Data Analyst Bootcamp: Excel, SQL & Python (2026)

Master Microsoft Excel in a hands-on 12-hour journey from fundamentals to dashboards, mastering the interface, data entry, and formulas. Build real-world skills with pivot tables, data cleaning, and interactive dashboards.

Build your first Excel workbook by creating a student marksheet with headings, data entry, bold headers, borders, and a total using the sum function and fill handle.

Boost your Excel data productivity by mastering autofill, flash fill, and paste special, then apply formatting, sorting, filtering, and transpose in hands-on demonstrations.

Master professional formatting in Excel by applying readable fonts, colors, borders, and currency formats; and using layout tools like resize, auto-fit, hide/unhide, and grouping for clear dashboards and reports.

Sort and filter data in Excel to quickly analyze large tables, using multi-level sorting, filters by values, text, date, or color, and conditional formatting on an attendance sheet.

Unlock Excel power with formulas, functions, and cell referencing to calculate, summarize, and analyze data; master arithmetic operations, sum, average, and round, and apply to a monthly budget tracker.

Explore Excel's built-in functions, including sum, average, min, max, and round, for dynamic calculations on a range. Apply them to expense data to compute totals, averages, extremes, and rounded values.

Master relative, absolute, and mixed cell referencing in Excel, using dollar signs to fix cells. Learn to drag formulas across a grid to apply fixed rates like GST.

Master ranges and named ranges in Excel to clean formulas, perform sum and average across data, and build a monthly budget tracker.

Master Excel arithmetic operators and formulas by using cell references, practicing addition, subtraction, multiplication, division, and percentage calculations, with examples of errors, decimals, and the BODMASK rule.

Master advanced Excel functions, including ifs and or, countif, sumif, averageif, text functions like left, right, mid, concat, textjoin, and date functions, through a bonus calculator and leap tracker.

Explore text functions in Excel to extract, combine, and clean data using left, right, mid, find, length, concat, and text join to derive first names, last names, and lowercase emails.

Master nested if in excel to evaluate multiple outcomes with layered, left-to-right logic. Use count if, sum if, and average if for hr analytics like attendance eligibility and salary totals.

Master data cleaning and validation in Excel by applying text cleanup functions, removing duplicates, splitting data into columns, and enabling drop-down lists with input messages.

Learn how to split messy data in Excel using text-to-column, handle delimited and fixed-width formats, and enforce clean data with data validation, drop-down lists, and error alerts.

Boost Excel efficiency with freeze panes, split screen, and protect sheet to keep headers visible. Apply text to column, remove duplicates and data validation to prepare clean data.

Master Excel lookups with VLOOKUP, HLOOKUP, XLOOKUP, INDEXMATCH, and INDIRECT to pull exact data and automate reporting. Build clean data with validation for dynamic, accurate insights.

XLOOKUP replaces VLOOKUP and HLOOKUP by providing a single formula for vertical and horizontal lookups, with built-in not found handling and bidirectional search.

Master index and match for flexible two-way lookups in Excel, surpassing vlookup for large data and multi-criteria searches. Explore dynamic lookups using name ranges, indirect, and offset with salary demos.

Create an employee attendance sheet, applying borders, header formatting, time with seconds, and sort, filter, and conditional formatting to highlight absences for 10 employees over five days, including email addresses.

Build an automated monthly budget tracker in Excel by naming category ranges and using sum, average, min, max, and absolute references to track daily spend and running total.

Build practical Excel tools with XLOOKUP and index-match to create an inventory lookup and a salary master, enhanced by IFERROR for user-friendly results.

Learn to turn raw Excel data into visual stories by selecting and customizing chart types—column, bar, line, area, and pie—to reveal trends and insights through a monthly dashboard.

Choose the chart that matches your data story: column or bar for comparison, line or area for trends, and pie or donut for proportions; use scatter for relationships.

Learn to compare metrics with different scales using a combo chart with a secondary axis, then use pathlines and sparklines for compact trend insights in dashboards.

Build an interactive monthly sales dashboard in Excel that combines a line chart, region-wise column chart, category share pie chart, and product sparklines to tell a complete story.

Master pivot tables in Excel to instantly summarize large datasets, explore filtering, sorting, and calculated fields, and visualize insights with pivot charts, slicers, and timelines.

Explore the four pivot table areas: row field, column field, value field, and grouping, along with filtering and sorting to analyze HR and sales data, including month and quarter grouping.

Explore advanced Excel features to automate and create interactive reports, including conditional formatting, dynamic name ranges, forecast analysis, and form controls, building dashboards and KPI trackers with forecasting.

Plan, clean, and set up a complete Excel dashboard by integrating formulas, lookups, pivot tables, charts, and branding to turn raw sales data into a clear, CEO-ready performance view.

Clean and structure data by removing duplicates, formatting dates and currency, and adding a helper column, then build summary tables with sumif and vlookup to support KPI dashboards and charts.

Plan a dashboard layout that guides the eye with key performance indicators at the top, charts in the middle, and tables at the bottom, with interactivity like slicers.

