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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 to 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
  • 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 and Machine Learning Basics
  • Pivot tables, contingency tables
  • Types of biases and why correlation is not causation
  • Advanced Excel/Spreadsheets
  • Power BI
  • Statistics for Data Analysis

Introduction to exploratory data analysis using SQL and BigQuery
  • Introduction to big Data
  • 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
  • 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
  • Advanced SQL programming and query optimization on top of Cloud
  • Database Structures
  • Advanced SQL
  • Basics of Python
  • The basics of Gemini, ChatGPT and other AI tools
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.
Introduction to Product Analyst and Data Studio
  • Defining the Characteristics of an Effective Dashboard
  • Leveraging Data as a Valuable Product
  • Utilizing Statistical Tests (t-tests, F-tests, Chi-Square) and Power Analysis for Experimental Insights
  • Crafting Compelling Narratives with Data
  • Selecting Appropriate Charts for Varied Analytical Objectives: Historical Performance and Leading Indicator Predictions
  • Identifying Key Metrics for Long-term Tracking and Valuable Data Segmentation
  • Data Pre-processing Strategies for Dashboards with an Emphasis on Flexibility
  • BI Development Basics
  • A/B Testing design
  • Data Visualization
  • Practical Analysis(Business and Product Challenges)
  • Business Orientation
  • Storytelling with Data
  • Tableau and other visualization tools besides Data Studio and BQ
  • Final Project (Capstone project)
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.

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