Kurumsal Eğitimlerle Firmalara Değer Katıyoruz!

Our Introduction to Python course is designed to take complete beginners or experienced developers up to speed on Python’s capabilities, setting up

students for success in using Python for their specific field of expertise. Whether you have never programmed before, already know basic syntax, or

want to learn about the advanced features of Py thon, this course is for you! In this course we will teach you Python 3. Learn how to use Python fo

real-world tasks, such as working with PDF Files, sending emails, reading Excel files, scraping websites for information, working with image files

and much more! This course will teach you Python in a practical manner and provides a full coding screencast and a corresponding code notebook t

review the concepts and exercises conducted in class. 

This course will provide your analytics staff with a useful introduction to Python, teaching them how to clean, aggregate, de scribe, and visualize data. Upon completion, they will be able to analyze numerical, categorical, and time-series data in Python. This course is geared toward individuals who are new to Python but have basic analytical skills and some programming experi ence. Senior Data Scientists have real-world business experience and will show your team how to apply Python to daily tasks. Your team will then be able to hit the ground running, using their new skills to immediately impact their work. We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place. 

Our Introduction to Python course is designed to take complete beginners or experienced developers up to speed on Python’s capabilities, setting up students for success in using Python for their specific field of expertise.

Master the skills to use machine learning in your day-to-day work with this Python course. Create algorithms to predict classes, continuous values, and more. This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Machine Learning. With a focus on SciKit Learn, you’ll learn all aspects of Machine Learning ranging from a variety of regression types (Linear / Lasso /Ridge), Elastic Net, K Nearest Neighbors and Means Clustering, Hierarchal Clustering, DBSCAN, PCA, and Model Deployment. This course includes 6-months access to the full course content in on-demand format to support post-class reference and re view.

This course is intended for complete beginners to Python to provide the basics of programmatically interacting with data. The course begins with a basic introduction to programming expressions, variables, and data types. It then progresses into condi t ional and control statements followed by an introduction to methods and functions. You will learn the basics of data struc tures, classes, and various string and utility functions. Lastly, you will gain experience using the pandas library for data analysis and visualization as well as the fundamentals of cloud computing. Throughout the course, you will gain hands-on practice through lab exercises with additional resources to deepen your knowledge of programming after the class. 

This course introduces using Python for Time Series Analysis. We will cover how to explore, visualize, and model Time Series data using Python. Particular emphasis will be placed on practical applications. 

In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models. 

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with con siderations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of in struction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of pro jects: fraud detection, recommendation engines, or flight delays.

In this course, you will learn about the process of planning and designing both relational and nonrelational databases. You will learn the design considerations for hosting databases on Amazon Elastic Compute Cloud (Amazon EC2). You will learn about our relational database services including Amazon Relational Database Service (Amazon RDS), Amazon Aurora, and Amazon Redshift. You will also learn about our nonrelational database services including Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. By the end of this course, you will be familiar with the planning and design requirements of all 8 of these AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. 

This course provides foundational SQL skills for data and analytics professionals. Attendees will learn how to use SQL to create and modify tables, retrieve and analyze data, and combine tables using joins. This course is geared towards individuals who have little to no SQL experience and work regularly on analytics projects. Lessons incorporate both lectures and hands-on exercises with a focus on cultivating practical skills. We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place.

Understanding the fundamental concepts of AI has significant implications for your business. Learn to make informed decisions regarding AI implementation and explore opportunities for innovation. 

Understanding the fundamental concepts of AI has significant implications for your business. Learn to make informed decisions regarding AI implementation and explore opportunities for innovation.   

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of in struction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of pro jects: fraud detection, recommendation engines, or flight delays 

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of in struction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of pro jects: fraud detection, recommendation engines, or flight delays.

In this course, you will explore the fundamentals of Apache Spark and Delta Lake on Databricks. You will learn the architectural components of Spark, the DataFrame and Structured Streaming APIs, and how Delta Lake can improve your data pipelines. Lastly, you will execute streaming queries to process streaming data and understand the advantages of using Delta Lake. 

