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CURRENT DATA SCIENCE PROJECTS

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

  • Data Scientist in Retail Companies

  • Data Scientist in Consulting Firms

  • Data Scientist in E-Commerce Companies

PROJECT DURATION & COST

  • Program Duration: 5-7 months

  • Upfront Cost: $0

  • Upon Securing Employment: $8,999

MENTOR

Dr. Mahdi Shahbab
Dr. Mahdi ShahbabSenior 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.

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.

POTENTIAL EMPLOYMENT OPPORTUNITIES

  • Data Scientist in Financial Institutions

  • Data Scientist in Insurance Companies

  • Data Scientist in Telecom Companies

PROJECT DURATION & COST

  • Program Duration: 5-7 months

  • Upfront Cost: $0

  • Upon Securing Employment: $8,999

MENTOR

Dr. Mahdi Shahbab
Dr. Mahdi ShahbabSenior 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.

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

  • Data Scientist in Legal Companies
  • Data Scientist in Financial Institutions

  • Data Scientist Consultant

PROJECT DURATION & COST

  • Program Duration: 6 months

  • Upfront Cost: $1,500

  • Upon Securing Employment: $7,499

MENTOR

 Moataz El Ayadi
Moataz El AyadiAssociate Director Data Scientist, 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.