#### Scratch Kodlama: Yeni Başlayanlar İçin Uygulamalı Scratch

( Turkish )Scratch ile programlamayı sıfırdan öğrenin. Hem keyifli oyunlar geliştirin hem Scratch ile programla öğrenmeye başlayın

Learn data science with R programming and Python. Use NumPy, Pandas to manipulate the data and produce outcomes

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Welcome to **Full Stack Data Science with Python, Numpy, and R Programming** course.

Do you want to learn Python from scratch?

Do you think the transition from other popular programming languages like Java or C++ to Python for data science?

Do you want to be able to make data analysis without any programming or data science experience?

It may be hard to know whether to use Python or R for data analysis, both are great options. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer.

Why not see for yourself what you prefer?

In this course, we offer R Programming, Python, and Numpy! So you will decide which one you will learn.

Throughout the course's first part, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily **handling big data**, **manipulate** it, and **produce** **meaningful** **outcomes**.

In the second part, we will teach you how to **use the Python to analyze data, create beautiful visualization**s, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this course.

In this course, you will also learn Numpy which is one of the most useful **scientific** libraries in Python programming.

Throughout the course, we will teach you how to **use the Python in Linear Algebra, and Neural Network concept**, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this **Full Stack Data Science with Python, Numpy and R Programming** course.

At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.

**In this course you will learn;**

How to use Anaconda and Jupyter notebook,

Fundamentals of Python such as

Datatypes in Python,

Lots of datatype operators, methods and how to use them,

Conditional concept, if statements

The logic of Loops and control statements

Functions and how to use them

How to use modules and create your own modules

Data science and Data literacy concepts

Fundamentals of Numpy for Data manipulation such as

Numpy arrays and their features

Numpy functions

Numexpr module

How to do indexing and slicing on Arrays

Linear Algebra

Using NumPy in Neural Network

How to do indexing and slicing on Arrays

Lots of stuff about Pandas for data manipulation such as

Pandas series and their features

Dataframes and their features

Hierarchical indexing concept and theory

Groupby operations

The logic of Data Munging

How to deal effectively with missing data effectively

Combining the Data Frames

How to work with Dataset files

And also you will learn fundamentals thing about Matplotlib library such as

**Pyplot, Pylab and Matplotlb concepts**What Figure, Subplot and Axes are

How to do figure and plot customization

Examining and Managing Data Structures in R

Atomic vectors

Lists

Arrays

Matrices

Data frames

Tibbles

Factors

Data Transformation in R

Transform and manipulate a deal data

Tidyverse and more

And we will do many exercises. Finally, we will also have **hands-on projects** covering all of the Python subjects.

*Why would you want to take this course?*

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy's seasoned instructors' expertise.

*Fresh Content*

It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest trends.

*Video and Audio Production Quality*

All our content are created/produced as **high-quality video/audio** to provide you the best learning experience.

You will be,

Seeing clearly

Hearing clearly

Moving through the course without distractions

*You'll also get:*

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for Download

**Dive in now!**

We offer **full support**, answering any questions.

See you in the course!

- No prior knowledge is required
- Free software and tools used during the course
- Basic computer knowledge
- Desire to learn data science
- Nothing else! It’s just you, your computer and your ambition to get started today

- Learn R programming without any programming or data science experience
- If you are with a computer science or software development background you might feel more comfortable using Python for data science
- In this course you will learn R programming, Python and Numpy from the beginning
- Learn Fundamentals of Python for effectively using Data Science
- Fundamentals of Numpy Library and a little bit more
- Data Manipulation
- Learn how to handle with big data
- Learn how to manipulate the data
- Learn how to produce meaningful outcomes
- Learn Fundamentals of Python for effectively using Data Science
- Learn Fundamentals of Python for effectively using Numpy Library
- Numpy arrays
- Numpy functions
- Linear Algebra
- Combining Dataframes, Data Munging and how to deal with Missing Data
- How to use Matplotlib library and start to journey in Data Visualization
- Also, why you should learn Python and Pandas Library
- Learn Data Science with Python
- Examine and manage data structures
- Handle wide variety of data science challenges
- 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 vectorized operation is faster than using a Python list.
- 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

- Anyone interested in data sciences
- Anyone who plans a career in data scientist,
- Software developer whom want to learn data science,
- Anyone eager to learn Data Science with no coding background
- Statisticians, academic researchers, economists, analysts and business people
- Professionals working in analytics or related fields
- Anyone who is particularly interested in big data, machine learning and data intelligence

- Lecture 1 Project Files and Course Documents
- Lecture 2 Installing Anaconda Distribution For MAC
- Lecture 3 Installing Anaconda Distribution For Windows
- Lecture 4 Installing Python and PyCharm For MAC
- Lecture 5 Installing Python and PyCharm For Windows
- Lecture 6 Installing Jupyter Notebook For MAC
- Lecture 7 Installing Jupyter Notebook For Windows

- Lecture 10 Strings and Operations
- Lecture 11 Data type Conversion
- Lecture 12 Exercise

- Lecture 13 Conditionals
- Lecture 14 Bool() Function
- Lecture 15 Comparison and logical Operators
- Lecture 16 If Statements
- Lecture 17 Exercise: Calculator
- Lecture 18 Exercise: User Login

- Lecture 19 Loops
- Lecture 20 While Loops
- Lecture 21 For Loops
- Lecture 22 Range Function
- Lecture 23 Control Statements
- Lecture 24 Exercise : Perfect Numbers
- Lecture 25 Exercise : User Login with Loops

- Lecture 26 Functions
- Lecture 27 Create A New Function and Function Calls
- Lecture 28 Return Statement
- Lecture 29 Lambda Functions
- Lecture 30 Exercise 9: Finding Prime Number

