Introduction to Data Analytics with Microsoft Excel

Assess whether data analytics fits your skills, natural talent, and passion by exploring the three-way intersection that defines a fulfilling career.

Data analytics studies vast data to uncover trends and insights, guiding timely, data-driven decisions and telling the data story with narrative clarity.

Qualitative data cannot be reduced to measurable outcomes and arises from interviews, focus groups, or observations, while quantitative data can be quantified and measured as facts and numbers.

Identify and evaluate data sources, understand why data is collected, distinguish structured and unstructured data, and ensure data integrity through engineering to combine internal and external datasets.

Learn to compute mean, mode, median, and range from data sets in Microsoft Excel, including how to average values, find most frequent value, identify the central value, and measure spread.

Explain normal and non-normal data by comparing even distributions, predictability, and required data points; explore kurtosis and skewness to assess tails and symmetry in data.

Identify outliers as data points far from the main data flow, seen on run or line charts. Scrub them or investigate why they occurred to shape the narrative.

Explore real world data sets in Excel, unlock datasets via a macro-enabled admin sheet, and enable the analysis tool pack to access descriptive statistics tools for analytics.

Explore data intimacy by examining a claim data set in Excel, identifying key elements such as claim date, ID, type, handler, time, and outcome to prepare for analyses.

Perform a practical activity in Excel to scrub an outlier and rerun descriptive statistics. Compare the before and after results and proceed to the module quiz.

Explore distributions and histograms, standard deviation and relative standard deviation, run charts, and control charts, to improve your data analytics with Excel. Practice data engineering to create or enhance datasets.

Explore standard deviation as a measure of data spread from the mean, and relative standard deviation as context, with Excel calculations and Six Sigma references on claims data.

Learn to use run charts and control charts to spot trends, and apply data engineering to produce a summarized claims data set with volumes by date and average handle time.

Create a control chart in Excel by plotting the mean handle time with upper and lower control limits computed as mean plus three times standard deviation, and assess process stability.

Explore how pivot tables unlock insights by creating a pivot table from a data set, using the pivot builder function, and adding calculated fields to tell a business story.

Create a summary pivot from the claims data by inserting a pivot table at cell B10, placing claim date in rows and into values to count daily claims.

Utilize the pivot builder views and panes in Excel to transform data with columns and row functions, creating grids and tables that isolate performance insights using filters.

Explore calculated fields in pivot tables, including average, max, min, and percentage calculations, then learn sorting and filtering to focus on top ten values and specific data elements.

Build a pivot table in Excel to analyze claims data using calculated fields, show percentage of grand total, and sort claim types from highest to lowest.

Learn to turn pivot-table insights into an analytical story by examining daily claim volume, processing times, and control-chart limits, then ask focused questions about cost, quality, and pay denials.

Create a pivot table from engineered claim data to show cost performance for the month, format as euro, and prepare for the quiz on the January 13, 2018 cost.

Build a pivot table from engineered claim data to show daily paid vs. denied percentages, identify the highest denial and highest paid dates, and compute the overall pay percentage.

Create a pivot table in Excel to show the percentage of denied claims by day of the week from the engineer claim data, then identify Thursday’s denial rate.

Create a pivot table in Excel to compare the average quality of claims by outcome, revealing that denied and paid claims have virtually identical quality, with no significant difference.

create a pivot table to compare average claim costs by outcome (denied vs paid) in euros, then answer quiz questions on cost implications.

Create a pivot table in Excel to analyze the volume of claims by type, then answer questions on the highest and lowest types and those with less than 1300 claims.

Explore how claim type affects paid and denial rates in data analytics with Microsoft Excel, using a pivot table and row-total percentages to identify the highest denial and paid rates.

Create a pivot table from engineered claim data to show average claim-processing cost by claim type in euros. Identify which type is most and least expensive, and the cost difference.

Create a pivot table from the claims engineer data to show paid and denial rates per claims handler, then identify the highest denial rate and how many exceed the average.

Create a pivot table from engineered claim data to compute the average claim quality per individual, format as a percentage, and identify the highest and lowest performers for the quiz.

Create a pivot table from engineer claim data to compare average cost per individual with quality scores, identify the highest and lowest cost handlers, and assess value to the business.

Explore how to build a pivot table in Excel to analyze individual delivery performance by day of the week, compare claims processed per day, and identify top performers on Fridays.

Learn to visualize data by selecting appropriate charts for operational, tactical, and strategic levels, applying color and layout guidelines to reveal trends and distributions.

Visualize data in Excel by producing run charts, trend lines, a pie chart, and stacked bars to analyze quality, cost, and claim types across days and individuals.

Pull a strategic level analysis of delivery, quality, and cost using run charts, pie charts, and narrative to showcase departmental performance.

Add recommendations from analyzed findings to stabilize claims processing by managing holidays and staff, share lessons learned to boost quality, and guide a time quality motion study to optimize cost.