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Information Technologies Institute Trainings

Big Data Trainings

Big Data Learning Training

Duration of Education	• 2 days
Prerequisites	• To graduate from fields such as Engineering, Mathematics, Statistics, Informatics. • To have basic Python knowledge. • To have basic Linux knowledge.
Who Can Participate?	• Designing highly scalable distributed systems dealing with big data expertise and using different open source tools, • Understanding how algorithms work and creating high-performance algorithms, • Working on processes such as collecting, parsing, managing, analyzing and visualizing complex big data projects, • It is suitable for people who want to decide on the necessary hardware and software design needs and design processes according to these decisions.
Education Goals	• Learning basic information about the history of big data, Hadoop foundations and basic technologies that make up the ecosystem, • Learning the basics of project life cycle, data collection, data evaluation, data conversion and data analysis, • To learn the basic information about the features and usage of the distributed file system (HDFS) that makes up the core Hadoop and YARN, which provides resource management, • Planning big data set setup, learning information about big data set setup, configuration and management with Ambari, • To learn general information about usage scenarios and basic components for Kafka and Nifi, which are the basis of data transfer technologies, • Learning basic information about Flume and Sqoop, which are used to transfer data to Hadoop environment, • Learning basic information about Hive, which enables to run query scripts on files in the distributed file system, • Learning basic information about Spark and SQL, DataFrame, Machine Learning and GraphX libraries used to perform in-memory analysis and analytical studies on big data, • Learning basic information about Pig Latin script language for data analysis, • Learning basic information about Zookeeper, which is a service manager in the big data ecosystem, and Oozie, which is a business planner, • To learn basic information about NoSQL databases and their usage.
Subject Headings	• Big Data History and Fundamentals • Data Science Fundamentals • Kernel Hadoop: HDFS and YARN • Big Data Cluster Management with Ambari • Data Integration: Kafka and Nifi • Data Integration: Flume and Sqoop • Data Analysis: Hive • Data Processing: Spark (Streaming, SQL, DataFrame, ML, GraphX) • Data Analysis: Pig • Zookeeper and Oozie • Data Storage: Hbase

Machine Learning Training

Duration of Education	• 2 days
Prerequisites	• To have knowledge of data, data analysis, mathematics, statistics, computer science, database, database query. • To have basic Python knowledge. • To have basic Linux knowledge.
Who Can Participate?	• Spark / Hadoop’ta veri bilimi ve makine öğrenimi uygulaması gereken, yazılım geliştiriciler, analistler ve veri bilimciler, • Collecting, analyzing and interpreting enormous amounts of data, • Using advanced analysis technologies, • Those who want to work on large amounts of data, collecting and analyzing data, detecting patterns, trends and relationships in data sets and various analysis and It is suitable for people who want to use reporting tools.
Education Goals	• Learning the basics of project life cycle, data collection, data evaluation, data conversion and data analysis, • Theoretical learning of machine learning (guided/unguided algorithms) algorithms, • Learning the basics of working on flowing data and using machine learning algorithms with Spark, which is used to perform in-memory analysis and analytical studies on big data, • Making sample application studies.
Subject Headings	• Data Science Fundamentals • Machine Learning Methods • Spark ML • Spark ML Lab Study • Application Study

Big Data Artificial Intelligence Training

Duration of Education	• 4 days
Prerequisites	• To graduate from fields such as Engineering, Mathematics, Statistics, Informatics. • To have basic Python knowledge. • To have basic Linux knowledge. • To have knowledge of data, data analysis, mathematics, statistics, computer science, database, database query.
Who Can Participate?	• Designing highly scalable distributed systems dealing with big data expertise and using different open source tools, • Understanding how algorithms work and creating high-performance algorithms, • Working on processes such as collecting, parsing, managing, analyzing and visualizing complex big data projects, • It is suitable for people who want to decide on the necessary hardware and software design needs and design processes according to these decisions. • Spark / Hadoop’ta veri bilimi ve makine öğrenimi uygulaması gereken, yazılım geliştiriciler, analistler ve veri bilimciler, • Collecting, analyzing and interpreting huge amounts of data, using advanced analysis technologies, • Those who want to work on large amounts of data, collecting and analyzing data, detecting patterns, trends and relationships in data sets and various analysis and using reporting tools
Education Goals	• Learning basic information about scenarios that require the emergence of the big data concept, Hadoop fundamentals and basic technologies that make up the ecosystem, • To learn the basic information about the features and usage of the distributed file system (HDFS) that makes up the core Hadoop and YARN, which provides resource management, • Planning big data set setup, learning information about big data set setup, configuration and management with Ambari, • To learn general information about usage scenarios and basic components for Kafka and Nifi, which are the basis of data transfer technologies, • Learning basic information about Flume and Sqoop used for data transfer to Hadoop environment • Learning basic information about Hive, which enables to run query scripts on files in the distributed file system, • Learning basic information about Spark, Streaming, SQL, DataFrame and GraphX libraries, which are used to perform in-memory analysis and analytical studies on big data, • Learning the basics of Pig Latin script language for data analysis • To learn basic information about Zookeeper, which is a service manager in the Big Data Ecosystem, and Oozie, which is a business planner, • Learning basic information about NoSQL databases and their usage, • Learning the basics of project life cycle, data collection, data evaluation, data conversion and data analysis, • Learning basic information about Artificial Intelligence and Machine Learning, • Learning the basics of using machine learning algorithms with Spark, which is used to perform in-memory analysis and analytical studies on big data, • Making sample application studies • Examining case studies on Advanced Analytics Applications in Big Data • Examining how big data technologies and artificial intelligence can be used in real world problems
Subject Headings	• Big Data History and Fundamentals • Kernel Hadoop: HDFS and YARN • Big Data Cluster Management with Ambari • Data Integration: Kafka and Nifi • Data Integration: Flume and Sqoop • Data Analysis: Hive • Data Processing: Spark (Streaming, SQL, DataFrame, GraphX) • Data Analysis: Pig • Zookeeper and Oozie • Data Storage: HBase • Data Science Fundamentals • Artificial Intelligence and Machine Learning Fundamentals • Data Processing: Spark ML • Spark ML Lab Study • Advanced Analytics Applications in Big Data • Big data technologies and artificial How can intelligence be used? • Application Study

