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

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

Python or R programming! Learn data science with R & Python with all in one course. You'll learn NumPy, Pandas and more

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Welcome to **Full Stack Data Science R and Python with Hands-on Projects** course.

Ready for the **Data Science** career?

Are you curious about Data Science and looking to start your self-learning journey into the world of data ?

Are you an experienced developer looking for a landing in Data Science!

In both cases, you are at the right place!

The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open-source.**R for statistical analysis** and **Python as a general-purpose programming language**. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.

With my full-stack Data Science course, you will be able to learn R and Python together.

If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming.

But do not worry! In this course, you will have a chance to learn both and will decide to which one fits your niche!

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**.

Throughout the course's 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 **Python for Data Science** course.

We will open the door of the **Data Science** world and will move deeper. You will learn the fundamentals of **Python** and its beautiful libraries such as **Numpy, Pandas, and Matplotlib** step by step. Then, we will **transform and manipulate real data**. For the manipulation, we will use the **tidyverse** package, which involves **dplyr** and other necessary packages.

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

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 **4 different final projects** covering all of 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 data science trends.

*Video and Audio Production Quality*

All our content is 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

- R and Python in the same course. You decide which one you would go for!
- R was built as a statistical language, it suits much better to do statistical learning and R is a statistical programming software favoured by many academia
- If you have some programming experience, Python might be the language for you
- Since R was built as a statistical language, it suits much better to do statistical learning. It represents the way statisticians think pretty well, so anyone with a formal statistics background can use R easily. Python, on the other hand, is a better choice for machine learning with its flexibility for production use, especially when the data analysis tasks need to be integrated with web applications. If you enroll this course you will have a chance to learn both
- You will learn R and Python from scratch
- Learn Fundamentals of Python for effectively using Data Science
- 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
- Numpy arrays
- Series and Features
- 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
- Select columns and filter rows
- Arrange the order and create new variables
- Create, subset, convert or change any element within a vector or data frame
- Transform and manipulate an existing and real data

- 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 3 Installing Anaconda for Windows
- Lecture 4 Installing Anaconda for Mac
- Lecture 5 Let's Meet Jupyter Notebook for Windows
- Lecture 6 Basics of Jupyter Notebook for Mac

- Lecture 7 Data Types in Python
- Lecture 8 Operators in Python
- Lecture 9 Conditionals
- Lecture 10 Loops
- Lecture 11 Lists, Tuples, Dictionaries and Sets
- Lecture 12 Data Type Operators and Methods
- Lecture 13 Modules in Python
- Lecture 14 Functions in Python
- Lecture 15 Exercise Analyse
- Lecture 16 Exercise Solution

- Lecture 17 What Is Data Science?
- Lecture 18 Data Literacy
- Lecture 19 1

- Lecture 20 What is Numpy?
- Lecture 21 Array and Features
- Lecture 22 Array Operators
- Lecture 23 Indexing and Slicing
- Lecture 24 Numpy Exercises

- Lecture 25 What is Pandas?
- Lecture 26 Series and Features

- Lecture 27 Data Frame Attributes and Methods
- Lecture 28 Data Frame Attributes and Methods Part – II
- Lecture 29 Data Frame Attributes and Methods Part – III
- Lecture 30 Multi Index
- Lecture 31 Groupby Operations
- Lecture 32 Missing Data and Data Munging
- Lecture 33 Missing Data and Data Munging Part II
- Lecture 34 How We Deal with Missing Data?
- Lecture 35 Combining Data Frames
- Lecture 36 Combining Data Frames Part – II
- Lecture 37 Work with Dataset Files
- Lecture 38 2
- Lecture 39 3

- Lecture 40 What is Matplotlib?
- Lecture 41 Using Matplotlib
- Lecture 42 Pyplot – Pylab - Matplotlib
- Lecture 43 Figure, Subplot and Axes
- Lecture 44 Figure Customization
- Lecture 45 Plot Customization

- Lecture 46 Analyse Data With Different Data Sets: Titanic Project
- Lecture 47 Titanic Project Answers
- Lecture 48 Project II: Bike Sharing
- Lecture 49 Bike Sharing Project Answers
- Lecture 50 Project III: Housing and Property Sales
- Lecture 51 Answer for Housing and Property Sales Project
- Lecture 52 Project IV: English Premier League
- Lecture 53 Answers for English Premier League Project

- Lecture 54 Downloading and Installing R & R Studio
- Lecture 55 R Console Versus R Studio

- Lecture 56 Getting Data into R
- Lecture 57 Data Manipulation
- Lecture 58 Graphs and Charts

- Lecture 59 Vector Basics
- Lecture 60 Atomic Vector Types
- Lecture 61 Converting Data Types of Atomic Vectors
- Lecture 62 Test Functions
- Lecture 63 Vector Recycling and Iterations
- Lecture 64 Naming Vectors
- Lecture 65 Subsetting Vectors

- Lecture 67 Arrays
- Lecture 68 Subsections of an Array

- Lecture 69 Matrices
- Lecture 70 Naming Matrix Row and Columns
- Lecture 71 Calculating With Matrices

- Lecture 72 Introduction to Data Frames
- Lecture 73 Naming Variables and Observations in DF
- Lecture 74 Manipulating Values in DF
- Lecture 75 Adding and Removing Variables
- Lecture 76 Tibbles in R

- Lecture 77 Introduction to Factors
- Lecture 78 Manipulating Categorical Data with Forcats

- Lecture 79 Introduction to Data Transformation
- Lecture 80 Select Columns with Select Function
- Lecture 81 Filtering Rows with Filter Function
- Lecture 82 Arranging Rows with Arrange Function
- Lecture 83 Adding New Variables with Mutate Function
- Lecture 84 Grouped Summaries with Summarize Function

Good point of this course is step by step using Juypter to let you follow to learn. The weak point is sometime too fast and the ipynb or csv files not provided. You need to have keyboard typing and waste a lot of time.

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