practical BI Developer
Training Program

In the first module of the course, we'll provide a comprehensive understanding of the key stages data goes through, from collection to reporting. In this module we'll coven the concpets of Data Collection, Data Storage, Data Cleaning and Preparation, Data Modeling, Data Analysis, Data Reporting and Visualization

In this module, we will focus on the basic commands of SQL language that enable data analysis. Beyond the technical content that will be learned, this module places great emphasis on acquiring correct writing and working habits, effective use of syntax, and business thinking through intensive practice.

In this module, the students will be exposed to  8 datasets from various industries, and about 300 exercises.

A unique module, developed by Ram Kedem, which focuses on complex queries and "real-world" problems. Its contents are based on the book "Advanced SQL: Practical Techniques and Use Cases" and rely on a decade of experience working with various databases, challenging job interview questions, and complex SQL problems.

During the lessons, students learn dozens of practical applications and practice advanced techniques based on real user problems.

In this module, students will be exposed to approximately 15 different data sets and over 200 exercises.

• In this module, we will focus on operations related to data updating and the creation of new tables. The goal of the module is to enable a comprehensive understanding of table structures, relationships, and how data population is carried out.
• Also, this module deals with understanding a macro of concepts related to the world of Data: what is a Data Warehouse (DWH) and how it differs from an Operational System (OLTP), what are indexes and execution plans, what are Views, and more.

• This module covers exact numeric data types, approximate numeric data types, and date types, as well as working with character data, collations, and computed columns. Students will also learn about the ROWVERSION data type and alias data type
• Also, this module focuses on working with views, system tables, schemas, and temporary tables

• This module covers the theory and practical implementation of indexes
• This module also focuses on understanding and analyzing execution plans to optimize query performance. It covers the concepts of estimated and actual execution plans, different join methods, and data modification operations.
• This learning unit covers the fundamentals of table partitioning in SQL, including how to create, manage, and optimize partitions to improve query performance and data management efficiency 

Tableau is a powerful tool for data analysis and visualization that transforms complex data into clear and actionable insights. It enables users to connect to a variety of data sources, process them, and create interactive and stunning reports and dashboards.

This course is designed to provide participants with in-depth knowledge and practical tools for working with Tableau, starting from data preparation to advanced visualizations. Throughout the course, participants will learn to:

• Connect to various data sources and manage connections, including Excel, databases, and cloud services.
• Clean and process data, including handling missing values, removing duplicates, and transforming data formats.
• Utilize data relationships and choose between live connections and extracts.
• Build hierarchies and analytical models for highly detailed insights.
• Design diverse visualizations, including maps, charts, graphs, and tables.
• Leverage advanced features like LOD Expressions, custom formulas, and quick table calculations.
• Create interactive dashboards that support navigation, filtering, and dynamic data exploration.
• Prepare data for presentation and publish results using Tableau Public.
• Use parameters to create dynamic calculations and customize reports.

The course also includes advanced techniques, such as using dual axes, creating box plots, and understanding the differences between central tendency measures (average vs. median).

In the age of data-driven decision-making, the ability to clearly and effectively visualize data is more important than ever. This module offers participants a deep dive into the art and science of data visualization. Through interactive demonstrations, and real-world case studies, participants will learn how to transform raw data into compelling visual stories that engage and inform.

• During this unit we will explore the use of programming objects in the SQL Server environment, such as functions, procedures, and triggers, which enable the automation of tasks and the programming of functionalities within the database.

• SQL Server Integration Services (SSIS) is a tool focused on transferring data from various environments to the data warehouse. SSIS offers its users efficient data movement capabilities, excellent performance, and an accessible, convenient, and simple workflow.
• This learning unit provides the necessary knowledge to implement various ETL processes using SSIS, including familiarization with the interface, types of ETL, different data movement techniques, debugging and troubleshooting, managing Incremental ETL, and more.

The workshop is delivered by experienced interviewers in the field of data analysis, and is designed to prepare participants for the job market. The following topics are emphasized:

• How to prepare effectively for a job interview in the field of data analysis
• The importance of a well-crafted resume and how to create an effective LinkedIn profile
• Familiarity with the stages of a job interview (preparation, conduct, closing, and follow-up) and principles for proper conduct at each stage. At the end of the workshop, individual meetings will be held with each participant, providing specific feedback.

This module introduces the new way BI Developers and data professionals work in an AI-assisted environment.

The goal is not to turn participants into Python developers, but to teach them how to work effectively with tools like Claude Code: how to guide AI, understand the code it generates, validate results, identify mistakes, and use AI to accelerate BI development, SQL workflows, documentation, automation, and data analysis – without losing professional control.

Participants will learn the Python and Pandas fundamentals required to read, supervise, and improve AI-generated analysis, while building practical workflows that connect business questions, data sources, SQL, code, validation, and insights.

• How AI tools fit into modern BI and data workflows.
• Working with Claude Code for data analysis, SQL, documentation, and development tasks.
• Python and Pandas fundamentals for BI Developers: reading files, DataFrames, filtering, sorting, calculations, groupby, joins, missing values, and basic transformations.
• Reading and reviewing AI-generated Python/Pandas code.
• Validating results with sanity checks, row counts, sample comparisons, and business logic.
• Identifying common errors such as wrong joins, dropped rows, incorrect aggregations, duplicated records, and misleading outputs.
• Using AI to write, review, and explain SQL queries.
• Using AI to document BI logic, data flows, metrics, and transformation rules.
• Working with Git and GitHub to manage versions and review AI-generated changes.
• Building context files and lightweight semantic layers that help AI understand tables, metrics, naming conventions, and business logic.
• Introduction to Skills, MCP, and data agents.
• Building an agentic BI workflow: business question → data context → SQL/code generation → execution → validation → insight.