Description
R is one of the most popular programming languages used in statistics and data science, with a rich library for simple and advanced data analysis tasks.
The most important feature that makes R different from other software programs is that it offers powerful analysis techniques and visualization opportunities in the fields of software, database, statistics, data mining and social network analysis at the same time.
With this course, you will learn the R language by practicing with real data and gain a comprehensive knowledge of data science. At the end of the course, you will be able to work both in R and in data pre-analysis, processing and visualization.
Those who want to work in fields such as artificial intelligence, machine learning and deep learning should also have basic skills in statistics and data analysis.
About Education
Education Objectives
- Understand the R programming language,
- Will have knowledge about the basic operation and functions of R,
- R’s rich library allows you to specialize further (according to your industry),
- Learn data visualization techniques with R,
- Understand the R programming language,
- Will be able to use R language actively,
- Developing software competencies,
- Will be able to implement projects in the field of Data Science,
- Will be able to analyze data,
- Will have basic knowledge of statistics,
- Will be able to acquire Data Literacy skills.
Education Content
- INTRODUCTION TO R
Introduction to R
R assistance system
File system access with R
R variables, memory, workspace
Missing observations
- DATA LITERACY
What is Data?
What is a Data Type?
What are the Data Collection Methods?
Data Mining
What is a Database?
Database Management System
Most Used Database Systems
Data Centers
What is Data Science?
History of Data Science
Data Science and its Relationship with Other Sciences
KVKK
What are Data Science Tools?
In Which Areas Data Science is Utilized?
What are the Career Fields in Data Science?
- BASIC STATISTICS
Statistical Thinking
Sample
Observation Unit
Parameter
Variables
Scale Types
Arithmetic Mean
Median
Mod
Cartilage
Change Range
Standard Deviation
Variance
Skewness
kurtosis
Confidence Intervals
Hypothesis Testing
Correlation
Regression
Reading Graphic Data
- LINEAR ALGEBRA
What is Linear Algebra?
Where is Linear Algebra Used?
Linear Equation Properties
Linear Equation Systems
Matrix Types
Matrix Related Terms
Matrix Operations
Co-factor of the matrix
Reduction in Matrices
Determinant
Inverse of Matrix
Vectors Introduction
Location Vector
Addition and Subtraction in Vectors
Scalar Product
Vector Product
Linear Dependent
Eigen Vector
- NATIVE DATA TYPES
Vector system
Numerical vectors
Character vectors
Logical vectors and operators
Category (factor)
Data frame – csv files
List – json files
- VARIABLE SUBSETTING
Subsetting with Boolean variables
Subclustering with numerical indices
Subclustering by observation names
Delete observation
Assign to a subset of variables
- FLOW CONTROL
Conditional flow (if)
Matrix loops (with index and apply*)
- FUNCTIONS
Modular programming
Conion writing rules in R
R’s “formula” system
Homework with practical exercises
- DATA IMPORT AND EXPORT METHODS
csv
json
xml
pulling data from http APIs
Homework with practical exercises
- DATA VISUALIZATION WITH R
Principles of graph creation for data analysis
Base R graphics system
Who Should Receive the Training?
- Those who have a basic knowledge of data analysis concepts and want to take their skills to the next level,
- Those who want to learn advanced and effective analysis methodologies in R in a detailed and hands-on way,
- Those who want to develop their career in data science,
- Those who want to deepen and differentiate their data analysis techniques in their research and projects,
- Those who want to learn the R programming language,
- Students interested in data analysis,
- Those pursuing a career in data science,
- Academics who want to conduct quantitative studies,
- Statisticians
- Analysts dealing with Big Data,
- Private company managers who want to increase the profitability of their companies,
- Software developers who want to learn the R programming language,
- Anyone interested in R,
- Those who want to continue to develop in areas such as artificial intelligence, machine learning, deep learning,
- Those who want to have basic knowledge of statistics,
- Those who want to gain Data Literacy competency,
- Anyone pursuing a career in Data Science.
Requirements
- Basic level computer usage,
- Knowledge of Data Science terminology.
Plan this training institutionally!
This training can be planned in different durations and content specific to your organization. Please contact us for detailed rich content and planning to realize your training objectives.
