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Syllabus Data Analyst

In the Google and Reichman Data Analyst program, we'll embark on an exciting journey to equip you with the skills and knowledge needed to excel in the world of data analysis. Throughout this program, you'll discover how to provide vital quantitative support, leverage market insights, and offer strategic guidance as part of a technology organizations. As you dive into this program, you'll transform into a data analytics expert, harnessing the power of data, coding, and cloud tools to drive informed decision-making. During the course, we'll explore the fascinating world of big data, learn to craft compelling narratives, and make crucial recommendations across domains such as Engineering, Product Development, Marketing, Finance, and Sales, guided by insights from guest lecturers. Additionally, we'll ensure you're equipped with the skills for self-directed learning, setting you on a path to continuous career growth and success. 

Introduction to statistics

Introduction to statistics
Introduction to probability
Central and dispersion measures
Introduction to google sheets
Percentiles and Z-scroes
Common distributions
Sampling distribution and hypothesis testing
One sample test
Confidence intervals and two-samples tests
Binom & Chi square
Correlations
Linear regression
Difference in differences, proportions
A/B test

Introduction to exploratory data analysis using SQL and Big Query

Cloud platform & Introduction to Big Data
SQL introduction, GCP Console, Select, From, Limit, Aliases
Distinct, Where, Logical operators, Data Types
Functions
Group by, Having, Join
Union, Table wildcard, Exporting BQ results & Plotting in Google Sheets
EDA Example (explore a dataset using SQL + build ERD)
Subqueries, CASE
Window Functions
Arrays, Structs, Query Performance
Data Cleaning
Common analysis pitfalls & Case Studies Practice
Data Visualization + EDA Fundamentals
EDA Project A-Z
Project Introduction

Working in a hi-tech company

Stakeholder Insights: Understand stakeholder preferences in data analysis across various business functions ( marketing, product, engineering, growth, finance, sales and executive teams).
Data Analysts in Strategy: Explore data analysts’ role in tech company strategy, from planning to execution.
Common Business Metrics: Discover key metrics used in both B2C and B2B companies.
Growth Metrics and Visualizations: Introduce growth metrics, charts, and product perspectives in data analysis.

Product Analytics, how to MVP with data, Data Visualization, Building Effective Dashboards with Data Studio

Data Sources and Connectors
Data Visualization Components
Filters and Date Ranges
Calculated Fields
Style and Themes
Google Analytics Integration
Custom Metrics and Dimensions
Google BigQuery Connector
Community Visualizations
Scorecards and KPIs
Advanced Date Functions
Drill-down Functionality
Annotations and Markers
Alerts and Notifications
Embedding and Sharing
User Segmentation
Google Ads Connector
Custom Reports and Dashboards
Google Cloud Functions
Cross-database Blending

Enhance productivity with AI

Learn AI concepts: Machine Learning, Generative AI, LLM, AGI and more
Capabilities and limitations of current AI tools
Using generative AI to summarize content, learn and understand, develop and visualize ideas
Human-in-the-loop approach with Gen AI
The art of prompt engineering
Using Gemini in Gmail, Docs, Slides and other Google Tools
Learning with AI assistance: principles for effective learning

Power Skills

The “Power Skills” section is all about building the practical abilities you need to succeed in your career. This chapter is packed with hands-on exercises and practical tips to help you develop and improve skills like public speaking, time and task management, teamwork, decision making, interviewing, creating a personal LinkedIn profile and resume, and using business English relevant to the hi-tech industry. This comprehensive skill development module aims to equip you with the essential tools for a successful career path.