TARANEH KORDI

Data Engineer at Gore Mutual Insurance

PROGRAM NAME

DATA SCIENCE TRAINING PROGRAM

DATA SCIENCE TECHNICAL MENTORSHIP PROGRAM

PROGRAM TYPE

CERTIFICATE PROGRAM

 

PROGRAM INFROMATION

In general, it was a great journey and I learned a lot in a short time.

Lantern helped me to learn how to learn new things. Alireza helped me to learn Python from almost zero. Apart from in-class learning, interacting with classmates about the homework and projects helped me to learn better.

Amirsina taught us SQL and Tableau very well; however, I think SQL material could be more advanced. There were some useful functionalities that we did not cover.

Tanaby is amazing. Apart from his knowledge in machine learning, he generously offered us to work on a real-world project to fill the gap of experience in our resume.

I also appreciate Lantern’s support regarding this project, resume and cover letter consultations,  and Mock interview which were really helpful.

In general, it was a great journey and I learned a lot in a short time.

TARANEH KORDI

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TECHNICAL ASSESSMENT

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OUR RESULTS DRIVEN TRAINING MODELS

ALLOWS EACH STUDENT TO TAKE PART IN A FLEXIBLE PROGRAM TO GAIN THAT REAL WORLD EXPERIENCE THROUGH INDUSTRY BASED, TRAINING, PROJECTS & MORE.

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ALUMNI

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The instructors are busy professionals, but they generously exchange ideas and supervise the projects and usually accept mentorship requests. The scopes of the projects vary from 1 month for the basic courses, to 4 months for the advanced courses. The system is very co-operative and in my personal case, I have learnt a lot from my classmates, while collaborating on different projects. Lantern has skin in the game and charges a small registration fee relative to the norm of the industry; its bulk of income depends on future graduates’ success in landing a decent career.

Prior to Lantern, I had specialized in UofT’s Analytics stream, which was just a theoretical exposure to 4 data science courses. At Lantern, I practically developed my coding skills. Serious students who dedicate time and effort (at least 20-30 hours a week) will develop the fundamental coding skills in Python and R within a 3-month time period, and then they can build up more complex Machine Learning and in-depth analytical projects supervised by instructors all of whom are industry professionals. The class size is approximately 20 students for general courses and gets smaller for more advanced courses. Throughout the semester, students get career advice, resume review, soft skills catch up, mock interviews and should also deliver a few presentations about the projects they have developed.