This three-day hands-on training course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications. During the course, participants will learn how to identify common sources of poor perfor mance in Spark applications, techniques for avoiding or solving them, and best practices for Spark application monitoring. Apache Spark Application Performance Tuning presents the architecture and concepts behind Apache Spark and underlying data platform, then builds on this foundational understanding by teaching students how to tune Spark application code. The course format emphasizes instructor-led demonstrations illustrate both performance issues and the techniques that address them, followed by hands-on exercises that give students an opportunity to practice what they’ve learned through an interac t ive notebook environment. The course applies to Spark 2.4, but also introduces the Spark 3.0 Adaptive Query Execution framework. 

During this two-day instructor-led training course, participants will learn development and operations for Cloudera Streaming Analytics, a framework for low-latency processing and analytics powered by Apache Flink and Cloudera’s innovative SQL Stream Builder. Through extensive hands-on exercises, students will gain experience deploying and managing a Flink cluster, developing and running Flink applications, and using SQL Stream Builder’s continuous SQL to perform analytics on streaming data.  

This four-day instructor-led course begins by introducing Apache Kafka, explaining its key concepts and architecture, and dis cussing several common use cases. Building on this foundation, you will learn how to plan a Kafka deployment, and then gain hands-on experience by installing and configuring your own cloud-based, multi-node cluster running Kafka on the Cloudera Data Platform (CDP). You will then use this cluster during more than 20 hands-on exercises that follow, covering a range of essential skills, starting with how to create Kafka topics, producers, and consumers, then continuing through progressively more challenging aspects of Kafka operations and development, such as those related to scalability, reliability, and performance problems. Throughout the course, you will learn and use Cloudera’s recommended tools for working with Kafka, including Cloudera Manager, Schema Registry, Streams Messaging Manager, and Cruise Control.

The Open Data Lakehouse is a modern data architecture that enables versatile analytics on streaming and stored data within cloud-native object stores. This architecture can span hybrid and multi-cloud environments. This course introduces Apache Ozone, a hybrid storage service addressing the limitations of HDFS. You’ll also explore Apache Iceberg, an open-table format optimized for petabyte-scale datasets. The course covers Iceberg’s benefits, architecture, read/ write operations, streaming, and advanced features like time travel, partition evolution, and Data-as-Code. Over 25 hands-on labs and a capstone project will equip you with the skills to build an efficient, performant Open Data Lakehouse within your own environment.

This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to devel op high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this course, partici pants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries. 

This course is designed as an entry point for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Spark. Topics include: An overview of the Hortonworks Data Platform (HDP), including HDFS and YARN; using Spark Core APIs for interactive data exploration; Spark SQL and DataFrame operations; Spark Streaming and DStream opera t ions; data visualization, building and deploying Spark applications; and an introduction to the Spark Machine Learning Li brary. 

This course is designed for ‘Data Stewards’ or ‘Data Flow Managers’ who are looking forward to automate the flow of data be tween systems. Topics Include Introduction to NiFi, Installing and Configuring NiFi, Detail explanation of NiFi User Interface, Explanation of its components and Elements associated with each. How to Build a dataflow, NiFi Expression Language, Under standing NiFi Clustering, Data Provenance, Security around NiFi, Monitoring Tools and HDF Best practices.

This course provides a foundation of the two largest areas in machine learning: supervised and unsupervised learn ing. Instructors will demonstrate how machine learning techniques are applied to business problems, as well as how to implement these techniques using popular Python libraries. Lessons incorporate both lectures and hands on exercises with a focus on cultivating practical skills. Senior Data Scientists have real-world business experience and will show your team how to apply machine learning concepts to daily tasks. Your team will then be able to hit the ground running, using their new skills to immediately impact their work. We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place.