Create an interactive dashboard by adding slicers, dropdowns, and check boxes linked to pivot tables and charts; apply final design touches and export the dashboard as a pdf.

Turn raw data into a dashboard powerhouse by building summary and pivot tables for total sales by region and product, plus monthly trends, all controlled by slicers.

Connect all slicers to the charts and summary tables to create a fully interactive dashboard where region, monthly, and product filters update in real time.

Develop a portfolio-ready Excel dashboard by cleaning messy sales and HR data, calculating metrics such as net sales, attendance percentage, and profit margin, and building pivot charts with slicers.

Learn essential data cleaning steps to prepare reliable datasets, including removing duplicates, standardizing dates, trimming text, handling missing values, and creating time-based helper columns.

Transform raw data into key performance indicators with formulas that compute net sales after discounts, profit margins, attendance, tenure, and bonus eligibility, using relative and absolute references and pivot tables.

Transform raw data into strategic insights with Excel pivot tables, revealing regional sales by month, product category performance, top profit drivers, HR salary averages, and performance distributions, plus charts.

Transform pivot table data into five key charts—line, bar, pie, column, and combo—to reveal monthly trends, regional contributions, top products, HR staff distribution, and sales versus profit.

Visualize top 5 salespeople by profit with a pivot table pie chart and data labels in percentage. Compare department salaries with a column chart and assess performance distribution for HR.

Transform static analysis into an interactive executive dashboard by arranging charts, adding slicers, KPI cards, and professional formatting for real-time filtering and storytelling.

Build an interactive Excel dashboard for sales and HR analytics using slicers, pivot tables, and KPI cards. Show end-to-end analytics from raw data to insights and create a portfolio-ready story.

Discover how artificial intelligence turns Excel into an assistant with a co-pilot and the analyze data feature, enabling natural-language prompts to generate formulas, pivot tables, charts, and insights.

Automate excel tasks with ai copilot to compute total revenue by region, average revenue by product, flag high discounts, and identify top three transactions.

Use prompts to detect outliers and summarize revenue by month in Excel with AI-assisted formulas and pivot tables. Learn how AI amplifies Excel skills for dashboards and data insights.

Learn the basics of sql by examining data definition language and its five core statements—create, alter, drop, truncate, and rename—and how they define and modify database objects.

Master data manipulation language basics (dml) by learning insert, update, and delete operations, distinguishing them from truncate, and understanding how to target specific rows in a table.

Learn how transaction control language drives SQL queries in the database, with commit to save changes, rollback to undo, and save point to record intermediate steps.

define data access using data control language, and use grant and revoke statements to assign or remove permissions in a database.

Explore data retrieval language (DRL) in SQL, focusing on the select statement to view and retrieve data from the database.

Learn DRL data retrieval language with SQL select statements, from selecting all columns to specific columns, applying where conditions, null checks, like operators, order by, and subqueries.

Learn how to manipulate database data with DML, covering insert, update, and delete statements to modify and remove records.

This lecture outlines the five sql insert types—values, prompt for input, selected columns, insert with select, and insert with select by column list—and notes not null constraints.

Demonstrates inserting only values using insert into a table, covering data types (number, varchar, varchar2, char), quoted strings, nullability, and basic constraint handling.

Learn how to insert records using type 2 prompts with ampersand substitutions, guiding entry of EXMID, EXM name, EXM code, EXM state, and EXM fee in a table.

Master how to insert specific columns with values using insert into, including handling duplicates and using insert with select to copy data between tables like exam1 and exam2.

Explore inbuilt SQL functions and how they make statements more efficient. Identify the four types: character or string, math or numeric, date or time, and conversion functions.

Explore seven string functions, including concat, init cap, instr, substring, length, lower, upper, and trim, with examples that show how to combine, capitalize, locate, extract, measure, and trim text.

Explore string functions in Oracle SQL, including concat, init cap, and instr, with live queries on the dual table to concatenate words, capitalize text, and locate character positions.

Master substring operations by selecting start positions and lengths, including negative indexing, and apply length, lower, upper, and trim to manipulate spaces and case in strings.

Explains four numeric functions—count, rownum, sum, and trunk (truncate)—and demonstrates counting records, assigning row numbers, summing selected rows, and truncating numeric values.

Explore numeric functions in Oracle live sql. Count records in hr countries, find the highest salary in hr employees, use row numbers, sum salaries, and apply distinct to remove duplicates.

Explore the five types of date and time functions: system date, system timestamp, current date, current timestamp, and last day, and learn how each displays the correct date or time.

Explore Oracle date and time functions, including sysdate, system_timestamp, current_date, and last_day, and learn how server time, time zone, and microseconds affect dates.

Explore how joins combine data from two or more tables, using inner, left outer, right outer, and full outer joins to retrieve matching data and all data.

Learn to perform inner, left, right, and full outer joins on employees and departments, combining first names with department names via primary and foreign keys.

