I am Abdenour
I’m Abdenour Landri, a Data Science instructor, content creator, and sales analytics expert based in Algiers. I guide professionals and students toward success through tailored coaching and smart data insights. Whether studying or seeking freelance analysis, I’m here to support you.
HI
My Quality Services
We put your ideas and thus your wishes in the form of a unique web project that
inspires you and you customers.
01
One-on-One Coaching
Customized training in Python and Data science to boost your career and business knowledge.
02
Sales Analytics Consulting
Support on data projects, whether academic, professional, or business-related, ensuring success with practical expertise.
01
One-on-One Coaching
Customized training in Python and Data science to boost your career and business knowledge.
02
Sales Analytics Consulting
Support on data projects, whether academic, professional, or business-related, ensuring success with practical expertise.
My Recent work
We put your ideas and thus your wishes in the form of a unique web project that
inspires you and you customers.
Monthly profit intelligence
An interactive dashboard distills a full year of store activity into one view, combining monthly profit trends with a cumulative performance summary to spotlight seasonality, momentum, and outliers. Leaders use it to time promotions, rebalance inventory, and concentrate resources on months with the highest return while troubleshooting periods that consistently underperform.
Product performance that guides action
A unified view ranks products by both sales volume and profitability so teams can see best‑sellers, high‑margin winners, and low‑impact items at a glance. This enables practical decisions: double down on top performers, tune pricing for margin, and redesign or bundle products that lag always with evidence, not guesswork.
Computer vision for urban intelligence
An end‑of‑study project classifies urban zones from satellite imagery with a convolutional neural network (CNN), showcasing the full CV pipeline from preprocessing to evaluation. Beyond technical depth, the project highlights applied value—supporting urban planning, infrastructure monitoring, and environmental analyses with scalable models.
Movie recommendation system
Another student project delivers a practical recommender that surfaces similar movies using genre and rating signals, demonstr ating baseline retrieval and ranking patterns. It’s a clean blueprint for moving from simple similarity to hybrid recommenders that combine metadata, embeddings, and user behavior.
The project was made by: Ayoub Nasri
Spotify clustering mini‑lab
A student‑built recommendation exercise groups songs by in‑track features using clustering, making unsupervised learning intuitive and visual. The deliverable shows how feature engineering shapes clusters and how similarity can power playlisting, discovery, and personalization use‑cases.
Computer vision for urban intelligence
An end‑of‑study project classifies urban zones from satellite imagery with a convolutional neural network (CNN), showcasing the full CV pipeline from preprocessing to evaluation. Beyond technical depth, the project highlights applied value—supporting urban planning, infrastructure monitoring, and environmental analyses with scalable models.
Movie recommendation system
Another student project delivers a practical recommender that surfaces similar movies using genre and rating signals, demonstr ating baseline retrieval and ranking patterns. It’s a clean blueprint for moving from simple similarity to hybrid recommenders that combine metadata, embeddings, and user behavior.
The project was made by: Ayoub Nasri
Spotify clustering mini‑lab
A student‑built recommendation exercise groups songs by in‑track features using clustering, making unsupervised learning intuitive and visual. The deliverable shows how feature engineering shapes clusters and how similarity can power playlisting, discovery, and personalization use‑cases.
Monthly profit intelligence
An interactive dashboard distills a full year of store activity into one view, combining monthly profit trends with a cumulative performance summary to spotlight seasonality, momentum, and outliers. Leaders use it to time promotions, rebalance inventory, and concentrate resources on months with the highest return while troubleshooting periods that consistently underperform.
Product performance that guides action
A unified view ranks products by both sales volume and profitability so teams can see best‑sellers, high‑margin winners, and low‑impact items at a glance. This enables practical decisions: double down on top performers, tune pricing for margin, and redesign or bundle products that lag always with evidence, not guesswork.
Monthly profit intelligence
An interactive dashboard distills a full year of store activity into one view, combining monthly profit trends with a cumulative performance summary to spotlight seasonality, momentum, and outliers. Leaders use it to time promotions, rebalance inventory, and concentrate resources on months with the highest return while troubleshooting periods that consistently underperform.
Product performance that guides action
A unified view ranks products by both sales volume and profitability so teams can see best‑sellers, high‑margin winners, and low‑impact items at a glance. This enables practical decisions: double down on top performers, tune pricing for margin, and redesign or bundle products that lag always with evidence, not guesswork.
Computer vision for urban intelligence
An end‑of‑study project classifies urban zones from satellite imagery with a convolutional neural network (CNN), showcasing the full CV pipeline from preprocessing to evaluation. Beyond technical depth, the project highlights applied value—supporting urban planning, infrastructure monitoring, and environmental analyses with scalable models.
Movie recommendation system
Another student project delivers a practical recommender that surfaces similar movies using genre and rating signals, demonstr ating baseline retrieval and ranking patterns. It’s a clean blueprint for moving from simple similarity to hybrid recommenders that combine metadata, embeddings, and user behavior.
The project was made by: Ayoub Nasri
Spotify clustering mini‑lab
A student‑built recommendation exercise groups songs by in‑track features using clustering, making unsupervised learning intuitive and visual. The deliverable shows how feature engineering shapes clusters and how similarity can power playlisting, discovery, and personalization use‑cases.
