Data AnalystWith AI+ AWS + Snowflake

Text, aggregation and conditional functions; SUMIFS, IF, SWITCH and FILTER; VLOOKUP, XLOOKUP and INDEX–MATCH; essential data cleaning.

Column, bar, line, pie, donut, histogram, waterfall and combination charts; formatting charts for business presentations.

Data Tables, Goal Seek, Scenario Manager and Solver for comparing assumptions and business outcomes.

Apply spreadsheet analysis and reporting to a consumer-goods business scenario.

Structure source data; create, navigate, refresh and format PivotTables; use grouping and conditional formats.

Percentages, running totals, differences, rankings and calculated fields for analytical reporting.

Practice business analysis and reporting with a textile-company dataset.

Connect, clean, merge and transform data; add columns and load refreshable results into Excel.

Build data models and DAX calculations; analyze business metrics, add targets and apply conditional formatting.

Create interactive Excel dashboards, dynamic PivotTable reports and the 365 Coffee Cafe analysis.

Databases, data lakes, warehouses and data marts; SQL Server, MySQL and PostgreSQL; query tools and basic SQL syntax.

SELECT, FROM, WHERE and ORDER BY; aliases, GROUP BY, TOP N, DISTINCT and OFFSET FETCH.

Numeric, string and date-time functions; counting rows, comparisons, logical operators, IIF, CASE and NULL handling.

Relationships and ER diagrams; INNER, OUTER, LEFT, RIGHT and CROSS joins; UNION, views, self joins and natural joins.

Scalar, multi-row and correlated subqueries; derived tables and common table expressions (CTEs).

ROW_NUMBER, RANK, DENSE_RANK, LAG and LEAD; partitioning results and troubleshooting analytical queries.

Remove duplicates, handle missing values, split and standardize columns, and prepare datasets for analysis.

World Life Expectancy, 365 Super Mart, financial-statement analysis with Power BI, and 365 Technologies.

Installation, configuration, interface, workflow and core settings.

Data connectors, table profiling, text/number/date transformations, conditional columns, grouping, aggregation, merge, append and parameters.

Normalization, fact and dimension tables, active/inactive relationships, cardinality, star and snowflake schemas, evaluation order, expanded tables and context transition.

Text, date, logical and conditional functions, including SWITCH for dynamic calculations.

Implicit and explicit measures; filter context; math, statistics, counting, filtering and iterator functions; variables, time intelligence, CALCULATE, ALL and FILTER.

Advanced charts, filtering, slicing, drilling, numeric/field parameters, navigation and tooltips; anomaly detection, smart narratives, Q&A, decomposition trees and key influencers.

365 Super Market, Granny’s Cafe, Media Feathers, redesigned Media Feathers and Retail Inc.; explore the larger cohort project collection below.

Power BI Projects 11–20 demonstrate web, cloud, database and file connections, plus sales-data modeling.

Python applications, Anaconda, Jupyter Notebook, notebook comments and the development environment.

Numbers, strings, lists, dictionaries, tuples, sets and booleans; string methods, concatenation and formatting.

Operators, if/else, for and while loops, break, continue, pass, list comprehensions, IN and NOT IN.

Built-in and user-defined functions, arguments, keyword arguments, lambda, map, filter and exceptions.

Array creation, slicing, indexing, arithmetic, mathematical functions and input/output operations.

Series and DataFrames; selection, aggregation, grouping, cleaning and combining data; Excel, CSV, HTML and database input/output.

Bar, line, scatter, histogram and box plots; selecting appropriate charts and improving their presentation.

Categorical, relational, distribution, regression and matrix plots; multi-plot grids and visual styling.

Installation, interface, first charts, color, formatting and filters.

Time series and categorical aggregation; calculated fields, table calculations and interactive parameters.

Build and format dashboards; add filters and highlighting; share an interactive analytical story.

Connect multiple datasets and combine sources for analysis.

Create hierarchies, geographical maps and visuals that communicate business findings.

Histograms, box-and-whisker plots, area and dual-combination charts, Gantt charts, treemaps and packed bubbles.

Data Interpreter, pivots, splitting and combining columns, the Analytics tab, clustering and forecasting.

Combine Tableau techniques in a business-performance dashboard.

Understand the platform, storage options, data transformations and enterprise analytics use cases.

Explore Fabric through the Power BI interface, its architectural framework and approaches to handling enterprise data.

Define the challenge and plan a pilot for the 365 Enterprises scenario.

Integrate external sources, transition existing BI content, publish reporting and automate refreshes with pipelines.

Apply Fabric to supply-chain operations and business reporting.

Classification and regression; supervised and unsupervised learning; key algorithms and analyst tools.

Requirements, scope, collection, preparation, feature engineering, model selection, training, evaluation, tuning, deployment and MLOps.

Interpret accuracy, precision, recall and F1 score when assessing model performance.

Data infrastructure, collection, storage and transformation; privacy, ethics and governance.

Use AI to support collection, cleaning, transformation, insight generation and visualization.

Use AI to improve analytical explanations, emails and presentations while retaining human judgment.

Resume customization, professional networking and AI-assisted technical, case-study and behavioral interview practice.

Course outcomes, prerequisites, learning routines and the practical workflow for analysts moving into cloud data.

OLAP versus OLTP; Data Engineer, Data Analyst and AI Engineer roles; data lakes, warehouses and lakehouses; ETL versus ELT.

Bronze, silver and gold layers; CSV/JSON versus columnar Parquet and their use cases.

Cloud accounts, S3 storage, IAM roles and policies, Snowflake trial setup, integration objects, trust relationships and external stages.

Create a repeatable flow from raw files into S3 and Snowflake, then validate the loaded data.

Explore and transform data with Snowflake SQL; connect Power BI through Import/DirectQuery and connect Tableau.

Build reporting for rainfall, monthly usage and cost savings; complete an S3 → Snowflake → BI workflow with a repeatable delivery checklist.

Use Claude to support the analytical tasks introduced in the course, with review of generated queries and explanations.

Practice the web-data collection and Google Sheets scenario in the cohort project collection.

Explore the Gmail data-extraction automation scenario.

Build and examine an agent workflow for working with SQL queries.

Apply the n8n Insight Generator and Data Governance With AI Agents project scenarios.

The Power Automate AI Automation project is planned; project materials are not yet available.