Introduction to Data Literacy Training
Course Outline
Data literacy is the ability to read, use, understand, and communicate data as information. At the organizational level, it is the extent to which the organization, 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 organization.
Introduction to Data Literacy Training Benefits
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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 organization's systems or processes to stakeholders.
- The output of machine learning algorithms.
- The essence of data shared with colleagues or other organizations.
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.
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Training Prerequisites
None.
Data Literacy Training Outline
Section 1: Understanding Data
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: Visualizing unfamiliar data
Section 2: Using 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)
Section 3: Reading data
Module 1: Data Quality Assessment
Module 2: Data Description
Module 3: Measures of Central Tendency
Module 1: Measures of Spread
Section 4: Interpreting Data
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: Visualizing Data to Communicate Results
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