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Time Series Analysis
NumPy āĻ ā§āϝāĻžāύā§āĻĄ Pandas āĻĻāĻŋā§ā§ āĻĄā§āĻāĻž āĻ ā§āϝāĻžāύāĻžāϞāĻžāĻāϏāĻŋāϏ
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Supervised Machine Learning: Regression, Decision Trees, Random Forest, SVM
Unsupervised Machine Learning: Clustering, PCA
āĻĄāĻŋāĻĒ āϞāĻžāϰā§āύāĻŋāĻ
MLOps
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Lead Instructor
Senior BI Analyst and Chief Of Station, Buddy and Selly GmbH (Germany) BD Team
Lead Instructor
Analyst, IDT Operations, BAT Bangladesh
Lead Instructor
Data Science Consultant, BDJobs.com Ltd
Support Instructor
Adjunct Lecturer, BRAC University








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Overall, I appreciate the breadth of topics covered in this Data Science course. However, to truly fulfill its potential and ensure students gain practical skills, several key areas need improvement. 1. Graded Assignments and Skill Evaluation: A major drawback is the lack of proper, graded assignments that are mandatory for certification. A certificate should reflect a student's proven skills and understanding. Allowing certification without rigorous evaluation undermines its value and fails to assess genuine learning. 2. Teaching Methodology and Hands-On Coding: Machine Learning Module: The over-reliance on pre-written notebooks, where the instructor primarily runs existing code, is a significant concern. This approach does not foster the essential habit of writing code from scratch, which is a critical skill for any data scientist. Deep Learning Module: This part would benefit from a more structured and foundational approach to building coding proficiency from the ground up. 3. Course Structure and Support: Conceptual Classes: The sessions need a more consistent and structured routine. Support instructors should follow a clear, standardized plan to ensure all students receive the same core knowledge, regardless of the session they attend. Student Engagement & Management: There were noticeable issues with course management and student engagement, which impacted the learning experience. 4. Course Content: Data Science Module: The classes in this section were generally good, though adding more in-depth coverage of statistical fundamentals would strengthen the curriculum.
Batch 2
âIâm really enjoying my data science course so far! this course is good
Batch 2
This course was really informative I must say, the instructors were really experienced as well as knowledgeable. Its an excellent outline for starting Data Science career I mut say.
Batch 2
It is a very nice course for data science and ml
Batch 2
It was a rewarding experience. I really liked how the course was structuredâfrom foundational topics to advanced machine learning conceptsâwith clear, practical examples. The instructors explained complex topics in an easy-to-understand way, and the hands-on projects helped solidify my understanding. I especially appreciated the support from mentors and the interactive platform that made learning more engaging. The course gave me a solid base to pursue further learning and apply my skills in real projects. I would definitely recommend this course to beginners or anyone looking to switch to a data science career.
Batch 2
Great Course
Batch 2
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Batch 2
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Batch 2
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Batch 2
Best Syllabus, Great Instructors!
Batch 1
The course enhanced my understanding of data science and machine learning, providing practical skills, real-world insights, and clear career direction.
Batch 1
I found the machine learning course very helpful, especially for my research thesis. It gave me the skills to conduct analysis using Python, which was directly useful for my work.
Batch 1