- Lecture 31 Logic of Using Modules
- Lecture 32 How It is Work
- Lecture 33 Create A New Module
- Lecture 34 Exercise: Number Game

- Lecture 35 Lists and List Operations
- Lecture 36 List Methods
- Lecture 37 List Comprehensions
- Lecture 38 Exercise: Fibonacci Numbers
- Lecture 39 Exercise: Merging Name and Surname

- Lecture 40 Tuples

- Lecture 41 Dictionaries
- Lecture 42 Dictionary Comprehensions
- Lecture 43 Exercise : Letter Counter
- Lecture 44 Exercise : Word Counter

- Lecture 45 What is exception?
- Lecture 46 Exception Handling
- Lecture 47 Exercise : if Number

- Lecture 48 Files
- Lecture 49 File Operations
- Lecture 50 Exercise : Team Building
- Lecture 51 Exercise : Overlap

- Lecture 52 Sets and Set Operations and Methods
- Lecture 53 Set Comprehensions

- Lecture 54 Logic of OOP
- Lecture 55 Constructer
- Lecture 56 Methods
- Lecture 57 Inheritance
- Lecture 58 Overriding and Overloading
- Lecture 59 Quiz

- Lecture 61 What Is Data Science?
- Lecture 62 Data literacy
- Lecture 63 Quiz

- Lecture 64 What is Numpy?
- Lecture 65 Why Numpy?
- Lecture 66 Array and features
- Lecture 67 Array’s Operators
- Lecture 68 Numpy Functions
- Lecture 69 Indexing and Slicing
- Lecture 70 Numpy Exercises
- Lecture 71 Using Numpy in Linear Algebra
- Lecture 72 NumExpr Guide
- Lecture 73 Using Numpy with Creating Neural Network
- Lecture 74 Quiz

- Lecture 75 What is Pandas?
- Lecture 76 Series and Features

- Lecture 77 Data Frame attributes and Methods Part – I
- Lecture 78 Data Frame attributes and Methods Part – II
- Lecture 79 Data Frame attributes and Methods Part – III
- Lecture 80 Multi index
- Lecture 81 Groupby Operations
- Lecture 82 Missing Data and Data Munging Part I
- Lecture 83 Missing Data and Data Munging Part II
- Lecture 84 Dealing with Missing Data
- Lecture 85 Combining Data Frames Part – I
- Lecture 86 Combining Data Frames Part – II
- Lecture 87 Work with Dataset Files
- Lecture 88 Quiz
- Lecture 89 Quiz

- Lecture 90 What is Matplotlib
- Lecture 91 Using Matplotlib
- Lecture 92 Pyplot – Pylab - Matplotlib
- Lecture 93 Figure, Subplot and Axes
- Lecture 94 Figure Customization
- Lecture 95 Plot Customization

- Lecture 96 Analyse Data With Different Data Sets: Titanic Project
- Lecture 97 Titanic Project Answers
- Lecture 98 Project II: Bike Sharing
- Lecture 99 Bike Sharing Project Answers
- Lecture 100 Project III: Housing and Property Sales
- Lecture 101 Answer for Housing and Property Sales Project
- Lecture 102 Project IV : English Premier League
- Lecture 103 Answers for English Premier League Project

- Lecture 105 R and R Studio Installation
- Lecture 106 Installation and Hands-On Experience
- Lecture 107 R Console Versus R Studio

- Lecture 108 Basic Syntax and Hands On Experience

- Lecture 109 Variables
- Lecture 110 Vectors Basics
- Lecture 111 Lists
- Lecture 112 Matrices
- Lecture 113 Arrays
- Lecture 114 Factors
- Lecture 115 Introduction to Data Frames

- Lecture 116 Operators in R
- Lecture 117 Flowcharts
- Lecture 118 Loops and Strings
- Lecture 119 Functions

- Lecture 120 Managing R Packages

- Lecture 121 Getting Data into R
- Lecture 122 Data Manipulation
- Lecture 123 Graphs and Charts

- Lecture 124 Simple Math Functions
- Lecture 125 Normal Probability Distribution
- Lecture 126 Correlation
- Lecture 127 Paired T-Test
- Lecture 128 Linear Regression
- Lecture 129 Multiple Regression
- Lecture 130 Decision Trees
- Lecture 131 Chi Square tests

- Lecture 132 Learn with Real Examples - Experiential learning 1
- Lecture 133 Learn with Real Examples - Experiential learning 2
- Lecture 134 Learn with Real Examples - Experiential learning 3

- Lecture 135 Atomic Vector Types
- Lecture 136 Converting Data Types of Atomic Vectors
- Lecture 137 Test Functions
- Lecture 138 Vector Recycling and Iterations
- Lecture 139 Naming Vectors
- Lecture 140 Subsetting Vectors
- Lecture 141 Subsections of an Array

- Lecture 142 Naming Matrix Row and Columns
- Lecture 143 Calculating With Matrices

- Lecture 144 Naming Variables and Observations in DF
- Lecture 145 Manipulating Values in DF
- Lecture 146 Adding and Removing Variables
- Lecture 147 Tibbles in R

- Lecture 148 Introduction to Factors
- Lecture 149 Manipulating Categorical Data with Forcats

- Lecture 150 Introduction to Data Transformation
- Lecture 151 Select Columns with Select Function
- Lecture 152 Filtering Rows with Filter Function
- Lecture 153 Arranging Rows with Arrange Function
- Lecture 154 Adding New Variables with Mutate Function
- Lecture 155 Grouped Summaries with Summarize Function

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