Data Analyst

Basic Syllabus

The Google and Reichman Data Analyst program trains our students in providing quantitative support, market understanding and strategic perspective to stakeholders throughout a tech organization. You will become an analytics expert for your partners, using numbers, code and cloud tools to help them make better decisions. You will tell stories with skin-in-the-game insights from big data.
You'll make critical recommendations for your potential partners in Engineering, Product, Marketing, Finance and Sales in a variety of domains (as presented by guest lecturers), and we will set you up for success with self-learning capabilities so you can evolve your career as time passes.

Introduction to statistics

Fundamental statistics computations
(mean, std, ste, correlation coefficients)
Types of samples and their usage in everyday work
Statistical tests (t, F, Chi-Sq), power analysis, experimental analysis
Linear regression
Pivot tables, contingency tables
Types of biases and why correlation is not causation

Academic Section

(Computer science Fundamentals)

Introduction to SQL on top of BigQuery

Messy data and big data – how to clean your data and prepare for analysis, getting to know your data one variable at a time, the journey
of a statistic
The basics of cloud technologies and the tech stack of Google Cloud Platform
SQL – the basics: select, order, group by, sum, count and group by
Types of variables and how to handle them with SQL
SQL – several tables: join, over
SQL – analytical functions and more advanced code.
How to work with scripts, manage your code and make it readable
and scaleable
exploratory data analysis: preparing your data, types of outlier clearing strategies, basic visualizations, troubleshooting your code, how to write a data flow chart, how to check the validity of your results

Introduction to SQL on top of BigQuery

Messy data and big data – how to clean your data and prepare for analysis, getting to know your data one variable at a time, the journey
of a statistic
The basics of cloud technologies and the tech stack of Google Cloud Platform
SQL – the basics: select, order, group by, sum, count and group by
Types of variables and how to handle them with SQL
SQL – several tables: join, over
SQL – analytical functions and more advanced code.
How to work with scripts, manage your code and make it readable
and scaleable
exploratory data analysis: preparing your data, types of outlier clearing strategies, basic visualizations, troubleshooting your code, how to write a data flow chart, how to check the validity of your results

Introduction to SQL on top of BigQuery

Messy data and big data – how to clean your data and prepare for analysis, getting to know your data one variable at a time, the journey
of a statistic
The basics of cloud technologies and the tech stack of Google Cloud Platform
SQL – the basics: select, order, group by, sum, count and group by
Types of variables and how to handle them with SQL
SQL – several tables: join, over
SQL – analytical functions and more advanced code.
How to work with scripts, manage your code and make it readable
and scaleable
exploratory data analysis: preparing your data, types of outlier clearing strategies, basic visualizations, troubleshooting your code, how to write a data flow chart, how to check the validity of your results

Professional Section

(Computer science Fundamentals)

Data Visualization on top of Data Studio

What is a useful dashboard
Data as a product
Statistical tests (t, F, Chi-Sq), power analysis, experimental analysis
Telling a story with Data

Which charts fit different types of analyses and use cases – for tracking historic performance and for leading indicators and predictions

Determine the metrics to track over time and the slices of the data that are most valuable

How to pre-process your data for a dashboard, while keeping some level of agility during the work with the dashboard

Working in a tech company

Which types of stakeholders do data analysts work with and what do they look for in an analytical partner (how marketing, product, engineering, growth, finance, sales and executive teams look differently at data)
How do tech companies plan and execute their strategies, and what role do data analysts have in this process (from planning to execution)
Key metrics that are commonly used in B2C and B2B companies
Introduction to growth metrics, charts and product world views