Explore Python, a beginner-friendly high-level language known for readability and use in web development, AI, and machine learning. This module covers Python from beginner to advanced concepts.

Learn to install Python and VS Code, create and activate a virtual environment, and write and run your first Python program using a basic hello world example.

Learn Python keywords, operators, and typecasting, including arithmetic, comparison, logical, assignment, and membership operators, and understand how to convert values between integer, float, and string types.

Learn how variables store values, explore Python data types such as int, float, string, and bool, and apply rules for valid variable names, keywords, comments, and indentation.

Learn how to take user input in Python, capture a name and age, convert input to integers, and compute sums.

Explore Python control flow by using if, else, and alif, and loop with for and while; master break, continue, and pass to build smarter, dynamic programs.

Define and call Python functions using def, pass parameters, and return values. Understand local and global scope and use lambda expressions for simple tasks.

Learn how Python uses the __name__ == '__main__' convention to run code only when a file is executed directly, demonstrated with a greet function and module import example.

Explore Python lists, learn to create, access by index, modify elements, and use negative indexing for reverse order. Practice append, insert, delete, pop, sort, and slicing to manipulate lists.

Explore Python tuples, their immutability, and how they differ from lists, including creation, indexing, slicing, length, packing and unpacking, nesting, and count and index operations.

Learn how to write and modify Python dictionaries, store data with key-value pairs, and nest lists and dictionaries using curly braces with practical examples like color and points.

Learn Python sets to store unique elements, create with curly braces or a set constructor, and use add, update, remove, discard, pop, union, intersection, difference, and membership testing with in.

Build a command line calculator by defining functions for add, subtract, multiply, and divide, handling user input and zero division, and printing the result.

Master Python file handling by reading and writing files safely, using open modes, handling append vs write, reading line by line or by character, and leveraging with statements to auto-close.

Learn how to read and write csv files in Python using the csv module, including dict writer and dict reader, handling headers, and avoiding extra newlines.

Learn to read and write JSON data in Python using the json module, including dump, dumps, load, and loads methods, and practice converting between Python objects and JSON.

master error handling in python by using try and except, raising custom exceptions, and applying else and finally to gracefully manage errors such as zero division and file not found.

Learn Python object-oriented basics by creating classes and objects with constructors, attributes, and methods, illustrated by student and bank classes with deposit and withdraw.

Explore instance, class, and static methods in Python object-oriented programming, including how instance methods affect objects, class methods for class-level operations, and static methods for validation and utilities.

Master encapsulation, abstraction, inheritance, and polymorphism in Python by exploring how classes and objects model real-world concepts, control access, and reuse code.

Discover how to install and import external Python modules, compare built-in vs external modules, and use examples like math, random, and requests to access APIs.

Learn how APIs in Python connect applications by using web and library APIs to fetch data via endpoints, requests, and JSON responses, with GitHub and weather API examples.

Build a weather app using the open weather API by learning how to obtain an endpoint, generate a personal API key, and fetch city weather data with Python.

Explore how Flask lets you build web apps with Python, run a local server, and view a hello flask page in your browser.

Master the basics of machine learning with Python, exploring foundational concepts and introductory modeling techniques within the data analyst bootcamp.

Create a Python game using pygame, a color match where the top color changes every second and you press the space bar when it matches the bottom color to score.

Develop a major project: an expense tracker using Excel, SQL, and Python. Build and showcase a practical data solution for managing expenses.

Learn to use pandas to create, clean, and analyze structured data from csv, excel, and sql sources. Apply concepts to a sales report analyzer mini project.

Master data analysis with pandas in the second module, applying pandas techniques to organize, analyze, and derive insights from datasets.

Please provide the lecture caption to generate a 1–2 sentence summary for the course landing page.

Build a data analysis project using pandas and matplotlib to visualize data and derive insights.

learn web scraping to automatically extract data from websites using requests to fetch pages and beautiful soap to parse html, enabling news headlines and price data collection.

Develop a Python CRUD app with SQLite to perform create, read, update, and delete operations as part of the data analyst bootcamp.

Develop a chatbot as a major project by applying Excel, SQL, and Python skills from the data analyst bootcamp.

Learn to visualize profits and sales by quarter using cluster bar and stack bar charts. Build pivot tables and refine charts with data labels, currency formatting, and clear legends.

Explore map charts to visualize data geographically, using Excel, SQL, and Python as core tools in the data analyst bootcamp.

Learn how to create a histogram in Excel, including when pivot tables cannot render it, copying data outside the pivot, and adjusting bin width and age distribution.

Explore how to create and interpret a scatter plot chart to reveal relationships in data. Apply techniques within the Data Analyst Bootcamp, leveraging Excel, SQL, and Python to analyze data.

Learn how data validation in Excel ensures data quality by enforcing criteria like lists, whole numbers, dates, times, and text length, with input messages and error alerts.

Explore macros and VBA to automate repetitive Excel tasks, from recording macros to editing the code behind them. Create charts and format data across monthly datasets with single-click automation.