Spark Training

Duration of Education	• 3 days
Prerequisites	• To have knowledge of data, data analysis, mathematics, statistics, computer science, database, database query. • To have basic Linux knowledge.
Who Can Participate?	• Software developers, analysts and data scientists who need to apply data science and machine learning with Spark, • Collecting, analyzing and interpreting enormous amounts of data, • Using advanced analysis technologies, • Those who want to work on large amounts of data, collecting and analyzing data, detecting patterns, trends and relationships in data sets and various analysis and Suitable for people who want to use reporting tools
Education Goals	• Learning the basics of Python Programming, • Learning basic information about big data foundations and basic technologies that make up the ecosystem, • To learn the basic information about the features and usage of the distributed file system (HDFS) that makes up the core Hadoop and YARN, which provides resource management, • Learning basic information about Spark and SQL, DataFrame, Machine Learning and GraphX libraries used to perform in-memory analysis and analytical studies on big data, • Learning the basics of using machine learning algorithms with Spark, which is used to perform in-memory analysis and analytical studies on big data,
Subject Headings	• Introduction to Python • Big Data Fundamentals • Kernel Hadoop: HDFS and YARN • Spark Architecture • Spark Low Level API (RDD) • Spark High Level API (DataFrame, Dataset, SQL) DataFrame and Dataset Persistence • Spark Streaming • Spark Structured Streaming • Spark Distributed Processing • Writing, Configuring and Running Spark Applications • Performance Tuning • Spark ML • Deep Learning with Spark

Safir Cloud Trainings

OpenStack User Training

Duration of Education	• 1 day
Prerequisites	• To graduate from fields such as Engineering, Mathematics, Statistics, Informatics. • To have basic Linux knowledge. • Have basic Networking knowledge.
Who Can Participate?	• Employees as System Specialists • Information processing officers • Those who manage systems in distributed and clustering architectures • Cloud infrastructure users
Education Goals	• Learning basic information about the history of virtualization and virtualization technologies • Learning the advantages and disadvantages of virtualization • Learning basic information about Cloud Computing • Learning the advantages and disadvantages of Cloud Computing • Learning basic information about Cloud Computing service models • Detailed explanation of Cloud Types • Explaining basic features about OpenStack • Learning the basics of OpenStack Architecture • Learning basic information about OpenStack services • Virtual machine, virtual network and disk on Safir Cloud Demonstration of creation steps in detail • Organizing a workshop on Safir Cloud
Subject Headings	• Virtualization History and Fundamentals • Cloud Computing History and Fundamentals • Cloud Computing Service Models • Types of Cloud Computing • OpenStack History and Fundamentals • OpenStack Architecture • Sapphire Cloud Bootcamp

OpenStack Admin Training

Duration of Education	• 3 days
Prerequisites	• To graduate from fields such as Engineering, Mathematics, Statistics, Informatics. • To have basic Linux knowledge. • To have basic knowledge of Linux Bash Script. • Have basic Networking knowledge. • Have basic Python knowledge
Who Can Participate?	• Employees as System Specialists • Information processing officers • Those who manage systems in distributed and clustering architectures • Cloud infrastructure administrators • Data Center Managers
Education Goals	• Learning basic information about the history of virtualization and virtualization technologies • Learning the advantages and disadvantages of virtualization • Learning basic information about Cloud Computing • Learning the advantages and disadvantages of Cloud Computing • Learning basic information about Cloud Computing service models • Detailed explanation of Cloud Types • Explaining basic features about OpenStack • Learning the basics of OpenStack Architecture • Learning basic information about OpenStack services • Detailed explanation of OpenStack Identity Management (Keystone) service • Detailed explanation of OpenStack Image Management (Glance) service • Detailed explanation of OpenStack Compute (Nova) service • Detailed explanation of OpenStack Block Storage (Cinder) service • Detailed explanation of OpenStack Network Management (Neutron) service • Detailed explanation of Ansible, the IT automation language • Learning the basics of OpenStack installation • Learning the basics of OpenStack Ansible • Virtual machine, virtual network and disk on Safir Cloud Demonstration of creation steps in detail • Organizing a workshop on Safir Cloud
Subject Headings	• Virtualization History and Fundamentals • Cloud Computing History and Fundamentals • Cloud Computing Service Models • Types of Cloud Computing • OpenStack History and Fundamentals • OpenStack Architecture • Sapphire Cloud Bootcamp

