Practical Data Analysis Program

The first meeting of the course serves as an introduction to the course and to the world of Data Analysis. During it we will:

• Cover various roles in the industry (business and technical) and understand their connection to our ongoing work as analysts.
• Understand the main stages in the Data Analysis process, and present a Use-Case that demonstrates an analysis of a real data-set from end to end.

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.

• 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.
• At the end of the module, a summarizing exercise will be given, which will allow students to develop an independent database, and thus embed the concepts that were learned during the module.

As life becomes more data-driven, data visualization skills are no longer just a bonus, they're a necessity. However, most people who work with data haven't learned or aren't familiar with the rules of presenting quantitative information to others. Efficient data presentation is done according to the principles of data visualization, a scientific field based on research in psychology, linguistics, and visual communication. Without this skill, recommendations are rejected, ideas are dismissed, stories don't reach the audience, and projects remain on the shelves.

Mastering data visualization skills can save a lot of confusion and frustration, up to 50% (in time and money), and provide a tremendous advantage in the business world!

• Principles of data visualization
• Rules for designing tables and graphs
• Exercises for identifying and presenting quantitative scripts
• Non-standard tables and graphs
• Conveying the message in a quantitative manner

This module is delivered by Bella Graff, a Data Visualization expert

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.

The Power BI platform is considered Microsoft's flagship in the business intelligence field. This module focuses on this tool and primarily on the way in which data exploration is implemented using it. In this part, we will discuss, among other things, how to determine the data cleanliness level and how to present relationships between different types of variables.

In addition, this module focuses on basic concepts and recommended methods for effective data communication. The module provides tools that will enable telling the data story and not just displaying it. Additionally, during the module, students will understand visualization principles and learn how (and how not) to create graphics that convey the business message in a clear and efficient manner.

As a technical foundation for these topics, we will learn, among other things:

• Importing data from multiple and diverse sources, working with them, processing and organizing them
• Various filtering and analysis
• Creating, editing, and deleting links between tables
• Producing reports and dashboards, and publishing them on the Power BI cloud platform to share with the rest of the organization
• Creating interactive visualizations that allow active exploration of information according to different partitions.

During the module, students will perform about 110 exercises on 15 different datasets.

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 is entirely dedicated to analyzing business use-cases and extracting insights.
• Throughout this unit, we will explore various scenarios, understanding the complexity and specifics of each case.
• This learning unit is designed to enable participants to understand how to interpret and analyze different cases, applying data analysis techniques to uncover significant patterns and trends.
• During this module we'll also delve into advanced techniques of data analysis using Microsoft Excel. We'll cover a wide range of tools and functions such as advanced use of PivotTables, conditional statements, reference and lookup functions, statistical functions, and dynamic array formulas.

Python – one of the most sought-after and popular programming languages in the market, is a powerful open-source language with many uses, and is considered easy and friendly to learn. Today, Python is used by many organizations that require fast analysis and calculations on enormous amounts of data, such as artificial intelligence, machine learning, bioinformatics, robotics, cybersecurity, and more.

As an introduction to the next module – Exploratory Data Analysis with Python, in this part we will learn the basics of the language required for data analysts. This module is mainly designed to allow analysts to develop "programming thinking" and deal with the common programming elements in the field of data analysis.

During this module, students will be exposed to over 500 exercises at various difficulty levels, classified by topics. 

Exploratory Data Analysis, or EDA for short, is an approach to data analysis that allows for systematic investigation, summary of key characteristics, making assumptions, and finding relationships between different data points. The process involves statistical analysis (Univariate / Multivariate) of various variables and graphical representation of the results.

EDA is a way of thinking and inference, an approach to organized work, and goal-oriented as a way to solve analytical problems. The main goal of this module is to develop analytical thinking and the ability to independently access any database and analyze it effectively.

This section begins with an overview of methods for data analysis, similar to what the SQL language enables, and also demonstrate important concepts from the world of descriptive & inferential Statistics.

During this module, students will be exposed to over 10 different datasets and more than 100 exercises.