Module 1: Advanced SQL & Relational Analytics
30 learning hours
- Window functions, CTEs, complex joins
- Query optimization & indexing
- BigQuery analytics syntax
Applied project: Analyzing multi-million row financial transaction datasets.
Cloud Education Learning · Program Guide
Data Science & Big Data Analytics
CEL-DAT-606 · 16 Weeks (120 Hrs) · Live Online + Cloud Data Warehouses
Transform raw data into business intelligence using SQL, Python, BigQuery, Apache Spark, and PowerBI dashboards.
About the program
Master the full data analytics lifecycle: from exploratory data analysis and statistical hypothesis testing to cloud data warehousing, distributed Spark computing, and executive storytelling.
Program highlights
- Advanced SQL & BigQuery Analytics
- Python Data Ecosystem (Pandas, Matplotlib, Seaborn)
- Distributed Big Data with Apache Spark
- Interactive Dashboards in PowerBI & Tableau
- Real-World Business Case Studies
Curriculum
Module 2: Exploratory Data Analysis with Python
30 learning hours
- Data wrangling and cleaning with Pandas
- Statistical testing & probability distributions
- Visual storytelling
Applied project: Customer lifetime value and cohort retention analysis.
Module 3: Big Data Processing with Apache Spark
30 learning hours
- PySpark DataFrames and Spark SQL
- Distributed computing concepts
- Handling streaming data
Applied project: High-throughput log analysis pipeline on Spark.
Module 4: Business Intelligence & Executive Dashboards
30 learning hours
- PowerBI data modeling and DAX formulas
- KPI definition and dashboard UX
- Executive reporting
Applied project: Executive commercial performance dashboard.
Learning outcomes
- Write high-efficiency analytical SQL queries on cloud warehouses.
- Build automated data cleaning and visualization pipelines in Python.
- Design executive dashboards driving actionable strategic decisions.
Program details are subject to change. Subject to authorized partnership status and approved branding.