Ansible Training

Duration of Education	• 1 day
Prerequisites	• To graduate from fields such as Engineering, Mathematics, Statistics, Informatics. • To have basic Python knowledge. • To have basic Linux knowledge. • Having basic knowledge of Linux Bash Script • Have basic Networking knowledge.
Who Can Participate?	• Employees as System Specialists • Information processing officers • Those who manage systems in distributed and clustering architectures • Cloud infrastructure administrators • Data Center Managers
Education Goals	• Explaining Ansible's features and architecture • Installing Ansible • Learning Ansible core components • Writing scripts to be executed in Ansible and writing a playbook • Installing a service running in clustering architecture with Ansible
Subject Headings	• Introduction to Ansible language • Ansible Installation • Inventory • Ad-Hoc tasks • Modules • Plugins • Ansible Playbooks • Ansible Roles

Intern

TÜBİTAK BİLGEM builds its basic strategy for the future on qualified knowledge and qualified people focused on national targets in the research, technology development and innovation ecosystem.

Starting from the understanding that "the most important resource of a country is generally people, specifically scientists," TÜBİTAK encourages and supports our youth from an early age. In this context, providing young minds with early exposure to technology production is crucial for the success of our National Technology Move. Accordingly, TÜBİTAK BİLGEM offers internship opportunities to undergraduate students from universities every year.

You can follow internship announcements and submit your applications through the Career Gateway at https://kariyerkapisi.cbiko.gov.tr.

You can access frequently asked questions about internships at TÜBİTAK BİLGEM from here.

Application Conditions

Students enrolled in undergraduate (2nd year and above) and associate degree programs in departments offering education in universities and conducting insurance procedures through the higher education institution to which they are affiliated can benefit from the internship opportunity.
For undergraduate and associate degree students, a minimum Weighted Grade Point Average (GPA) of 2.50 out of 4 is required. The GPA of candidates with a 100-point system is converted to a 4-point system based on the "Conversion Table of Grades from the 4-Point System to the 100-Point System" published by the Higher Education Council.
There is no requirement for a foreign language certificate during the internship application process.
Students enrolled in departments such as Forensic Computing Engineering, Computer Sciences, Computer Science and Engineering, Computer Engineering, Computer and Informatics, Computer and Software Engineering, Information Systems Engineering, Electrical and Electronics Engineering, Control Engineering, Control and Computer Engineering, Control and Automation Engineering, Mechanical Engineering, Mechatronics Engineering, Telecommunication Engineering, or Software Engineering in universities can apply for internships.

Internship applications are accepted between December and January, and the internship period covers June, July, and August.

Scholar

Scholar assignments are made for research and development activities for undergraduate, master's, doctoral students, and post-doctoral researchers. In our center, scholars are appointed for practical purposes in externally funded, TARAL, or European Union projects.

You can contact us via the email address bilgem.yetenekkazanimi@tubitak.gov.tr to apply to be a scholar.

Application Conditions

(1) The conditions for undergraduate scholars in externally funded projects conducted by the institution are specified below:

Being a student continuing undergraduate education at higher education institutions established in Turkey (excluding foreign language preparatory students).
Having a weighted cumulative GPA for previous years, excluding preparatory years, based on the university's grading system, which satisfies the formula score and foreign language requirements in the recruitment criteria.
Completing at least the first semester of the first year of undergraduate education.
Having a GPA of "+3.00" and a University Placement Exam Ranking of "10,000 ≥" for undergraduate general average.
For foreign students placed in Turkish universities without taking the ÖSYM exam or for those who completed undergraduate education through exams such as Vertical Transfer Exam, the lowest university placement ranking of the department from the year the candidate started the undergraduate program is considered in the ranking formula.

(2) The conditions for master's degree scholars in externally funded projects conducted by the institution are specified below:

Being a student continuing master's degree education at higher education institutions established in Turkey (excluding special students and foreign language preparatory students).
Currently pursuing a master's degree in the project's field of responsibility.

(3) The conditions for doctoral students in externally funded projects conducted by the institution are specified below:

Being a student continuing doctoral education at higher education institutions established in Turkey (excluding special students and foreign language preparatory students).
Currently pursuing a doctorate in the project's field of responsibility or conducting a doctorate in areas determined within the framework of the YÖK-TÜBİTAK Doctoral Program Project Collaboration Protocol. (Students in medical specialization and artistic proficiency are accepted as doctoral students.)