Data literacy is the ability to read, use, understand, and communicate data as information. At the organisational level, it is the extent to which the organisation, and its members, comprehends and communicates data to drive value. A data-literate workforce will be able to understand, share common knowledge of, and have meaningful conversations about data.
After taking this data literacy course, you'll be able to understand, use, read, and interpret enterprise data consistently and derive trusted insights. Your skills will be manifested in the execution of your role in the organisation.
Data Literacy Training Delivery Methods
Data Literacy Training Information
In this course, you will learn how to:
Interpret and explain:
- Straightforward statistical operations such as correlations or judge averages.
- A business case based on concrete, accurate, and relevant data.
- The output of the organisation's systems or processes to stakeholders.
- The output of machine learning algorithms.
- The essence of data shared with colleagues or other organisations.
Use data in the:
- Managing complex supply chains.
- Understanding of market and customer requirements.
- Driving product and service innovations.
- Evaluation and monitoring of process efficiencies.
- Mitigation of risks.
- Spotting unexpected operational issues and identification of their root causes.
Training Prerequisites
None.
Data Literacy Training Outline
Module 1: Qualitative vs. Quantitative Data
Module 2: Structured vs. Unstructured Data
Module 3: Data at Rest, in Use, and Motion
Module 4: Transactional vs. Master Data
Module 5: Big Data
Module 6: Storing Data
Module 7: Database
Module 8: Data Warehouse
Module 9: Data Marts
Module 10: The ETL Process
Module 11: Big Data Frameworks
Module 12: Cloud Systems
Module 13: Edge Computing
Module 14: Batch vs. Stream Processing
Module 15: Graph Database
Module 16: Visualising unfamiliar data
Module 1: Analysis vs. Analytics
Module 2: Descriptive Statistics
Module 3: Inferential Statistics
Module 4: Business Intelligence (BI)
Module 5: Artificial Intelligence (AI)
Module 6: Machine Learning (ML)
Module 7: Supervised Learning
Module 8: Regression Analysis
Module 9: Time Series Forecasting
Module 10: Classification
Module 11: Unsupervised Learning
Module 12: Clustering
Module 13: Association Rules
Module 14: Reinforcement Learning
Module 15: Deep Learning
Module 16: Natural Language Processing (NLP)
Module 1: Data Quality Assessment
Module 2: Data Description
Module 3: Measures of Central Tendency
Module 1: Measures of Spread
Module 1: Correlation Analysis
Module 2: Correlation Coefficient
Module 3: Correlation and Causation
Module 4: Simple Linear Regression
Module 5: R-Squared
Module 6: Forecasting
Module 7: Forecast Errors
Module 8: Statistical Tests
Module 9: Hypothesis Testing
Module 10: P-Value
Module 11: Statistical Significance
Module 12: Classification
Module 13: Accuracy
Module 14: Recall and Precision
Module 15: Visualising Data to Communicate Results
