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Data Intelligence
Transforming data into information, insights, and data systems that can be utilized for decision-making and AI development.
LEARNING
Python for Data Science, NumPy, Pandas, Data Cleaning, Exploratory Data Analysis (EDA), Statistical Analysis, Feature Engineering, Machine Learning Fundamentals, Model Evaluation, Data Visualization, Predictive Modeling.
Expected Skills
- Ability to understand and explore datasets.
- Ability to discover patterns and insights.
- Ability to build predictive models.
- Ability to explain analytical results scientifically.
OUTPUT
Data analysis, Prediction model, Experiment, Research, Dataset analysis, Dashboard/visualization.
LEARNING
Data Analysis, SQL, Data Visualization, Business Intelligence, Descriptive & Diagnostic Analytics, Dashboard, KPI & Metrics, Data Storytelling, Reporting, Analytical Thinking.
TOOLS
SQL, Python, Excel/Spreadsheet, Power BI / Tableau, Pandas.
OUTPUT
Analytical report, Dashboard, Data storytelling, Business insight, Research supporting analysis.
LEARNING
SQL, Database, Data Modeling, ETL / ELT, Data Pipeline, Data Warehouse, Data Lake, API & Data Ingestion, Batch & Streaming Data, Data Processing, Workflow Orchestration.
TOOLS/TECHNOLOGIES
PostgreSQL / MySQL, Python, Apache Airflow, Spark, Kafka, Cloud data services.
OUTPUT
Data pipeline, Data warehouse, ETL system, Data ingestion system, Dataset infrastructure.
LEARNING
Cloud Computing, Storage, Compute, Database Infrastructure, Distributed Systems, Data Security, Infrastructure Monitoring, Scalability, Data Architecture.
FOCUS
Not just "processing data," but building the infrastructure so data can be used reliably and scalably.