Data Analytics + AI: data analytics and artificial intelligence

Course overview

Description generated based on course syllabus and open data.

About Data Analytics + AI: scope and approach

Data Analytics + AI covers the data lifecycle: collection, cleaning, analysis, visualization, and interpretation. The toolkit typically includes Python (pandas, NumPy), SQL for database querying, Power BI for dashboards, and core artificial intelligence methods (classification, regression, simple machine learning models) to support analytical findings.

The approach emphasizes reproducibility, transparency, and data ethics: version control (Git), documentation, correct metric interpretation, and attention to data quality.

Who Data Analytics + AI suits and who it does not

Suitable for (Data Analytics, data analysis)

• Beginners aiming to systematically learn basic statistics, SQL, Python, and visualization.
• Adjacent roles (marketing, finance, operations) that need analytical reports and Power BI dashboards.
• IT specialists adding data analysis and AI fundamentals to their stack.
• Teams seeking unified terminology and processes for working with data.

Not suitable for (AI, artificial intelligence)

• Those expecting immediate outcomes without regular hands‑on work with data.
• Needs focused on advanced ML or deep neural networks beyond core fundamentals.
• Scenarios where analytics must be performed without access to source data or context.

Problems → analytical outcomes with Data Analytics and AI

• Fragmented data sources → unified datasets, clear data models, and verified metrics.
• Dirty, duplicated records → cleaned datasets and reproducible preparation pipelines.
• Hidden trends → Power BI visualizations and descriptive statistics for insights.
• Manual reporting → automated dashboards with data refresh.
• Uncertain forecasts → basic ML (AI) models for decision support with validation and constraints.

Data Analytics + AI vs learning alternatives

Self‑study in data analytics

• High flexibility but uneven depth and lack of a cohesive roadmap.
• Requires validating sources and building a personal curriculum.

Short intensives / AI workshops

• Focused topics and limited duration.
• Often cover a single tool without system‑level practice.

Formal education (university)

• Strong theoretical foundation and academic depth.
• Less emphasis on current toolchains used in daily data analytics work.

Learning outcomes from Data Analytics + AI (competencies)

• Data preparation: collection, cleaning, joining, transformation, and quality checks.
• SQL: querying, aggregation, joins, views, and basic optimization.
• Python for analytics: pandas, NumPy, visualization (Matplotlib/Seaborn), basic automation.
• Power BI: data modeling, DAX calculations, interactive dashboards, and publishing.
• AI/ML fundamentals: simple models, validation, interpretation, and limitations.
• Documentation and reproducibility: Git, project structure, transparent metric methodology.
• Portfolio artifacts: analytical case, dashboard, and an exploratory data analysis notebook.