Turn Your STEM Degree Into High-Paying Tech Skills. Build Software That Solves Real Scientific Problems.
Bridge the gap between science and technology in 18 weeks. Join the elite group of engineer-scientists building computational tools for research, healthcare, and innovation, earning ₦500,000-₦1.2M monthly while advancing human knowledge.
₦500,000
Also available: 4 monthly payments of ₦130,000
Money-back guarantee • No hidden fees • Secure payment
₦350k–₦800k
Earning Potential
5 Real Projects
Portfolio-Ready
Alumni Network
Flutterwave, Andela
Career Support
Lifetime Access
Choose Your Cohort
Select the learning schedule that best fits your availability. All cohorts receive the same industry-leading curriculum and career support.
Scientific Software Engineering • SSE-C2
⭐ 4.9 • 1,247 enrolled
24 Weeks 
Starts 27-Jul-2026
₦500,000
ⓘ Early bird pricing ends soon
Scientific Software Engineering • SSE-C3
⭐ 4.9 • 1,247 enrolled
24 Weeks Starts 18-Jan-2027
₦500,000
ⓘ Early bird pricing ends soon
Who This Bootcamp Is For
This program is designed for driven individuals ready to invest in their future
Recent Graduates
STEM or non-STEM graduates struggling to find jobs that match their potential
Underemployed Professionals
Working in roles below your capability and ready for a tech career upgrade
Career Switchers
Professionals from banking, engineering, or other fields seeking tech transition
Parents & Sponsors
Investing in your child's or mentee's future with a high-ROI skill
Requirements Checklist
Basic computer literacy (can use Excel, browse the web)
Access to a laptop and stable internet connection
20-25 hours per week to dedicate to learning
Growth mindset and willingness to be challenged
No prior programming experience required
Requirements Checklist
You're looking for 'get rich quick' shortcuts
You can't commit 20+ hours weekly for 16 weeks
You're not willing to struggle through difficult problems
You expect to learn without putting in the work
You're not ready to invest in your own growth
18-Week Curriculum Overview
In 18 weeks, you’ll master the intersection of scientific computing and software engineering—the skills that make you irreplaceable.
Phase 1
Programming Fundamentals for Scientists
Weeks 1-5
Module 1: Python for Scientific Computing
Python fundamentals: The language of choice for 90% of scientific computing
NumPy: Numerical computing and array operations (the foundation of all scientific code)
SciPy: Advanced mathematical functions, optimization, and scientific algorithms
Pandas: Data manipulation and analysis for research datasets
Matplotlib & Seaborn: Scientific visualization and publication-quality plots
Module 2: Mathematical Computing & Algorithms
Numerical methods: Solving differential equations, integration, differentiation
Linear algebra implementations: Matrix operations critical for simulations
Statistics & probability: Hypothesis testing, distributions, Bayesian methods
Algorithm complexity: Writing efficient code for large-scale computations
Version control with Git: Collaborate like research software engineers do
PROJECT
Build a data analysis pipeline that processes real experimental data (physics, chemistry, or biology dataset of your choice)
PROJECT 2
Implement a Monte Carlo simulation for a scientific problem (e.g., particle physics, molecular dynamics, or epidemiology)
Phase 2
Scientific Software Development
Weeks 6-11
Module 3: Object-Oriented Programming for Science
OOP principles: Design reusable, modular scientific code
Software design patterns: How professional research software is structured
Unit testing & validation: Ensure your scientific computations are correct
Documentation: Write code that other scientists can use and understand
API design: Create tools that other researchers can integrate into their work
Module 4: High-Performance Computing & Optimization
Performance optimization: Make your code 10-100x faster
Parallel computing: Leverage multiple processors for large simulations
GPU programming basics: Use graphics cards for scientific computation
Memory management: Handle large datasets efficiently
Profiling & debugging: Find and fix performance bottlenecks
PROJECT 3
Build a scientific Python package with full documentation (like NumPy or SciPy, but specialized for your field)
PROJECT 4
Optimize a computational model to run in minutes instead of hours
Phase 3
Specialized Scientific Applications
Weeks 12-14
Module 5A: Computational Biology & Bioinformatics (Choose One Specialization)
DNA/RNA sequence analysis and genomics
Protein structure prediction and molecular modeling
Systems biology and network analysis
Biostatistics and clinical trial data analysis
Module 5B: Physics & Engineering Simulations (Alternative Specialization)
Finite element analysis for engineering problems
Computational fluid dynamics (CFD)
Particle simulations and molecular dynamics
Signal processing and control systems
Module 5C: Data Science & Machine Learning for Scientists (Alternative Specialization)
Machine learning fundamentals for scientific applications
Feature engineering from scientific data
Model validation and scientific ML best practices
Deep learning for image analysis (microscopy, medical imaging, satellite data)
PROJECT 5
Build a specialized tool in your chosen domain
Phase 4
Production-Grade Scientific Software
Weeks 15-18
Module 6: Web Interfaces for Scientific Tools
Flask/Django: Create web interfaces for your scientific models
REST APIs: Let others access your computational tools programmatically
Data visualization dashboards: Interactive scientific visualizations
Deployment: Put your tools online for researchers worldwide to use
Module 7: Scientific Cloud Computing
AWS/Azure for scientific computing: Run simulations in the cloud
Docker containers: Package scientific software for reproducibility
Workflow management: Automate complex computational pipelines
Databases for scientific data: Store and query large research datasets
CAPSTONE PROJECT (Weeks 15-18): Your Research Software Portfolio
Build a complete scientific software system that demonstrates your specialized expertise. Examples: a bioinformatics analysis pipeline, a physics simulation toolkit, a climate data visualization platform, or a drug discovery computational tool. This project will be production-grade—actually usable by real researchers—and will be the centerpiece of your portfolio when applying for scientific software positions.
Key Technologies
Python
SQL
TensorFlow
Scikit-learn
Git
AWS
Learn from Industry Experts
Get trained by professionals who’ve built real data science solutions for top
African companies
Olumide Shobanke
Lead Data Science Instructor
Former Senior Data Scientist at Flutterwave with 8+ years experience. Built ML systems processing millions of transactions daily. Passionate about training the next generation of African data scientists.
Aisha Aliyu
Lead Data Science Instructor
Tech recruiter turned career coach with 200+ successful placements in Nigerian tech companies. Former talent lead at Andela. Expert in resume optimization and interview preparation.
1:8
Instructor to student ratio for personalized attention
Weekly
1-on-1 check-ins to track your progress
24hr
Response time for questions on Slack
Career Outcomes
Our graduates don’t just learn data science, they launch careers at
top companies.
87%
Employed within 90 days
\u20a63.2M\u2013\u20a65.5M
Average starting salary annually
200+
Alumni network
150-300%
Salary increase for career switchers
Key Technologies
Success Stories
Limited Spots Available
Your Next Career Starts 5 October 2026. Do Not Miss It.
Second cohort now open — 26 spots remaining at this price. Secure yours before enrollment closes.