Virtual Self-Paced Class - Structured Data Analysis for Investigators - Excel & SQLite in Practice
A virtual self-paced class ($450) that a student can complete by watching a pre-recorded 2 day virtual class while completing hands-on practical’s.
Access to virtual class material and recordings for up to 60 days or until completion of class (whichever occurs first).
This class is open to any student with no prerequisites.
This self-paced virtual class (up to 60 day access) is designed for Investigators, Analysts, and Digital Evidence Professionals who regularly handle structured data from mobile extractions, app exports, call detail records (CDRs), geolocation logs, and SQLite databases.
This hands-on course equips participants with the skills to transform structured datasets into actionable investigative intelligence.
On Day 1, students learn practical techniques using Microsoft Excel to filter, sort, pivot, and visualize large datasets to identify patterns and build investigative timelines. On Day 2, students work with SQLite databases extracted from mobile applications or system artifacts, learning to navigate, query, and link information to broader case data.
Topics Covered:
· Understanding structured data formats used in investigations
· Importing and preparing datasets in Excel for analysis
· Filtering, conditional formatting, and formulas for identifying patterns
· Creating pivot tables and investigative timelines
· Visualizing data for investigative reports and presentations
· Understanding SQLite database structure and investigative value
· Using SQLite viewer tools and running basic queries
· Joining tables and correlating app activity with other evidence
· Exporting SQLite results and integrating them with Excel analysis
By the end of the course, participants will confidently process and correlate digital exports to uncover leads and produce court-ready reports.
Students who complete this course will receive a training certificate along with a 10% discount off 1 future in-person or virtual Hi-Tech class (limit 1 per paid student).
Click the Learn More button to create your Hi-Tech online account and then to pay for the class via credit card. Make sure you pay for the class using the student’s Hi-Tech online login.
Email training@hi-techinvestigations.com to pay via check and to also inquire about a discount for multiple students ($75 off the 2nd purchase and additional purchases per agency).
-
-
Part 1 - Introduction to Data Analysis with SQLite
This opening section introduces the course structure and materials, then lays the foundation for working with structured data in SQLite. Students are introduced to the differences between structured and unstructured data and begin exploring SQLite databases, including tables, columns, keys, and common data types.
This opening section introduces the course structure and materials, then lays the foundation for working with structured data in SQLite. Students are introduced to the differences between structured and unstructured data and begin exploring SQLite databases, including tables, columns, keys, and common data types.
-
Part 2 - FQLite and DB Browser
Building on the introductory database concepts, this section continues exploring SQLite structure while introducing two key forensic database tools, FQLite and DB Browser. Students compare tool capabilities and gain insight into when and how each can be used during analysis.
Building on the introductory database concepts, this section continues exploring SQLite structure while introducing two key forensic database tools, FQLite and DB Browser. Students compare tool capabilities and gain insight into when and how each can be used during analysis.
-
Part 3 - FQLite & Foundational SQL concepts
This section introduces students to viewing and interpreting SQLite WAL files in FQLite, followed by presentation-based instruction on foundational SQL concepts. Topics include writing queries, filtering and sorting data, using aliases and JOINs, handling null values, working with timestamps, and applying aggregate functions for analysis.
This section introduces students to viewing and interpreting SQLite WAL files in FQLite, followed by presentation-based instruction on foundational SQL concepts. Topics include writing queries, filtering and sorting data, using aliases and JOINs, handling null values, working with timestamps, and applying aggregate functions for analysis.
-
Part 4 - Practical Exercises with SQL
In this hands-on section, students apply SQL concepts through practical exercises and mobile artifact query examples. Participants work directly with query construction, joins, conditional logic, date and time conversion, and grouping functions to reinforce core techniques used in forensic analysis.
In this hands-on section, students apply SQL concepts through practical exercises and mobile artifact query examples. Participants work directly with query construction, joins, conditional logic, date and time conversion, and grouping functions to reinforce core techniques used in forensic analysis.
-
Part 5 - Intermediate & Advanced SQL Techniques
This section continues hands-on query development and moves into more intermediate and advanced SQL techniques. Students strengthen their skills through increasingly complex queries designed to support deeper data analysis and investigative problem-solving.
This section continues hands-on query development and moves into more intermediate and advanced SQL techniques. Students strengthen their skills through increasingly complex queries designed to support deeper data analysis and investigative problem-solving.
-
-
-
Part 6 - Introduction To Excel
Day 2 begins with an introduction to using Excel as an analysis tool for investigative data. Presentation topics cover Excel navigation essentials, data cleaning functions, understanding how Excel stores dates and times, and using conditional formatting to identify patterns and highlight relevant information.
Day 2 begins with an introduction to using Excel as an analysis tool for investigative data. Presentation topics cover Excel navigation essentials, data cleaning functions, understanding how Excel stores dates and times, and using conditional formatting to identify patterns and highlight relevant information.
-
Part 7 - Excel Practicals
This hands-on section gives students practical experience applying Excel techniques introduced earlier. Participants work through exercises involving data cleanup, date and time handling, formatting, and visual highlighting techniques to prepare data for analysis.
This hands-on section gives students practical experience applying Excel techniques introduced earlier. Participants work through exercises involving data cleanup, date and time handling, formatting, and visual highlighting techniques to prepare data for analysis.
-
Part 8 - Formulas & Pivot Tables in Excel
This section focuses on analytical techniques in Excel using formulas and Pivot Tables. Students learn to apply logical and summary functions, perform lookups across datasets, and build PivotTables to summarize and explore data by key investigative categories.
This section focuses on analytical techniques in Excel using formulas and Pivot Tables. Students learn to apply logical and summary functions, perform lookups across datasets, and build PivotTables to summarize and explore data by key investigative categories.
-
Part 9 - Finale With Charting Techniques & Capstone Exercise
The final section introduces basic charting techniques to visualize findings from PivotTables and concludes with a capstone exercise. Students apply skills developed throughout the course to analyze and interpret a SQLite export in a practical, self-guided exercise.
The final section introduces basic charting techniques to visualize findings from PivotTables and concludes with a capstone exercise. Students apply skills developed throughout the course to analyze and interpret a SQLite export in a practical, self-guided exercise.
-