Data Analytics

Correlation, simple and multiple linear regression, coefficients, assumptions, and interpretation

Handling missing values, duplicates, inconsistent formats, outliers, and noisy data

Mean, median, mode, variance, standard deviation, percentiles, quartiles, and range

Formulas, functions, sorting, filtering, pivot tables, lookup functions, conditional logic, and analysis

Understanding datasets through summaries, distributions, relationships, patterns, and anomalies

Python fundamentals for analytics including variables, data structures, functions, and working with datasets

SELECT, WHERE, ORDER BY, GROUP BY, HAVING, joins, aggregations, subqueries, and basic data manipulation

CTEs, window functions, CASE expressions, date functions, ranking, and analytical queries

BI concepts, dashboard design, KPIs, interactive reporting, and decision support

Choosing charts, visual encoding, readability, comparison, trends, distributions, and avoiding misleading visuals

Null and alternative hypotheses, p-values, significance levels, test statistics, and common tests

DataFrames, Series, indexing, filtering, aggregation, transformation, and numerical operations

Data loading, Power Query, data modeling, reports, dashboards, and basic visualization

Business understanding, data collection, preparation, analysis, interpretation, and communication

Turning analytical results into meaningful findings, recommendations, and business decisions

Entities, relationships, normalization, star schema, fact tables, dimension tables, and analytical models

Accuracy, completeness, consistency, validity, uniqueness, timeliness, and data-quality checks

Creating analytical charts and plots using Python visualization libraries and interpreting results

Extract, transform, load processes, transformations, validation, and data pipeline concepts

Data analytics concepts, objectives, lifecycle, roles, use cases, and types of analytics

Basic probability concepts, events, conditional probability, independence, and probability rules

Time-based data, trends, seasonality, moving averages, decomposition, and basic forecasting

Methods for collecting data from files, databases, APIs, surveys, and other sources

Responsible data use, privacy principles, security, bias, consent, and ethical analytics

Structured, semi-structured, and unstructured data; internal and external data sources

Defining, calculating, comparing, and interpreting operational and business performance metrics

Population versus sample, sampling methods, sampling error, confidence intervals, and estimation

Normal, binomial, Poisson, uniform, and other commonly used distributions

Translating business questions into analytical problems, selecting methods, and evaluating conclusions

Presenting findings clearly through narratives, visualizations, reports, and stakeholder communication