture 83 Dealing with Missing Data in Pandas Python

Lecture 86 Work with Dataset Files in Pandas Python

Section 21: Python For Data Science: Data Visualization

Lecture 87 What is Matplotlib

Lecture 88 Using Matplotlib

Lecture 89 Pyplot – Pylab – Matplotlib

Lecture 90 Figure, Subplot and Axes in Python Matplotlib

Lecture 91 Figure Customization in Python Matplotlib

Lecture 92 Plot Customization in Python Matplotlib

Section 22: Data Science: Hands on Projects

Lecture 93 Analyse Data With Different Data Sets: Titanic Project

Lecture 94 Titanic Project Answers in Numpy Python

Lecture 95 Project II: Bike Sharing in Numpy Python

Lecture 96 Bike Sharing Project Answers in Numpy Python

Lecture 97 Project III: Housing and Property Sales in Numpy Python

Lecture 98 Answer for Housing and Property Sales Project in Numpy Python

Section 23: Why You Should Learn R Programming Language

Lecture 101 Introduction to R

Section 24: Environment Installation for R

Lecture 102 R and R Studio Installation

Lecture 103 Installation and Hands-On Experience

Lecture 104 R Console Versus R Studio

Section 25: Basic Syntax in Python

Lecture 105 Basic Syntax and Hands On Experience

Section 26: Data Types in R

Lecture 107 Vectors Basics in R programming

Lecture 108 Lists in R programming

Lecture 109 Matrices in r programming

Lecture 110 Arrays in R programming

Lecture 111 Factors in R programming

Lecture 112 Introduction to Data Frames in R programming

Section 27: Operators and Functions in R Programming

Lecture 113 Operators in R

Lecture 115 Loops and Strings in R Programming

Lecture 116 Functions in R Programming

Section 28: R Packages in R Programming

Lecture 117 Managing R Packages

Section 29: Data Management in R

Lecture 118 Getting Data into R

Lecture 119 Data Manipulation in R

Section 30: Computation and Statistics in R

Lecture 121 Simple Math Functions in R programming

Lecture 122 Normal Probability Distribution in R

Lecture 123 Correlation in R programming

Lecture 124 Paired T-Test in R programming

Lecture 125 Linear Regression in R programming

Lecture 126 Multiple Regression in R programming

Lecture 127 Decision Trees in R programming

Lecture 128 Chi Square tests in R programming

Section 31: Experiential Learning in Python

Lecture 129 Learn with Real Examples – Experiential learning 1

Lecture 130 Learn with Real Examples – Experiential learning 2

Lecture 131 Learn with Real Examples – Experiential learning 3

Section 32: Examining and Managing Data Structures in R

Lecture 132 Atomic Vector Types in Python R Programming

Lecture 133 Converting Data Types of Atomic Vectors in Python R Programming

Lecture 134 Test Functions in Python R Programming

Lecture 135 Vector Recycling and Iterations in Python R Programming

Lecture 136 Naming Vectors in Python R Programming

Lecture 137 Subsetting Vectors in Python R Programming

Lecture 138 Subsections of an Array in Python R Programming

Section 33: Matrices in Python R Programming

Lecture 139 Naming Matrix Row and Columns in Python R Programming

Lecture 140 Calculating With Matrices in Python R Programming

Section 34: Data Frames in Python R Programming

Lecture 142 Manipulating Values in DF

Lecture 144 Tibbles in R

Section 35: Factors in Python R Programming

Lecture 145 Introduction to Factors

Lecture 146 Manipulating Categorical Data with Forcats

Section 36: Data Transformation in R

Lecture 147 Introduction to Data Transformation

Lecture 148 Select Columns with Select Function in r

Lecture 149 Filtering Rows with Filter Function in python and r

Lecture 150 Arranging Rows with Arrange Function in R programming

Lecture 152 Grouped Summaries with Summarize Function in R programming

Section 37: DATA SCIENCE BONUS

Lecture 153 Full Stack Data Science with Python, Numpy and R Programming Bonus