Data Science & Machine Learning (Part-Time)
Data Science & Machine Learning (Part-Time)
Summary
Xccelerate's 6-week part-time evening course offers a comprehensive introduction to data science and machine learning. Designed for beginners, this online program covers essential tools and techniques for data manipulation and visualization using Python libraries. Participants engage in hands-on learning through Jupyter Notebooks, exploring algorithms and machine learning concepts to transform raw data into actionable insights.
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Before You Learn / Who This Course Is For
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Ideal for beginners in data science
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Open to technical and non-technical learners
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No prerequisites required
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What to Expect
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Part-time evening online course
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Hands-on projects using Jupyter Notebooks
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Introduction to Python libraries and algorithms
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What You'll Achieve
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Data Science certification upon completion
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Skills in data manipulation and visualization
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Understanding of machine learning concepts
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Certifications covered by this course
No certifications are covered by this course.
Course Reviews
Anonymous
Graduate 2020
Review on Xccelerate
The course provide great offline learning experience and allow high degree of freedom in campus. You can stay overnight and work hard in a quiet environment. The curriculum include essential knowledge for entering industry and staff is nice. Also, they provide CV reviews and connect students with a lot of companies.
Ray
Graduate 2019
Like Minded People
Was looking for an in-person DataScience bootcamp to keep me committed (to my career switch) and to meet like-minded people interested in ML and AI in HK. Happy to report that Xccelerate delivered in both thoses regards. Was also half the price of the nearest competitor.
In-person courses offer face-time with instructors that I couldn't find in online offerings. I also got to work with people in a team, in person--something which I believe contributed to my job offer.
After graduating, I had a look around and found a place for a (junior) Data Scientist at an established startup within a month.
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