Welcome to Analytics Center

Analytics Center is the go-to source for developing data and analytics capabilities. We provide developmental services to help businesses harness the power of data so they can make better, more informed decisions. With our expertise and knowledge, we can help your business grow and thrive in today’s data-driven world.

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We help you identify a winning data strategy, design the right data governance structure and capability building roadmaps.

Training

We help you unlock your data and analytics capabilities with best-in-class training sessions. We train and coach technical and non-technical professionals to improve data-driven business models and decision making.

Events

We organize leading events at the intersection of business and technology. Global thought leaders meet at smartcon events: Istanbul Tech Week, Data Science Days and ITW Meet-Ups.

We create strategies and build critical competencies in data and

We are a team of experts helping companies to build data and analytics strategies, data governance structures and develop right data and analytics skill sets within the organization. We promote the effective usage of analytical methodologies and technologies in reinventing business models, decision-making and effective management.

We analyze

We train

We strategize

We develop

Check out our
Training Programs

Analytics Center trains technical and non-technical professionals from different backgrounds to develop their data and analytics skills.

Training programs are enriched with real case studies and customized to meet company and individual needs, identified for different functions and levels.

All our trainers are subject matter experts with relevant academic and business experience, and all our training programs are enriched with a period of mentorship to support on-the-job development.

Mastering Analytics

Fundamentals of Business Analytics

Basic Statistics & Numerical Programming

User Data Driven Action Taking

Data Analytics with R

Data Analytics with Python

Applied Data Science Techniques with Python

Fundamentals of Python

Data Visualization & Storytelling

Predictive Analytics

Customer LifeTime Value Training

Fraud Analytics

Mastering Big Data

Data Analysis and Data Manipulation

Quantitative Approach in Business Applications

Big Data Essentials

Big Data for BI Professionals

Architecting Big Data Applications

Machine Learning on Big Data

Mastering Machine Learning

Introduction to Machine Learning

Introduction to Deep Learning

Applied Machine Learning Algorithms

Machine Learning on Big Data

Operationalizing Machine Learning (ML Ops)

Custom-made for D&A Roles

Custom-made for non D&A Roles

Elements

Advances in Modeling User Behavior

This module reviews the advances in modeling user behavior and gently introduces the trends in the field. We cover implicit feedback models, click models, and choice models for user behavior, combined with complex machine-learning architectures.

Biases and Fairness in Recommendation and Ranking

This is a series of seminars and discussions introducing actual metrics to measure the ecosystem’s health, as well as common solution approaches. Sampling biases in user interaction datasets, confounding factors influencing user behavior, and fairness metrics are discussed.

Causal Inference and Online Learning

Learning from user interaction data requires accounting for user biases and confounding factors that arise in user interactions. This module gently introduces related concepts from causal inference and online learning. Two solution approaches to address the challenges of machine learning from user interactions.

Offline Learning from Logged User Interaction Data

This module reviews how to interpret previously logged user interaction data and develop learning algorithms to run on such data. The theory and hands-on knowledge of implementing these algorithms are discussed. We cover counterfactual machine learning, batch learning from logged bandit feedback, off-policy evaluation, and learning from user behavior in the face of sampling biases and confounding factors.

Online Learning from User Interactions

The gold standard in learning from user interactions is through online, bandit, and reinforcement learning algorithms. We cover the basics of bandit (and contextual bandit) learning, common setups, and learning algorithms. We then move to the advances in online learning, covering useful bandit variants and complex setups, such as combinatorial, ranking, and deep bandits.

Simulation Frameworks

Simulation frameworks are useful when developing online algorithms and warm-starting and evaluating them using previously logged user interaction data. They are also helpful in understanding the long-term impact of the deployed algorithms on the platform. We cover a gallery of existing simulation frameworks and demonstrate how they can be tuned to mimic the marketplace environment.

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Transform your business with actionable
Analytics and Big Data Strategies

Analytics Center supports organizations in their data driven journeys by unlocking data and analytics related challenges, identifying winning data strategies, designing the right data governance structures and building data and analytics capabilities.

Data and Analytics Maturity Assessment

We review existing roles within the analytics and data science spectrum across the organization, determine necessary skills for data and analytics professionals, and conduct a maturity assessment to determine current internal capabilities.

Building a Data-Driven Culture

We establish the definition and all requirements to build a data driven culture including technology, processes, and people. We design cultural elements, specific behaviors and action plans to transform employees into “Data Citizens”.

Building a Data Academy

We design and establish a Data Academy, including key data and analytics skills for various roles, training requirements, assessment programs, career paths, certification methods, and governance model. We determine activities and communication plans to continuously drive awareness, disseminate knowledge and manage change in the organization.

Designing Data and Analytics Governance Structure

We assess the current data and analytics organization and governance model of the firm, share global best practices, and develop different alternatives specific to the firm’s operating model. We then design key roles, responsibilities, decision making processes, and principles for future governance changes.