Practical Data Analysis Program

Introduction to the course and the world of data analysis. Overview of various roles in the industry (both business and technical) and their relation to our day-to-day work as analysts.

During this module we'll go through the main steps in the data analysis process and demonstrate a use case that presents an end-to-end analysis of a real data set .

The Structured Query Language (SQL) is a language that primarily allows querying of data located in various databases. In this part, we'll focus on the basic data analysis syntax of SQL

In addition to the technical content, this module places a great emphasis on acquiring proper writing and working habits, effective use of syntax, and business thinking through intensive practice.

In this module, students will be exposed to approximately 8 data sets from various industries, as well as around 300 exercises.

In this module, we'll focus on actions related to updating data and creating new tables. The module's goal is to enable a broad understanding of table structures, relationships, and how data population is performed.

At the end of the module, a summary exercise will be given, allowing students to develop their own database, thus embedding the concepts learned throughout the module.

This module deals with understanding the macro level 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.

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.

This module focuses on developing the business thinking of the analyst through the implementation of complex SQL queries.

During the module, a customer life cycle will be analyzed using an economic dataset with millions of records, and we will cover, among other things, the key stages in that cycle: Acquisition, Registration, Activity, and Churn.

Excel, the popular spreadsheet software in the world, has truly revolutionized the way businesses work with and analyze their data. In just a few years, it has become a standard product in every office and computer.

The software provides users with user-friendly and efficient tools to organize, process and improve the presentation of their data, analyze and extract insights, and, of course, create new data.

This module provides participants with knowledge and experience in Excel skills, ensuring they feel comfortable working in this environment on a day-to-day basis.

During the module, participants will acquire the following skills:

• Familiarity with the software and the work interface
• Working with data, including entering new data, processing and organizing data, performing basic filtering and analysis
• Using basic statistical functions and formulas to perform different calculations on the data
• Using charting tools to create charts and graphs that display the data in a visual and aesthetic way
• Connecting data from different sheets using the VLOOKUP function
• Creating smart reports and slices on the data quickly using pivot tables

During the module, students will complete approximately 110 exercises on 15 different data sets.

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.

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. At the end, a summary exercise will be given.

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 focuses on important concepts in the world of statistics (Descriptive/Inferential Statistics). Through the use of Python, students will understand how statistical tools can be implemented to obtain estimates on data, make assumptions, perform various tests such as T-Tests, ANOVA, Chi-squared, Correlations, and draw data-based conclusions.

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

A/B testing is a statistical experiment that compares between two versions with the goal of identifying and implementing the change that will lead to the desired outcome, for example: click-through rates on an ad, website registration, purchasing a product, and so on. Based on the statistical understanding gained in the previous module, in this part we will understand when to use A/B testing, how to conduct it, and how to interpret the results.

During the course, you will be given real use cases and tasked with extracting business insights from them.

You will then present these insights in a summary session, where an experienced analyst will provide feedback to help you improve.

As you progress through the course, you will gain a better understanding of what to present and how. During this course we will also address numerous business questions commonly asked in job interviews and discuss their solutions.