Course Intro

Onboarding

Karen Hovhannisyan

2026-04-02

Agenda


  • A little bit about myself
  • What is Data Analytics?
  • Who is a Data Analyist?
  • Program Overview
  • Q&A

What is Data Analytics?

… turning data into understanding and action.

<img src=“/img/statistics/00/whatisda.png” height=“600px” style=“float:center; margin-left:700px;”>

Business Questions…

  • Why are customers leaving?
  • Which products generate the most revenue?
  • Which marketing channel performs best?
  • Where are operational inefficiencies?
  • What drives customer behavior?

Types of Work…

  • Cleaning data
  • Exploring data
  • Visualizing patterns
  • Running analyses
  • Communicating insights

How Companies Use Analytics

Analytics supports decisions across the entire business:

  • Marketing: ROI, segments, attribution
  • Sales: funnel, forecasting
  • Finance: profitability, risk
  • Operations: optimization, efficiency
  • Product: customer journeys, A/B tests

Analytics isn’t optional.
It’s how companies stay competitive.

Decision-Making with Analytics

The cycle:

  1. Define the business problem
  2. Identify and gather data
  3. Clean and structure it
  4. Analyze patterns
  5. Build dashboards & reports
  6. Recommend actions
  7. Monitor results and iterate

Types of Analytics


Descriptive Diagnostic Predictive Prescriptive
Purpose Learn what happened. Learn why something happened. Learn what is likely to happen. Learn what to do.
Output Static reports with KPIs. Reports with drill-down, slicing, and dicing capabilities. Forecasts. Actionable recommendations.

Skills a Data Analyst Needs

  • Technical Skills
  • Business(soft) Skills

Technical Skills

  • SQL
  • Python
  • Excel
  • Statistics
  • Visualization tools (Tableau, Power BI)

Business

  • Problem formulation
  • Interpretation
  • Data storytelling
  • Communication

About Bootcamp


Instructor: Karen Hovhannisyan
Duration: 6 Months
Modules: 6
Tech Stack: PostgreSQL · Python · Tableau · Docker · Git/GitHub
Tools: VScode · Pgadmin · Terminal · Excel

Goal

Prepare analytical thinkers , not just tool users


Equipping participants with a complete, functional, and standardized analytics environment from coding to version control, databases, and visualization tools.

Learning Journey

   

Who This Program is For

  • Students
  • Professionals switching careers
  • Analysts wanting to upskill

Module 1 | Statistical Thinking


Outcome: Build a foundational analytical mindset and statistical reasoning before moving to SQL and Python.

  • Duration: 4 weeks
  • Foundations: descriptive → inferential, sampling, distributions
  • Hypotheses, p-values, confidence intervals
  • Mini storytelling project

Module 2 | SQL

Outcome: Develop the ability to query, transform, and aggregate data efficiently, creating analytical datasets that form the foundation for Python-based analysis and dashboards.

  • Duration: 6 weeks
  • Basic queries
  • Joins, CTEs, window functions
  • Stored procedures, UDFs, CUBE/ROLLUP, materialized views
  • Export analytical tables → CSV (for Tableau)
  • Mini storytelling project

Module 3 | Python

Outcome: Strengthen analytical programming skills by automating data preparation, performing statistical tests, and applying predictive techniques to extract insights from SQL data.

  • Duration: 7 weeks
  • Python fundamentals, pandas, visualization
  • A/B testing, regression, clustering (high-level)
  • SQLAlchemy: read/write with PostgreSQL; ETL
  • Mini project

Module 4 | Tableau

Outcome: Learn to visualize, interpret, and communicate insights effectively through interactive and dynamic dashboards that transform analytical results into compelling business narratives.

  • Duration: 5 weeks
  • Visual analytics, LODs, interactivity
  • Design principles & performance
  • Build public dashboards (Tableau Public)

Module 5 | Capstone

Integrate all acquired skills to design and deliver a complete end-to-end analytics project. From raw data to visual storytelling,demonstrating readiness for real-world analytics roles.

  • Duration: 3 weeks
  • Full pipeline: Problem Definition → SQL → Python → Tableau
  • GitHub repo + Tableau Public link
  • Portfolio
  • Final presentation

Connected the dots

  • Module 1 — Statistical Thinking: Build a foundational analytical mindset and statistical reasoning before moving to SQL and Python.

  • Module 2 — SQL: Develop the ability to query, transform, and aggregate data efficiently, creating analytical datasets that form the foundation for Python-based analysis and dashboards.

  • Module 3 — Python: Strengthen analytical programming skills by automating data preparation, performing statistical tests, and applying predictive techniques to extract insights from SQL data.

  • Module 4 — Tableau: Learn to visualize, interpret, and communicate insights effectively through interactive and dynamic dashboards that transform analytical results into compelling business narratives.

  • Module 5 — Capstone: Integrate all acquired skills to design and deliver a complete end-to-end analytics project—from raw data to visual storytelling—demonstrating readiness for real-world analytics roles.

Course Logistics

Lecture Notes

All the course materials you can find here

The material includes:

  • Slides
  • Extended Reading
  • Homework
  • Program installation and setup

Q&A