SQL for Data Analysis: Extracting and Transforming Data from Relational Databases

Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 5 أيام, 25 ساعة.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work.

Covers relational database querying, SELECT, filtering, sorting, joins, aggregation, subqueries, CTEs, and analytical extraction. Key source references: SQL.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, SELECT, 2, 3.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, 3, 4, 5, 6.

Covers relational database querying, SELECT, filtering, sorting, joins, aggregation, subqueries, CTEs, and analytical extraction. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: 1, 2, SQL, 3, 2012.

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, 3, 1997, 31, 12, 4, 5, 10.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, 3, 4, 5.

Covers aggregation, grouping, HAVING filters, summary tables, and analytical indicators. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: SELECT, GROUP BY.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work. Key source references: WHERE, HAVING.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Key source references: 50, HAVING, 1997, WHERE.

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 2, 3, JOIN, 4, 1000, 5.

Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Key source references: JOIN.

Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: SELECT, 1, JOIN, 2.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work.

Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Key source references: JOIN.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: SELECT, WHERE.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, SELECT, 2.

Covers aggregation, grouping, HAVING filters, summary tables, and analytical indicators. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: CTE, SELECT, GROUP BY, WHERE, 1000.

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, JOIN, WHERE, 3, 4, CTE, 10, 000.

Covers aggregation, grouping, HAVING filters, summary tables, and analytical indicators. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: GROUP BY.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work. Key source references: 1, 2, 3, 4.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, 3.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity.

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, 3, 4.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, SELECT, 3, 4.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 1, 2, WHERE, SELECT.

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work. Key source references: WHERE.

Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: WHERE, JOIN.

Covers relational database querying, SELECT, filtering, sorting, joins, aggregation, subqueries, CTEs, and analytical extraction. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: SQL.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, SELECT, 2, WHERE, HAVING, 3, 2023, 01, 12, 31, 4, 5, ALL.

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Covers Power BI Desktop, Power Query, data models, DAX, visualizations, Power BI Service, dashboards, sharing, and scheduled refresh. Covers dashboard design principles, executive KPIs, interactive filters, navigation, visual storytelling, and publishing. Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers report structure, findings, recommendations, executive communication, and actionable insight delivery. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, Dashboard, 2, 10, 3, 4, 5, Excel, Power BI, 6.

Covers inner joins, left joins, right joins, full joins, keys, table relationships, and query validation. Covers aggregation, grouping, HAVING filters, summary tables, and analytical indicators. Covers window functions, partitions, ranking, running totals, moving averages, and analytical SQL calculations. Key source references: JOIN, GROUP BY, Window Functions.