#### 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 python and r 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
- Curiosity for r programming
- Desire to learn Python
- Desire to work on r and python

- 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.
- 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

- Anyone interested in data sciences
- Anyone who plans a career in data scientist,
- Software developer whom want to learn python,
- Anyone eager to learn python and r 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
- Anyone eager to learn Python with no coding background
- Anyone who wants to learn Pandas
- Anyone who wants to learn Numpy
- Anyone who wants to work on real r and python projects
- Anyone who wants to learn data visualization projects.

- Lecture 1 Installing Anaconda Distribution For MAC
- Lecture 2 Installing Anaconda Distribution For Windows
- Lecture 3 Installing Python and PyCharm For MAC
- Lecture 4 Installing Python and PyCharm For Windows
- Lecture 5 Installing Jupyter Notebook For MAC
- Lecture 6 Installing Jupyter Notebook For Windows
- Lecture 7 Project Files and Course Documents
- Lecture 8 FAQ regarding Data Science
- Lecture 9 FAQ regarding Python and R

- Lecture 12 Strings and Operations
- Lecture 13 Data type Conversion
- Lecture 14 Python: Exercise

- Lecture 15 Conditionals
- Lecture 16 Bool() Function
- Lecture 17 Comparison and logical Operators
- Lecture 18 If Statements
- Lecture 19 Exercise: Calculator
- Lecture 20 Exercise: User Login

- Lecture 21 Loops
- Lecture 22 While Loops
- Lecture 23 For Loops
- Lecture 24 Range Function in Python
- Lecture 25 Control Statements
- Lecture 26 Exercise : Perfect Numbers
- Lecture 27 Exercise : User Login with Loops

- Lecture 28 Functions in Python Programming
- Lecture 29 Create A New Function and Function Calls
- Lecture 30 Return Statement
- Lecture 31 Lambda Functions
- Lecture 32 Exercise 9: Finding Prime Number

- Lecture 33 Logic of Using Modules
- Lecture 34 How It is Work
- Lecture 35 Create A New Module
- Lecture 36 Python Exercise: Number Game

- Lecture 37 Lists and List Operations
- Lecture 38 List Methods
- Lecture 39 List Comprehensions
- Lecture 40 Exercise: Fibonacci Numbers
- Lecture 41 Exercise: Merging Name and Surname

- Lecture 42 Tuples

- Lecture 43 Dictionaries
- Lecture 44 Dictionary Comprehensions
- Lecture 45 Exercise : Letter Counter
- Lecture 46 Exercise : Word Counter

- Lecture 47 What is exception?
- Lecture 48 Exception Handling
- Lecture 49 Python Exercise : if Number

- Lecture 50 Files
- Lecture 51 File Operations
- Lecture 52 Exercise : Team Building
- Lecture 53 Exercise : Overlap

- Lecture 54 Sets and Set Operations and Methods
- Lecture 55 Set Comprehensions

- Lecture 56 Logic of OOP
- Lecture 57 Constructer
- Lecture 58 Methods
- Lecture 59 Inheritance
- Lecture 60 Overriding and Overloading
- Lecture 61 Quiz

- Lecture 63 What Is Data Science?
- Lecture 64 Data literacy
- Lecture 65 Python Data Science Quiz

- Lecture 66 What is Numpy?
- Lecture 67 Why Numpy?
- Lecture 68 Array and features
- Lecture 69 Array’s Operators
- Lecture 70 Numpy Functions
- Lecture 71 Indexing and Slicing
- Lecture 72 Numpy Exercises
- Lecture 73 Using Numpy in Linear Algebra
- Lecture 74 NumExpr Guide
- Lecture 75 Using Numpy with Creating Neural Network
- Lecture 76 Numpy Quiz

- Lecture 77 What is Pandas?
- Lecture 78 Series and Features

- Lecture 79 Data Frame attributes and Methods Part – I
- Lecture 80 Data Frame attributes and Methods Part – II
- Lecture 81 Data Frame attributes and Methods Part – III
- Lecture 82 Multi index
- Lecture 83 Groupby Operations
- Lecture 84 Missing Data and Data Munging Part I
- Lecture 85 Missing Data and Data Munging Part II
- Lecture 86 Dealing with Missing Data
- Lecture 87 Combining Data Frames Part – I
- Lecture 88 Combining Data Frames Part – II
- Lecture 89 Work with Dataset Files
- Lecture 90 Data Science ( Python and R ) Quiz
- Lecture 91 Data Science ( Python and R ) Quiz 2

- Lecture 92 What is Matplotlib
- Lecture 93 Using Matplotlib
- Lecture 94 Pyplot – Pylab - Matplotlib
- Lecture 95 Figure, Subplot and Axes
- Lecture 96 Figure Customization
- Lecture 97 Plot Customization

- Lecture 98 Analyse Data With Different Data Sets: Titanic Project
- Lecture 99 Titanic Project Answers
- Lecture 100 Project II: Bike Sharing
- Lecture 101 Bike Sharing Project Answers
- Lecture 102 Project III: Housing and Property Sales
- Lecture 103 Answer for Housing and Property Sales Project
- Lecture 104 Project IV : English Premier League
- Lecture 105 Answers for English Premier League Project

- Lecture 107 R and R Studio Installation
- Lecture 108 Installation and Hands-On Experience
- Lecture 109 R Console Versus R Studio

- Lecture 110 Basic Syntax and Hands On Experience

- Lecture 111 Variables
- Lecture 112 Vectors Basics
- Lecture 113 Lists
- Lecture 114 Matrices in r programming
- Lecture 115 Arrays
- Lecture 116 Factors
- Lecture 117 Introduction to Data Frames

- Lecture 118 Operators in R
- Lecture 119 Flowcharts
- Lecture 120 Loops and Strings
- Lecture 121 Functions

- Lecture 122 Managing R Packages

- Lecture 123 Getting Data into R
- Lecture 124 Data Manipulation
- Lecture 125 Graphs and Charts

- Lecture 126 Simple Math Functions
- Lecture 127 Normal Probability Distribution
- Lecture 128 Correlation
- Lecture 129 Paired T-Test
- Lecture 130 Linear Regression
- Lecture 131 Multiple Regression
- Lecture 132 Decision Trees
- Lecture 133 Chi Square tests

- Lecture 134 Learn with Real Examples - Experiential learning 1
- Lecture 135 Learn with Real Examples - Experiential learning 2
- Lecture 136 Learn with Real Examples - Experiential learning 3

- Lecture 137 Atomic Vector Types
- Lecture 138 Converting Data Types of Atomic Vectors
- Lecture 139 Test Functions
- Lecture 140 Vector Recycling and Iterations
- Lecture 141 Naming Vectors
- Lecture 142 Subsetting Vectors
- Lecture 143 Subsections of an Array

- Lecture 144 Naming Matrix Row and Columns
- Lecture 145 Calculating With Matrices

- Lecture 146 Naming Variables and Observations in DF
- Lecture 147 Manipulating Values in DF
- Lecture 148 Adding and Removing Variables
- Lecture 149 Tibbles in R

- Lecture 150 Introduction to Factors
- Lecture 151 Manipulating Categorical Data with Forcats

- Lecture 152 Introduction to Data Transformation
- Lecture 153 Select Columns with Select Function
- Lecture 154 Filtering Rows with Filter Function
- Lecture 155 Arranging Rows with Arrange Function
- Lecture 156 Adding New Variables with Mutate Function
- Lecture 157 Grouped Summaries with Summarize Function

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