Excel for Data Analysis. Learn the Art of Excel Analytics.

Learn to filter and sort data in Excel to slice and dice movie lists by rating, genre, and box office, using drop-down filters, custom sorts, and multi-criteria sorting.

Master pivot tables in Excel to reorganize and summarize data for analysis and reporting, using preformatted tables, no blanks, and no extra columns, with filters and value summaries.

Explore how to use conditional formatting in Excel to highlight income and loan amount against the average annual income, color-code outcomes, and freeze panes for clearer data analysis.

Learn to print an Excel sheet efficiently by choosing the right orientation, fitting columns on A4, using print preview and the control p shortcut, with gridlines, headings, and repeating headers.

Learn how to protect Excel workbooks and sheets with passwords, restrict edits, and create clear file names using timestamps to ensure secure, searchable data analysis reports.

Explore the banks' telemarketing dataset to identify customer profiles most likely to buy, using demographic data and contact details. Predict future campaign responses to guide acquisition and marketing insights.

Learn segmentation techniques and Excel formulae to uncover demographic insights, group ages into bands, and apply and copy formulas to predict which customers are likely to say yes to campaigns.

Perform univariate analysis of average response rate across age bands to reveal trends, from low engagement in midlife to spikes after retirement, and explore marital status effects and duplicates removal.

Apply an Excel formula to calculate a response rate using average and if criteria, select ranges with shortcuts, and use dollar signs to fix references when copying.

Explore multivariate analysis by combining two columns with concatenate and ampersand to create a new field, copy formulas, and identify unique values.

Master calibrating category-wise response rates in Excel using formulas, text to columns, left and upper functions, and conditional formatting with color scales for the marketing campaign.

Explore how to customize an Excel bar plot by adjusting tick marks, interval units, alignment, and text direction, then experiment with colors and moving the chart to a new sheet.

Explore the Excel chart types for data analysis, including column/bar, line, and scatter plots, plus stacked and pie charts, and learn when to use them with categorical and numerical variables.

Explore when to use column, line, bar, pie, and scatter plots in Excel to reveal trends, compare categories, and enhance readability for actionable insights.

Analyze fraud claims in life insurance using bar plots and box plots to reveal age-based patterns and distinguish fraudulent from legal claims.

Explore pivot tables in Excel for data aggregation, enabling drag-and-drop analysis to identify top selling products, track trends, and spot exceptions in sales data.

Learn to use pivot tables to compute and format average response rates as a percentage by categories such as marital status and education, enabling quick, formula-free data analysis.

Create pivot tables to summarize data, drag the response rate to values, and apply conditional formatting to highlight trends and exceptions; ensure the sample count exceeds 30 for population-level analysis.

Explore how to use vlookup to pull data from multiple sheets with a common key column, ensure the lookup column is first, and set range_lookup to false.

Use vlookup to merge data from two sheets by matching a common column, returning salaries for occupations with exact match and absolute references.

Name the salary column on sheet one as salary one, then use Vlookup to look up values from that named range, simplifying the formula.

Learn to perform vlookup from a different workbook, concatenate marital and education data into a single key, and correctly reference lookup columns and directories to determine qualified yes or no.

Identify common VLOOKUP mistakes, such as forgetting absolute ranges, using approximate matches, and looking up values on the left side of the lookup column, with tips for cross-sheet references.

Identify common Excel errors and fix them with error-handling and default hyphen values, build computed columns like balance, and debug formulas by tracing dependencies.

This investment case study uses company, investment, and mapping data to identify top English-speaking countries and leading company categories for a 5-15 billion USD startup investment.

Learn to clean and unify data in Excel by mapping category lists to category types, linking company and funding sheets via a unique permalink, and handling duplicates and missing values.

Learn data preparation in Excel: copy and clean data, count records, map category types with an if formula and absolute references, and map values from company sheet with a lookup.

Copy raise amount and funding round type from the round two sheet to the company sheet using VLOOKUP, remove the funding round column, and verify no hash values.

Clean and organize data in Excel by deleting unnecessary columns, renaming sheets, and applying lookups to map English country and funding type, preparing the dataset for analysis.

Analyze investment data in Excel by funding type, use pivot tables to find the most frequent fund type and countries USA, GBR, and Canada, leading into sector analysis.

Present data insights to the CEO and stakeholders with a concise ppt. Highlight global trends, top markets, and leading investment types and company categories from cleaned, mapped data.

Discover why sql is a key in data science, as analysts tame massive data from platforms like Facebook, Twitter, and Gmail, unlock insights, and boost earning potential beyond 100k.

learn how relational database management systems store data on servers and enable efficient retrieval of account statements, as shown by a bank’s search for a customer’s statement.

Explore the SQL concept and how a database engine retrieves data from a server in response to a command, with examples illustrating data manipulation language and common definition language.

Learn to connect Python and R to an RDBMS, run SQL queries, and extract data for analysis and visualization using relevant libraries.

Explore how machine learning uses historical data to predict loan defaults and classify emails as spam, while distinguishing regression and classification (supervised) from clustering (unsupervised).

Explore simple linear regression to model how smartphone sales relate to marketing spend, estimate the intercept and slope, and use residual sum of squares for fit and trend forecasting.