Data Workflow and Capstone

Data Analytics

Data Workflow and Capstone

Ship analysis end-to-end. Use Git, APIs, and automated pipelines to deliver a capstone that pulls live data, refreshes a dashboard, and ends with a recommendation.

Advanced
12 weeks
Online · Synchronous
Cost TBD

Key details

Track at a Glance

Program: Data Analytics
Track level: Advanced
Cohort length: 12 weeks
Cohort number: 1
Cohort size per section: 25
Number of sections: 1
Cohort schedule: TBD
Live sessions: One 120-minute session per week
Live sessions schedule: TBD
Self-study: About 4–8 hours per week (recommended)
Total time: About 72–120 hours
Format: Online – Synchronous
Orientation: TBD
Credential: Certificate of completion from iVERT
Prerequisite: Tracks 2–4, or equivalent SQL, BI, and Python skills
Program cost: TBD
Registration period: TBD

All students must attend orientation. Otherwise, they will be dropped.

Overview

Real analytics work is repeatable: data arrives on a schedule, gets checked, and lands in a dashboard without anyone copying files by hand. This Track teaches the professional habits that make that possible.

You learn version control, APIs, and simple automation, then combine everything from earlier Tracks into a capstone you can walk an interviewer through.

This Track is for you if you

Have SQL, a BI tool, and Python, and want to combine them
Want a strong, end-to-end portfolio project before job searching
Are curious about analytics engineering as a next step

It is not designed for

Cloud or DevOps engineering
Learners still building SQL or Python basics (take Tracks 2 and 4 first)

By the end of the Track

What You Will Learn

Version-control a project with Git and collaborate through GitHub
Set up clean, reproducible project folders and environments
Pull data from REST APIs safely, without exposing keys
Build a pipeline that extracts, cleans, checks, and loads data
Schedule a pipeline to run automatically and refresh a dashboard
Document a project so someone else can run it
Present a recommendation and defend it under questioning

12 weeks

Track Outline

Learn the fundamentals, build with realistic data, and excel by presenting your work.

Weeks 1–2
Learn
Git and GitHub

Commits, branches, pull requests, and README files; reviewing a partner’s work.
Week 3
Learn
Project structure

Folders, virtual environments, requirements files, and keeping secrets out of repositories.
Weeks 4–5
Build
APIs and data ingestion

Requests, JSON, API keys, pagination, and limits; loading results into a database. Mini-project 1 due in week 5.
Weeks 6–7
Build
Building pipelines

Extract, clean, check, and load; data quality checks, logging, and safe re-runs.
Week 8
Build
Automation

Schedule the pipeline with GitHub Actions or cron, and refresh a dashboard from it. Mini-project 2 due in week 8.
Weeks 9–11
Excel
Capstone build

Propose, build, and review an end-to-end project in short sprints with feedback.
Week 12
Excel
Presentations and career launch

Capstone presentations, portfolio and resume review, and a mock interview.

TBD Successful candidates Closing and certification

How you are graded

Assessment and Certificate

Component	Weight	What it covers
Git and API labs	15%	Hands-on checks, with feedback
Mini-projects (2)	25%	Written comments and one resubmission on each
Capstone	50%	Pipeline, dashboard, memo, and presentation
Mock interview	10%	SQL questions and a behavioral round, with written feedback

Certificate of completion: awarded to learners who score 70% or higher with an acceptable capstone. You get written comments on projects, an answer key for practice sets, and one resubmission on each mini-project.

What you will build

Choose one business question and deliver it end-to-end: pull data from an API, load it into a database, refresh a dashboard, and present a written recommendation. Everything lives in a documented GitHub repository you can share with employers.

Tools and Materials

All student materials are free.

Version control

Git and GitHub (free)

Code

VS Code and Python, with the requests library (free)

Data

PostgreSQL and public APIs, with a saved fallback dataset

Automation

GitHub Actions or a local scheduler (free)

Learning platform

iVERT’s E-Learning site for materials, quizzes, submissions, and recordings

Community channel

Discord or Slack for quick questions and peer help

Prerequisites and Enrollment Requirements

Required

Tracks 2–4, or working SQL, a BI tool, and Python with pandas
A computer where you can install software, and reliable internet access
Time for a demanding capstone in the final four weeks

Helpful, not required

Track 5 (statistics)
Comfort using a command line

Not required

Software engineering experience, cloud accounts, or paid software.

After this Track

Career Fit and What Comes Next

Skills you will leave with

Git and GitHub
REST APIs
Data pipelines
Data quality checks
Automation
Documentation
Stakeholder presentations

Where these skills apply

Data analyst (all-rounder)
Product analyst
BI developer (junior)
Analytics engineer (stretch)
Data or M&E specialist

The capstone is your main interview talking point: a complete, working project.

MORE TRACKS

Explore the Data Analytics Tracks

See every Track in this Program and choose your next step.

Finish here with a certificate and a portfolio project, or continue. Either is a good outcome.

Browse all Tracks

How to Apply

Check that you meet the prerequisites above.
Take note of the Program, Track, and Cohort number (needed to apply).
Use the button below to apply, or to register your interest if the Track has a waitlist.
Watch your email for confirmation and onboarding details.

Applications are not open for this Track right now. See which Tracks are open, or request information and we will let you know when the next Cohort opens.

Track FAQs: Find Quick Answers

How hard is this Track?

It is the most demanding Track: it combines earlier skills and adds new tools. The last four weeks focus on a single capstone with regular feedback.

Can I take it without Track 5?

Yes, if you have SQL, a BI tool, and Python. Your capstone should still include one checked finding, which Track 5 makes easier.

What if the API I choose stops working?

Every capstone keeps a saved copy of the data, so a broken API never stops your project.

What if I miss a session?

Sessions are recorded and posted on the learning platform, and you can ask questions at office hours.

How much time will I need each week?

About 6–10 hours: one 120-minute live session plus 4–8 hours of self-study.

Will this get me a job?

iVERT does not place graduates or guarantee employment. iVERT is not an accredited college; Programs do not grant degrees or academic credit. However, you will leave with job-relevant skills and a portfolio project to show employers.

iVERT does not place graduates or guarantee employment. iVERT is not an accredited college; Programs do not grant degrees or academic credit.