Introduction to Data Literacy Training

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 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. Training Prerequisites None. Data Literacy Training Outline Section 1: Understanding Data Module 1: Qualitative vs. Quantitative DataModule 2: Structured vs. Unstructured DataModule 3: Data at Rest, in Use, and MotionModule 4: Transactional vs. Master DataModule 5: Big DataModule 6: Storing DataModule 7: DatabaseModule 8: Data WarehouseModule 9: Data MartsModule 10: The ETL ProcessModule 11: Big Data FrameworksModule 12: Cloud SystemsModule 13: Edge ComputingModule 14: Batch vs. Stream ProcessingModule 15: Graph DatabaseModule 16: Visualizing unfamiliar data Section 2: Using Data Module 1: Analysis vs. AnalyticsModule 2: Descriptive StatisticsModule 3: Inferential StatisticsModule 4: Business Intelligence (BI)Module 5: Artificial Intelligence (AI)Module 6: Machine Learning (ML)Module 7: Supervised LearningModule 8: Regression AnalysisModule 9: Time Series ForecastingModule 10: ClassificationModule 11: Unsupervised LearningModule 12: ClusteringModule 13: Association RulesModule 14: Reinforcement LearningModule 15: Deep LearningModule 16: Natural Language Processing (NLP) Section 3: Reading data Module 1: Data Quality AssessmentModule 2: Data DescriptionModule 3: Measures of Central TendencyModule 1: Measures of Spread Section 4: Interpreting Data Module 1: Correlation AnalysisModule 2: Correlation CoefficientModule 3: Correlation and CausationModule 4: Simple Linear RegressionModule 5: R-SquaredModule 6: ForecastingModule 7: Forecast ErrorsModule 8: Statistical TestsModule 9: Hypothesis TestingModule 10: P-ValueModule 11: Statistical SignificanceModule 12: ClassificationModule 13: AccuracyModule 14: Recall and PrecisionModule 15: Visualizing Data to Communicate Results