Mammoth Club All levels 10 sections 34 lectures

Conversational CSV Analysis using Kimi 2.7 Code with Python and Ollama

Start turning your CSVs into an actual conversation.

01
Skill level
All levels
02
Sections
10
03
Lectures
34
04
Instructor
James Dabalus
What's inside

This course includes.

10
Sections
34
Lectures
31
Resources
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

10 sections · 34 lectures
+ 01 Foundations of Conversational Data Analysis 3 lectures
01 Defining Conversational Data Analysis Locked
02 Why This Stack — Kimi K2.7 Code, Ollama, and Python Locked
03 Where This Fits Among BI Tools and SQL Bots Locked
+ 02 Inside Kimi K2.7 Code 5 lectures
01 Model Lineage Locked
02 Mixture-of-Experts Architecture Locked
03 Context Window and Multimodal Input Locked
04 Coding-Focused Agentic Behavior Locked
05 Reading the Benchmark Tables Locked
+ 03 Ollama as the Local Runtime 3 lectures
01 How Ollama Serves Models Locked
02 Running a Trillion-Parameter Model Through the Cloud Locked
03 Local Alternatives and Model Tags Locked
+ 04 Python as the Orchestration Layer 5 lectures
01 Talking to Ollama from Python Locked
02 Structuring the System Prompt Locked
03 Carrying Conversation and Reasoning Forward Locked
04 Free-Form Code Generation as an Integration Pattern Locked
05 Tool Calling as an Integration Pattern Locked
+ 05 Designing the CSV Analysis Agent 3 lectures
01 Describing a CSV to a Model Locked
02 Handling Real-World, Messy Data Locked
03 Comparing Agent Design Patterns Locked
+ 06 Safety, Sandboxing, and Reliability 3 lectures
01 The Risk Surface of Generated Code Locked
02 Sandboxing and Validation Locked
03 Self-Correction and Auditability Locked
+ 07 Conversation Memory and Multi-Turn Analysis 1 lecture
01 Holding a Long Analysis Conversation Together Locked
+ 08 Visualization and Communicating Results 3 lectures
01 Choosing an Appropriate Chart Type Locked
02 Generating and Executing Visualization Code Locked
03 Narrating Results in Plain Language Locked
+ 09 Evaluating and Comparing the Stack 5 lectures
01 What the Benchmark Tables Measure Locked
02 This Stack vs. a Pure Hosted API Locked
03 This Stack vs. PandasAI and LangChain Agents Locked
04 Known Limitations Locked
05 Judging Fit for Your Own Use Case Locked
+ 10 Practical Considerations and What's Next 3 lectures
01 Deployment Shapes Locked
02 Extending Beyond a Single CSV Locked
03 Responsible Use and Data Privacy Locked
Description

About this course.

Asking your data questions in plain language works differently than typing another SQL query or building a dashboard.

This video course builds that conversational loop using Kimi K2.7 Code, Ollama, and Python, from the reasoning behind the stack to a working analysis agent.

► Understand why this stack beats dashboards, fixed scripts, and SQL bots.

► Explore Kimi K2.7 Code's architecture, context window, and agentic coding behavior.

► Design system prompts, code-generation, and tool-calling patterns for analyzing CSVs conversationally.

► Cover safety, sandboxing, multi-turn memory, visualization, and how the stack compares to alternatives.

It's as much about knowing the tradeoffs as it is about getting the model to answer.

✅ Lifetime access to all modules.

⚠️ Requires an Ollama Cloud sign-in or API key.

Instructors

Taught by people who ship.

James Dabalus

James Dabalus

Instructor

James is a versatile IT Technician specializing in Prompt Engineering, Generative AI, Graphic Design, Web Development, Video Editing, and E-learning. With a passion for automation, he continually seeks innovative ways to streamline digital workflows.

Ready to start building?

Start turning your CSVs into an actual conversation.

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