DATA SCIENCE TECHNICAL MENTORSHIP PROGRAM

BECOMING A DATA SCIENTIST

Data Science Involves A Plethora Of Disciplines And Expertise Areas To Produce A Holistic, Thorough And Refined Look Into Raw Data. Data Science Has Grown Tremendously In Popularity And So Has The Competition. How Do We Combat That? How did we manage to get a 78% student hire rate within 12 months of graduation? Heres Our Recipe Of success where we help our students become hireable professionals.

WORK WITH SENIOR INDUSTRY PROFESSIONALS​

WE CANNOT EMPHASIZE THIS ENOUGH. THE DATA INDUSTRY IS CONSTANTLY CHANGING AND ADAPTING, SO SHOULDN'T YOUR TRAINING PROGRAM BE THE SAME? HAVING INDUSTRY PROFESSIONALS NOT ONLY GIVES US A DIRECT INSIDER VIEW TO THE INDUSTRY ITSELF BUT IT ALSO ALLOWS US TO MAKE SURE OUR TRAINING STAYS CONSISTENT TO THE NEEDS AND DEMANDS OF YOUR INDUSTRY.​

ITS NOT ALWAYS WHAT YOU KNOW BUT WHO YOU KNOW​

WORKING WITH INDUSTRY PROFESSIONALS HAS OPENED NETWORKS FOR OUR STUDENTS TO OTHER PROFESSIONALS WITHIN THEIR DOMAINS. AT LANTERN WE PUSH NETWORKING OPPORTUNITIES, LEVERAGING INTERNAL OPPORTUNITIES AND MORE THROUGH OUR TEAM OF PROFESSIONALS AND COMMUNITY PARTNERS.

CURRENT PROJECTS

DR. MAHDI SHAHBAB

SENIOR DATA SCIENTIST
RBC

Dr. Mahdi Shahbaba is a Senior Data Scientist in Marketing Science team at RBC. He is currently working on Machine Learning models for product recommendation, pricing, and customer profitability. Prior to RBC, he was a Lead Data Scientist in Digital Factory and Decision Sciences departments at Scotiabank, where he developed ML models for fraud prevention and marketing campaigns. He has a PhD in Electrical and Computer Engineering from Ryerson University with the main focus on Unsupervised Machine Learning.

CUSTOMER PROFITABILITY USING RETAIL DATA

PROJECT DETAILS

In this project you will explore retail data to build Machine Learning models and predict customer profitability as well as Lifetime Value (LTV). You need to cover all the steps of a generic data science project: data exploration, visualization, model selection, training, validation, fine-tuning, testing, etc. Then, after building your benchmark model, you will explore a wide range of Machine Learning algorithms for a complete comparison between results.
At the end, you will deploy your final model to Google Cloud Platform and make it accessible to other users. Through this process, you will gain invaluable skill sets of a full stack Data Scientist.

POTENTIAL EMPLOYMENT OPPORTUNITIES

PROJECT DURATION & COST

KNOWLEDGE-BASED CHATBOT

PROJECT DETAILS

Many organizations have domain-specific knowledge-base related to each of its departments, e. g., human resources, financial risk, … etc.In this project, students will develop a chatbot that facilitates the retrieval of specific information within the knowledge base through question-answers dialogues. That is, the chatbots ‘understands’ input questions and provides relevant answers which triggers more questions from the user.

Students will learn how to build a chatbot from scratch. This will involve techniques for text processing such as text cleaning, statistical language modelling, and vocabulary creation. They will develop two types of chatbots: rule-based chatbots and generative, aka AI, chatbots. In this regard, students will start by learning text processing techniques such as tokenization, word stemming and lemmatization and then they will learn about BNF grammar and rule-base matching. In addition, they will be introduced to common feature engineering techniques such as term frequency document inverse frequency (TFIDF) and latent semantic indexing (LSI) as well as more advanced methods such as Word2Vec and Doc2Vec. More importantly, students will learn how to build, design, train, validate, and test neural language models such as RNN/LSTM. Finally, particular attention will be focused on Seq2seq models which are used to encode input questions and output decoded answers using an RNN/LSTM-based language generating model.

