Data Analytics

Harness Excel, SQL, Power BI, and Python Programming to tell compelling stories with data. Build confidence and credibility to power insight-driven strategy on the job.

Applications opened

Course Overview

This is a beginner-friendly course, that will teach you how to use large data sets to make critical decisions. You’ll use industry tools: Excel, SQL, Power BI, and Python programming to analyze large real-world data sets and create dashboards to share your findings.

Program Eligibility


Job Opportunities

Job opportunities you can apply for at the end of the training includes:


Our Three Pillars

Blended Peer Learning

You will be placed in small groups to work together as a team and complete academic goals in real-time virtual classrooms and during in-person sessions; which has goals at the individual and group level.

Mentor-Based Learning

A mentor will be available at all times during office hours to provide help and evaluate your work. We offer you a supportive and engaging work environment, where you can feel free to make mistakes and learn from your experiences.

Projects-Based Learning

Our learning methodology focuses 100% on the needs of today’s market. You will work on real-world projects similar to those you’ll find on the job and complete them using the same tools used by professionals currently in analytics positions.

Our Admission Process

Step 1 → Apply

Submit your application. Share a bit about yourself and what's driving you to start a career in data science.

Step 2 - Admissions Assessment

Complete a short critical thinking and problem-solving assessment. This allows us to assess your aptitude for data.

Step 3 - Admissions Interview

Speak with an Admissions representative in a non-technical interview. This is an opportunity for us to get to know each other a little better. Nothing technical - just a friendly conversation.

Step 4 - Admissions Decision

Receive your acceptance decision from Admissions. This usually happens within 3 business days.

Step 5 → Prework

If accepted, you'll begin course pre-work to prepare for the first day of class. Our data courses pre-work consists of 20-40 hours of lessons and labs covering the basics of Python (including loops and functions), statistical measures such as central tendency and dispersion, and building data visualizations using matplotlib and seaborn.