Data Analytics & Visualization: Using Excel and Python

Explore how to use Excel's statistical functions—count, counta, countblank, countif, average, median, mode, standard deviation, and correlation—to analyze a dataset of quantity, discount, and revenue and make informed business decisions.

Learn Excel's mathematical functions—sum, sum if, average, count, round, rand, mod, int, and abs—and apply them to expenses, sales, scores, and survey data to gain insights.

Learn to use vlookup and hlookup in Excel to search tables and retrieve data with exact matches across sales, payroll, student grades, and marketing ROI.

Explore the index and match functions as flexible, two-way and multi-criteria lookups in Excel, moving beyond vlookup to retrieve precise values from any row or column.

Create pivot tables in Excel to summarize data with row labels, column labels, filters, and values, and use pivot charts for regional and product insights.

Learn to visualize pivot table data with pivot charts in Excel, choosing chart types, customizing visuals, and using interactivity to drill down, filter, and update charts in real time.

Explore how excel logical functions automate decisions and insights, using if, if error, countif, sumif, and averageif to analyze profits and conditions.

Master conditional formatting in Excel by using the if and iferror functions to format cells based on logical tests, such as profit versus shipping costs.

Explore text functions in Excel such as upper, lower, proper, left, right, search, trim, concat, and Len to manipulate and extract information from text strings.

Apply conditional formatting in Excel to highlight scores and use text functions to format names starting with A, using greater than 90 in green and less than 70 in red.

Explore date and time functions in Excel to create dates, extract year, month, day, and time components, and use datedif, eomonth, networkdays, and text for formatting.

Master the basics of data cleaning in Excel, including removing duplicates, handling missing values, standardizing formats, and using text to columns to ensure data quality.

Explore feature engineering in Excel by cleaning data, extracting and transforming features, encoding categories, and selecting key variables to improve predictive models, with practical Excel examples.

Learn how Power Query in Excel acts as a data transformation and preparation tool, cleaning data, capitalizing names, removing duplicates, replacing values, and loading results into Excel for analysis.

Learn how to use Excel's scenario manager to compare budget, moderate, and luxury travel costs with a what-if analysis and a summary table.

Explore Excel's goal seek analysis within what-if analysis to determine the input needed to reach a desired outcome, using scenarios like loan payoff, sales targets, or optimal pricing.

Explore data table analysis in Excel to automate revenue calculations by varying price and quantity, using row and column input cells in one-way and two-way tables.

Explore Excel solver package to optimize by adjusting decision variables and defining an objective function. Set constraints, run what-if analyses, and compare outcomes using NPV, IRR, and payback in Excel.

Learn essential data visualization best practices in Excel, including choosing chart types, using color wisely, and building interactive dashboards with slicers and pivot charts to reveal trends and insights.

Explore Excel chart types—pie, column, bar, and line—and build pivot tables and dashboards with slicers to visualize total sales per region, profit by region, and trends by subcategory.

Create a data dashboard in Excel by arranging line charts of total sales by subcategory, pie charts of sales by product category, and formatted visuals for clarity.

Learn how to use linear regression in Excel to model relationships between variables, such as house size and sale price, and interpret the regression line, slope, intercept, and scatter plot.

Explore preliminary forecasting analysis in Excel, learning moving averages, exponential smoothing, and linear regression through real-world sales, finance, and supply chain examples.

Explore real-world Python use cases across web development, data science, automation, scientific computing, IoT, and game development, and learn why its simplicity and versatility power beginner-friendly careers.

Learn how Python variables store values, using examples a, b, and c, print results, and master naming rules: letters, digits, underscores; no starting with numbers; case sensitive.

Learn Python data types—integers, floats, and strings—and how Python infers types automatically, and how to use the type and print functions along with type casting with int, float, and str.

Explore scope of variables in Python, differentiate local and global scope, and learn how to use the global keyword to modify global variables.

Explore Python operators, including seven types: arithmetic, assignment, comparison, and logical, with examples of addition, subtraction, multiplication, division, modulus, exponent, and floor division.

Learn how to use lists and tuples in Python, including creating and indexing lists, mutating via append and other methods, and performing concatenation, repetition, and slicing with tuples.

Explore Python sets and dictionaries, learning how to create, modify, and delete elements while using the in operator, and mastering add, update, remove, and nested structures.

Learn stacks and queues as foundational data structures, using push and pop for stacks (lifo) and in-queue operations for queues (fifo), implemented with lists and the DK library in Python.

Explore time and space complexity in Python, learning how big O notation characterizes constant, linear, and quadratic complexities, with examples like accessing first element, loops, and matrix generation.

Explore bubble sort, selection sort, and insertion sort, learning how they compare and swap adjacent elements in an array, and why bubble sort is inefficient for large data.

Explore searching algorithms in Python, including linear and binary search on sorted data, how they locate elements, validate data, and return the index or minus one.