Computer vision for urban intelligence
An end‑of‑study project classifies urban zones from satellite imagery with a convolutional neural network (CNN), showcasing the full CV pipeline from preprocessing to evaluation. Beyond technical depth, the project highlights applied value—supporting urban planning, infrastructure monitoring, and environmental analyses with scalable models.
Movie recommendation system
Another student project delivers a practical recommender that surfaces similar movies using genre and rating signals, demonstr ating baseline retrieval and ranking patterns. It’s a clean blueprint for moving from simple similarity to hybrid recommenders that combine metadata, embeddings, and user behavior.
The project was made by: Ayoub Nasri
Spotify clustering mini‑lab
A student‑built recommendation exercise groups songs by in‑track features using clustering, making unsupervised learning intuitive and visual. The deliverable shows how feature engineering shapes clusters and how similarity can power playlisting, discovery, and personalization use‑cases.
Monthly profit intelligence
An interactive dashboard distills a full year of store activity into one view, combining monthly profit trends with a cumulative performance summary to spotlight seasonality, momentum, and outliers. Leaders use it to time promotions, rebalance inventory, and concentrate resources on months with the highest return while troubleshooting periods that consistently underperform.
Product performance that guides action
A unified view ranks products by both sales volume and profitability so teams can see best‑sellers, high‑margin winners, and low‑impact items at a glance. This enables practical decisions: double down on top performers, tune pricing for margin, and redesign or bundle products that lag always with evidence, not guesswork.
My Education
2018
BACCALAUREATE – TECHNICAL MATHEMATICS
Rabah Bitat High School
July 2024
MASTER’S IN ELECTRICAL ENGINEERING – AUTOMATION & SYSTEMS
USTHB – Algiers, Algeria
My Experience
Jun 2023 - Present
WORKSHOPS & COACHING (UNIVERSITY COLLABORATIONS)
ENSSEA, USTHB, and National Polytechnic School
Aug 2024 - Present
DATA SCIENCE, MACHINE LEARNING, POWERBI & PYTHON INSTRUCTOR
GoMyCode – Algiers, Algeria
Oct 2024 - Mars 2025
PRIMARY SCHOOL ROBOTICS & COMPUTER BASICS TEACHER
Aqua School – Algiers, Algeria
My Skills
We put your ideas and thus your wishes in the form of a unique web project that
inspires you and you customers.
Data workflows
ETL design, cleaning, validation, documentation, and versioning with Git.
Analytics
descriptive and diagnostic analysis, forecasting baselines, KPI definition, and experimentation design.
Communication
executive summaries, stakeholder facilitation, and data storytelling for non‑technical audiences.
Languages and tools
My Clients's Story
Empowering people in new a digital journey with my super services
“ It was honestly a real pleasure doing this formation with you. I learned so much about Python from the best teacher, and at the same time I really enjoyed the classes with the whole group.
I really admire the fact that you’re a complete teacher – always ready to help and never lacking in any domain.
Thank you again for giving us such a great experience.
I’m Raslane Bachatene, proud to say I learned Python from you!
The goat himself ”
Raslane Bachatene
Web developer
“Hello, my name is Ali ZITANI, a trainee in the Python course, taught by the trainer AbdeNour, who has given me a lot of experience and ways of thinking in the field of programming. He has helped me develop myself, so I extend my sincere thanks to the trainer Abdel Nour. Good luck for more successes”
Ali Zitani
1st year INFO USTHB
“I’m Meriem, a student who attended your Python course at the Summer Academy in Gomycode.
I really appreciated having you as my teacher. You were always patient with us and took the time to answer every question clearly. I liked how you encouraged us by suggesting many useful resources and ideas, and how you didn’t limit the course to only the basics. You gave us plenty of time to learn and practice, which made the learning experience richer and more enjoyable. I truly enjoyed learning with you.
If I had to mention one thing, it’s that I felt the course was a bit too short. I know it’s a summer academy and meant to be only three weeks, but I would have loved to spend more time learning with you.”
Meriem B
College student (USTHB)
Sooo I’m Malak, I reaaally enjoyed the “Introduction to AI (Machine Learning)” course that I had with you at GoMyCode. It was super useful and waaay better than the previous course I took there, for many reasons: déjà because je maîtrisais mieux, but mostly because you explain very well and go straight to the point. You also take the time to help each and every student with their work individually since on avance à des rythmes différents, and I find that really cool. You give pas mal d’exemples pratiques pour comprendre, which to me is the best way to learn this kind of stuff. And you’re also very fun as an instructor, like we had constructive conversations in class, everyone had the chance to share their pov, and having fun and playing games when we had free time gives you like +1000 points as a very cool instructor.
Overall, I learned a lot and I’m super satisfied with my experience. I also felt so proud (and honestly super happy) when you asked me recently if you could share my project from the course with other students. It made me realize how valuable what I learned actually is, and it also made me even more grateful for the course, so thank you so much!”
Malak Nasri
College student (The higher school of banking “ESB”)
“Hi abdenour this is Ayoub from the Introduction to AI course and I wanted to say that I really enjoyed the way you explained complex topics in a simple, clear way and how open you were to questions. If anything, maybe a few more hands on examples would make it even better and thanks again for the great sessions”
Ayoub Nasri
College student (National Polytechnic school)
Let's build something useful
Whether you need a profit dashboard that leaders trust, a product performance system to guide merchandising, or a workshop that levels up your team’s analytics, the focus is the same: measurable outcomes, repeatable processes, and clear decisions grounded in data
Phone
+213 540 52 20 50
