Statistics for Data Analysis: From Description to Inference

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 descriptive statistics, probability, sampling, inference, confidence intervals, hypothesis testing, correlation, and regression.

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers Python fundamentals, notebooks, data structures, data analysis libraries, scripts, automation, and reproducible analysis. 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, Excel, Python.

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers Python fundamentals, notebooks, data structures, data analysis libraries, scripts, automation, and reproducible analysis. 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, 50%, Excel, Python.

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.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 2, 3.

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. 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, 3, 4, 5.

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: 68, 95, 99, 7, 68%, 95%, 99.7%, 3.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Key source references: 30.

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: 95%, 1, 2.

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.

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. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 100, 35, 10, 2, 95%, 3, 40, 0, 30, 4, 99%, 5, 400.

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 0, 1, 05, 5%, p-value.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 30, 0, 25.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 30, 0, 3.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. 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.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities.

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, 30, 2, 0, 05, 3, 4, p-value, 03.

Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Key source references: 1, 7.

Covers duplicate detection, duplicate removal, record matching, and validation of unique keys. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. 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, 3, 4, p-value.

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Key source references: 3, ANOVA.

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 identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: 1, 5, 3, 2, ANOVA, 4.

Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. Key source references: 1, 0, 7.

Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. 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.

Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. 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.

Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Key source references: 1, 2, 3, 4.

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 linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. 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, 3, 4, 5, 200, 50, 30, 6.

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 descriptive statistics, probability, sampling, inference, confidence intervals, hypothesis testing, correlation, and regression. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. 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: 1000, 1, 0, 2, t-test, 3, 4, 5, 6.

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