Complete Excel Data Analysis Bootcamp For Beginners

Learn how Excel handles dates, auto fills date sequences, and converts year, month, and day into a proper date using the built-in date function.

Import a messy csv text file into an Excel workbook from external data. Set the delimiter to comma, semicolon, or tab to separate columns.

Apply sort and filter in Excel using the ribbon, select categories, and clear filters; expand the selection to keep columns aligned when sorting, and copy filtered data to another workbook.

Apply conditional filtering with operators to refine data, viewing top ten and bottom ten items, and mixing criteria like greater than or equal to 1000 and not equal to 147.

Use Excel's logical operators to compare strings across columns a and b, returning true or false to identify equal values and differences.

Learn to use Excel to check conditions with if logic, flag over-budget items (threshold 50), and build an Amount Check column.

Master applying and/or conditions in Excel to evaluate two criteria. See how and requires both true, while or requires only one, with examples like B2>20 and C2≥50.

Explore basic Excel formulas to perform sums, averages, medians, standard deviation, and other statistics on a dataset, using the equal sign, formula bar, and cell ranges like E2 to E20.

Explore how auto sum automatically detects a range of numbers and performs quick sums, along with max and min calculations, for columns or rows.

Explore how brackets and the order of operations in Excel govern calculations, with a practical tax example showing why parentheses matter and how multiplication interacts with addition.

Explore how brackets and the BODMAS order of operations work in Excel by evaluating a nested expression and seeing how Excel carries out computations.

Apply descriptive statistics to summarize Excel data, including mean, median, standard error, and confidence intervals, to height and weight columns with group-by options.

Explore pivot tables to summarize data across countries and food items, using values in tonnes per hectare, with sums, averages, and flexible layouts across years.

Use pivot tables to summarize data and insert a slicer to filter by country and year, then view production totals for cereals, crops, and livestock like beef, pork, and poultry.

Explore switching from sum to average in a pivot table, use field settings to compute averages for cereals, poultry, and fish across European countries, and show data as percentages.

Learn to plot a histogram in Excel to show the distribution and frequency of a quantitative data set, using Excel decided bins or choosing your own bins.

Create a basic bar chart to visualize discrete data in tonnes per hectare, using bar plots and pie charts, with data labels and chart titles for Belgium's food items.

Explore how to create and customize bar charts in Excel, including stacked and side-by-side layouts, to compare math and reading scores across parental education categories.

Create an x-y scatterplot to compare two continuous variables like height and weight, observe their positive correlation, and consider adding a trend line for simple linear relationships.

Line charts visualize time series data, such as Google stock prices over dates, displaying open and high values with customizable colors, legends, and marked data points.

Explore how to activate and use Excel add-ins, including the analysis tool pack and solver, to perform basic statistics and data analysis.

Explore the relationship between two quantitative variables using scatterplots and correlation, identify positive or negative association, and recognize that correlation does not imply causation.

Compute the correlation between opening and high stock prices in Excel using the CORREL function and input ranges, then explore the data analysis tool to create a correlation matrix.

Compute correlations in Excel using the correlation function to analyze open and volume and build a correlation matrix for open, high, low, and close across 48 entries.

Introduces linear regression, from simple to multiple, detailing the regression equation, slope, intercept, residuals, and least-squares fitting with r-squared and p-values.

Determine the Y (response) and the X (predictor) before linear regression, and understand how this choice shapes interpretation, such as median home prices responding to interest rate.

Learn to run a basic ordinary least squares regression in Excel, interpret outputs with height as the response and weight as the predictor, and examine R-squared, coefficients, and p-values.

Learn to encode gender as a dummy variable for regression by assigning male as 1 and female as 0, enabling categorical attributes with two levels.

Learn how to perform a multiple linear regression with weight and a male female dummy variable. Interpret the adjusted r-squared, assess predictor significance with p-values, and refine the height equation.

Explore how adding independent variables inflates R-squared in regression and learn how adjusted R-squared corrects for variables and observations to reveal true importance.

learn how to use excel's linear forecast to predict future values in time series, using a dataset of Google stock prices and volume to forecast the highest price.

Choose between SQL and NoSQL databases for data analysis, applying a quick rule to decide when SQL data fits.