This course provides a practical introduction to statistics and inference. Participants will learn the difference between the pop ulation parameters we want to know, and the statistics from samples that estimate them. In addition, participants will be able to use summary statistics to describe the central value and spread in a distribution. The course will demonstrate how to design an experiment, including an estimate of sample size, to determine if the means of two populations are different. Lessons incorporate both lecture and hands-on exercises with a focus on cultivating practical skills. We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place

Employee Data Literacy – the ability to read, work with, analyze, and argue with data – is key to building and maintaining a data-driven company culture. Data literate employees lead to data fluent teams that demonstrate an enthusiastic appreciation for data and use it as a common language to collaborate, drive results, and make better business decisions. Achieve this goal for your workforce by offering the course in Data Literacy, which is designed for non-technical (e.g. marketing, HR, operations) and technical team members who are not data professionals (e.g. engineers and developers). 

Data Visualization is an essential part of data-related work. The increasing availability of informative datasets and software tools has led to increased reliance on data visualizations in many fields. Data visualization provides a powerful way to communicate data-driven findings, motivate analytics, and detect flaws.

Istanbul Data Science Academy’s “No Code AI Bootcamp” program is designed for professionals who would like to have the ability to read, work with, analyze, and argue with data. We believe that having data literate employees is key to building and maintaining a data-driven company culture. Data literate employees lead to data fluent teams that demonstrate an enthusiastic appreciation for data and use it as a common language to collaborate, drive results, and make better business decisions. Achieve this goal for your workforce by offering the course in No Code AI Bootcamp, which is designed for non-technical (e.g. marketing, HR, operations) and technical team members who are not data professionals (e.g. engi neers and developers). We offer no code ai tools which are designed to abstract away the programming typically required to cre ate AI systems, this tools enable non-experts to visualize, analyze data and develop machine learning models. 

Data analytics plays a crucial role in decision-making for business leaders in today’s world. The rapidly in creasing volume of data and the need for businesses to understand, interpret, and use this data for mak ing strategic decisions are driving a grow ing demand for data analysts every day. The Data Analytics Ca reer Program offered in partnership with Google Cloud equips participants with the necessary skills to specialize in this field. 

İstanbul Data Science Academy’s 14-week Data Science Bootcamp is a full-time, immersive program designed to provide stu dents of diverse backgrounds with a uniquely rigorous learning environment that helps them begin a new data science career. We offer the only accredited data science bootcamp, which is taught by Senior Data Scientists with deep industry experience. Our program combines traditional instruction in theory and technique with a project-based approach, through which students apply their new knowledge to build a five-project portfolio using real data that they can present to potential employers. Each project is a start-to-finish application of the skills needed to be a well-rounded, competitive practitioner in the data sci ence workforce. Projects are carefully designed to highlight the skills needed in every facet of data science: project design, data acquisition and storage, tool selection, analysis, interpretation, and communication. In succession, the projects deepen in both difficulty and independence, leading up to a Passion Project that students present to İstanbul Data Science Academy hiring partners during our Career Day event at the end of the program. This project acts as the final piece in their newly robust online portfolio. Upon successful completion of the program, graduates are awarded a Data Science Certificate. They will have completed rigor ous training in machine learning, programming in multiple languages (Python, Bash, SQL, PySpark), data wrangling, project de sign, and communication of results for integration in a business environment.

Over the last decade, most companies have completed a digital transformation. This has produced unimaginable volumes of new types of data and much more complicated data at a higher frequency. While it was previously apparent that Data Scientists were needed to make sense of it all, it was less apparent that someone needs to organize and ensure this data’s quality, security, and availability for the Data Scientists to do their jobs. Companies need teams of people whose sole fo cus is to process data in a way that allows them to extract value from it. This is why will we continue to see the role of Data Engineers grow in importance and breadth. Data engineers create data pipelines that connect data from one system to another. They are also responsible for trans forming data from one format to another so that a data scientist can pull data from different systems for analysis. Even though data engineers aren’t as visible as data scientists, they’re just as important, when it comes to data analysis. Data scientists interact with data by writing queries. They are responsible for creating dashboards for insights and devel oping machine-learning strategies. They also work directly with decision-makers to understand their information needs and develop strategies for meeting these needs. Data engineers build and maintain the data infrastructures that connect an organization’s data ecosystems. These infrastructures make the data scientist’s work possible. İstanbul Data Science Academy’s 14-weeks Data Engineer Bootcamp is an immersive program designed to provide stu dents of diverse backgrounds with a uniquely rigorous learning environment. We offer the data engineering bootcamp, which is taught by Senior Data Architects with deep industry experience. Our program combines traditional instruction in theory and technique with a project-based approach. Projects are care fully designed to highlight the skills needed in every facet of data engineering. Upon successful completion of the program, graduates are awarded a Data Engineering & Big Data Bootcamp Certificate