Python 3.7 is the main programming language used in the course. Throughout this project, students will be using a variety of important python packages such as NLTK and spacy for text processing, Keras and tensorflow for designing and training neural networks, and common packages such as numpy, scipy, sci-kit learn, and pandas.

POTENTIAL EMPLOYMENT OPPORTUNITIES

PROJECT DURATION & COST

DR. MOATAZ EL EYADI

ASSOCIATE DIRECTOR
RBC

Dr. El Ayadi is an Associate Director in the Operational Risk Analytics team which is a part of the Group Risk Management (GRM), Royal Bank of Canada. He is currently leading the technical team towards developing Natural Language Processing (NLP) solutions that address business needs and implementing efficient and standardized ETL pipelines for computing key risk indicators (KRIs).Dr. El Ayadi earned his PhD degree in Electrical and Computer Engineering, University of Waterloo (Pattern Analysis and Machine Learning (PAMI) lab) in 2009. Dr. El Ayadi was a Data Scientist in the Decision Sciences department at Scotiabank where he was leading the AI for Speech Insights team to develop voice-enabled business solutions using both Speech Recognition and NLP methods.

TANABY MOFRAD

DIRECTOR OF DATA SCIENCE
TANGERINE BANK

Tanaby Zibamanzar Mofrad is a senior data scientists at Scotia Bank. He uses Artificial Intelligence and Machine learning as one of the main parts of his job and he has a great knowledge in analytics and big data related tools. He has received his masters degree in computer science from Brock university and has been working in data science and data analysis industry for more than 5 years.

EVEREST BOOT CAMP

PROJECT DETAILS

“Comparison of different classifiers in their ability to deal with data with imbalance class distributions”

In this project we will assess classification ability of multiple classification algorithms when dealing with imbalance data.

Algorithms which will be used: SVM, ANN, XGB, LGBM, Auto Encoder, AE + Kmeans Data: Data will provided by mentoring team and will be used in the project annotated as below: 10%, 20%, 30%, 90%,100%.

Each algorithm will be assessed on different data annotations. Data will be pushed in both Github and Source repo with proper documentation

POTENTIAL EMPLOYMENT OPPORTUNITIES

PROJECT DURATION & COST

MARKETING CAMPAIGNS

PROJECT DETAILS

In this project you will build Machine Learning models to predict client response to marketing campaigns. The trade-off between profit and response, marketing channel selection and cost minimization are some of the challenges that you have to overcome in any marketing campaign. You need to cover all the steps of a generic data science project: data exploration, visualization, model selection, training, validation, fine-tuning, testing, etc. After building your  benchmark model, you will explore a wide range of Machine Learning algorithms for a complete comparison between results. At the end, you will deploy your final model to Google Cloud Platform and make it accessible to other users.

At the end, you will deploy your final model to Google Cloud Platform and make it accessible to other users. Through this process, you will gain invaluable skill sets of a full stack Data Scientist.

POTENTIAL EMPLOYMENT OPPORTUNITIES

PROJECT DURATION & COST

DR. MAHDI SHAHBAB

SENIOR DATA SCIENTIST
RBC

Dr. Mahdi Shahbaba is a Senior Data Scientist in Marketing Science team at RBC. He is currently working on Machine Learning models for product recommendation, pricing, and customer profitability. Prior to RBC, he was a Lead Data Scientist in Digital Factory and Decision Sciences departments at Scotiabank, where he developed ML models for fraud prevention and marketing campaigns. He has a PhD in Electrical and Computer Engineering from Ryerson University with the main focus on Unsupervised Machine Learning.

COMPLETE A TECHNICAL ASSESSMENT

AS A PART OF OUR APPLICATION PROCESS FOR THE TECHNICAL MENTORSHIP PROGRAM, ALL APPLICANTS MUST COMPLETE THE TECHNICAL ASSESSMENT.

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