DATA ANALYTICS AND STATISTICAL MODELLING
About This Course
At the end of this course, learners will be able to:
Use Python and Pandas to collect data from different sources
Merge data using Pandas
Check and clean for the 5 common data issues: outliers, duplicates, missing values, out-of-range values and inconsistencies.
State the 5 data quality metrics: accuracy, completeness, auditability, consistency, validity.
Create databases in PostgreSQL
Create tables in PostgreSQL with multiple columns and different data types including integers, floats, text and dates
Create functions in PostgreSQL to perform data processing
Create a data dictionary based on a set of guidelines for a given dataset.
Document ETL processes including: data source, data destination, ER Diagrams, description of transformation.
Perform 5 checks for structure and quality of data warehouse
What You'll Learn
Entry Requirements
Academic Requirement
- 3 GCE O level subjects (including English) with a pass (i.e. C6
and above) or equivalent
- Completion of General Assembly's Course - Data Visualization
Language Proficiency
- GCE O Level English with a pass (i.e. C6 of above) or
- Workplace Literacy & Numeracy (WPLN) Level 5