İstanbul Data Science Academy’s 10-weeks Cloud Engineer Bootcamp is expected that lab studies will be completed in Google Cloud environments with the participation of the instructor one day a week. A project presentation will be made at the Career Day & Graduation event to be held at the end of the program. Undergraduate students who successfully complete their lab studies, courses and project presentation will be entitled to participate in the “Associate Cloud Engineer” certification exam with a 50% discount. (Anyone can apply to the program, only the exam discount is for undergraduate students.) During the program, meeting and experience sharing sessions will be held with Deloitte and Google Cloud professionals, and job opportunities at Deloitte will be shared with those who successfully complete the program. In order to successfully complete the program, it is necessary to attend the course, do the lab work and complete the graduation project

İstanbul Data Science Academy’s 8-week ML Ops Bootcamp introduces the router to MLOps tools and best practices for attempting to deploy in ML operations/systems and for evaluating and monitoring those systems. MLOps is a discipline that focuses on Machine learning management, test running, devices, and automation. Machine learning engineers – experts, scientists and data engineers use important tools for continuous storage and evaluation of distributed models. Models that provide the best-performing model finding, speed, and rigor are targeted towards their intended goals. 

This course provides a technical overview of Apache Hadoop. It includes high-level information about concepts, architecture, operation, and uses of the Hortonworks Data Platform (HDP) and the Hadoop ecosystem. The course provides an optional primer for those who plan to attend a hands-on, instructor-led course.

This 5 day training course is designed primarily for systems administrators and platform architects who need to understand HDP cluster capabilities, and manage HDP clusters. Topics include: Understanding HDF capabilities, Apache Hadoop, Apache YARN, HDFS, and other Hadoop ecosystem components. Stu dents will understand how to administer, manage, and monitor HDP clusters

This course is designed for experienced administrators who manage Hortonworks Data Platform (HDP) 2.3 clusters with Am bari. It covers upgrades, configuration, application management, and other common tasks.

This 4-day training course is designed for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Apache Pig and Apache Hive, and developing applications on Apache Spark. Topics include: Essential under standing of HDP & its capabilities, Hadoop, YARN, HDFS, MapReduce/Tez, data ingestion, using Pig and Hive to perform data analytics on Big Data and an introduction to Spark Core, Spark SQL, Apache Zeppelin, and additional Spark features.

This 4 day training course is designed for developers who need to create real-time applications to ingest and process stream ing data sources using Hortonworks Data Platform (HDP) and Hortonworks Data Flow (HDF) environments. Specific technolo gies covered includes: Apache Hadoop, Apache Kafka, Apache Storm, Apache Spark and Apache HBase as well as Apache NiFi. The highlight of the course is the custom workshop-styled labs that will allow participants to build streaming applications with Storm and Spark Streaming.

This 5-day training course is designed for developers who need to create applications to analyze Big Data stored in Apache Ha doop using Apache Pig and Apache Hive, and developing applications on Apache Spark. Topics include: Essential understanding of Big Data & its capabilities, Hadoop, YARN, HDFS, MapReduce/Tez, data ingestion, using Pig and Hive to perform data ana lytics on Big Data and an introduction to Spark Core, Spark SQL, Apache Zeppelin, and additional Spark features.