The meaning of data

Use of data, interpretation and presentation in operational and decision-making processes of companies and their use for operational, tactical and strategic decision-making; the concept of Big Data and the evolution of relevant technologies and solution methods.

Data types

Unstructured data, data quality, Governance and security, changes in the way large volumes of data are processed compared to classical Business Intelligence methods.

Principles of knowledge discovery

The position of the so-called Data Science in projects and in solving practical issues of Business.

Modern architectures for data storage solutions

Data warehouses, Data marts, data lakes, ODS platforms, Big data solutions and more.

Hadoop Ecosystem Technology and Architecture - MapReduce

Hadoop, HDFS, YARN, Spark, HBase, Hive, Pig, Sqoop, Flume and more.

NoSQL, graph databases and document type databases, CAP theorem vs. ACID consistency etc.

Analysis and visualization of large data volumes using JavaScript API D3 and R language

Web analytics and visualization

Importance, characteristics and metrics, tools and technologies used.

Methods of analysis and visualization of multidimensional geographical and spatial data

Social network data analysis and cognitive analysis, sentiment analysis

Methods of text or spoken word analysis, algorithms used and visualization

Use of graph analyzes and algorithms - technologies, tools and specific applications

Real time data processing and analysis

Data streams, CQRS methods and so-called complex event processing, used tools and solution architectures.

Examples of typical use-cases

Fraud analytics, Churn analysis, Data Monetization, Location analytics, Affinity analysis and more.

The most used tools and platforms for data analysis and visualization

Comparison, pros and cons, use depending on the required tasks, type and volume of data, integration of analytical tools into the overall architecture of the company.