Job Readiness

Identify missing values and formula errors in an Excel dataset and select an appropriate treatment.

Detect and remove duplicate records without deleting legitimate repeated transactions.

Standardize inconsistent text values with Excel text-cleaning functions.

Convert inconsistent date values into a valid, uniform Excel date format.

Retrieve matching values from another table with XLOOKUP.

Import and transform a CSV file with Excel Power Query.

Summarize business data by category and time with a PivotTable.

Choose and create an appropriate Excel chart for a stated business question.

Calculate and interpret the mean, median, and mode of a business dataset.

Calculate and interpret range, interquartile range, and standard deviation.

Detect potential outliers with the interquartile-range method.

Calculate percentage change, growth rate, ratio, and contribution to total.

Interpret the shape of a distribution from a histogram.

Distinguish a population from a sample and identify common sources of sampling bias.

Calculate a correlation coefficient and interpret its direction and strength without claiming causation.

Select an appropriate summary statistic for a business question and explain the choice.

Identify tables, rows, columns, primary keys, and foreign keys in a relational database schema.

Import a CSV dataset into a local PostgreSQL database table.

Retrieve specified columns with SELECT and assign readable aliases.

Filter records with comparison, logical, and pattern-matching conditions in WHERE.

Test for and handle NULL values in SQL query results.

Remove duplicates, order results, and limit output with DISTINCT, ORDER BY, and LIMIT.

Transform text fields with common SQL string functions.

Extract and transform date components with SQL date functions.

Calculate counts, totals, averages, minima, and maxima with SQL aggregate functions.

Summarize data by one or more dimensions with GROUP BY.

Filter aggregated results with HAVING.

Create conditional categories and metrics with CASE expressions.

Combine matching records from two tables with an INNER JOIN.

Preserve unmatched records from one table with a LEFT JOIN.

Join three or more tables by following primary-key and foreign-key relationships.

Combine compatible query results with UNION and UNION ALL.

Organize a multi-step SQL analysis with a common table expression.

Calculate several category-specific metrics in one query with conditional aggregation.

Rank records within groups with SQL window functions.

Calculate a running total with a window frame.

Compare a value with an earlier period using LAG.

Group timestamped records into daily, monthly, and quarterly reporting periods.

Validate a query result by reconciling row counts, totals, and join behavior.

Solve a timed SQL business case using filtering, aggregation, joins, and window functions.

Connect Power BI Desktop to Excel, CSV, and PostgreSQL data sources.

Use Power Query profiling to identify nulls, errors, distinct values, and column distributions.

Set appropriate data types and resolve import errors in Power Query.

Reshape a table with split, merge, pivot, and unpivot transformations.

Combine datasets with Power Query merge and append operations.

Determine the grain and key fields of each table before modeling.

Organize fact and dimension tables into a star schema.

Configure relationship cardinality and filter direction in a Power BI model.

Distinguish a DAX calculated column from a measure and choose the appropriate one.

Create DAX measures for totals, counts, distinct counts, averages, and ratios.

Explain row context and filter context in a given Power BI calculation.

Modify filter context with CALCULATE to answer a business question.

Create a date table and calculate month-over-month and year-over-year changes.

Choose a suitable Power BI visual for a comparison, trend, composition, or relationship.

Configure slicers, drill-through, and tooltips for interactive analysis.

Build and validate a clear one-page Power BI dashboard for a defined audience.

Run Python code in a Jupyter notebook and organize work with Markdown cells.

Create variables using numbers, strings, booleans, lists, and dictionaries.

Use comparison and logical operators to evaluate data conditions.

Write conditional statements that implement a business rule.

Use a loop to process a small collection of values.

Define and call a function with parameters and a return value.

Import and use a Python package in a notebook.

Diagnose a basic Python error by interpreting its traceback.

Load CSV and Excel data into pandas DataFrames and inspect their structure.

Select, filter, and sort DataFrame rows and columns.

Detect and treat missing values in a pandas DataFrame.

Remove duplicate records and convert columns to appropriate data types in pandas.

Create derived columns from existing DataFrame values.

Summarize data by category with pandas groupby operations.

Combine DataFrames with pandas merge operations.

Create and label a basic business chart from a pandas DataFrame.

Define the audience, business question, scope, and KPIs for a portfolio analysis.

Create a data dictionary and document assumptions for the capstone dataset.

Produce an analysis-ready dataset and a data-quality checklist from messy source files.

Write validated SQL queries that answer the capstone business questions.

Create a Power BI star schema and DAX measures for the capstone KPIs.

Build an interactive Power BI dashboard that highlights the capstone findings.

Write an executive summary that separates evidence, interpretation, and recommendations.

Package the capstone files, screenshots, and methodology in a clear GitHub repository README.

Tailor a one-page data analyst resume to a specific entry-level job description.

Create a concise LinkedIn headline, About section, and skills list for a junior data analyst profile.

Deliver a two-minute explanation of a portfolio project using problem, process, result, and impact.

Answer a behavioral interview question with the STAR structure.

Complete a timed Excel cleaning and analysis exercise.

Complete a timed SQL querying exercise and explain the solution aloud.

Explain the design, data model, and key measures of a Power BI dashboard in an interview.

Create a weekly job-application plan and track applications, follow-ups, and interview outcomes.