Data Science
Data Science
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- Course platform: Udemy
- Level: Advanced/Expert
- Full lifetime access
- Price: Paid Course (with certificate)
- Class length: Approx. 11 hrs.
★★★★★
More details +The score is based on the learner ratings/experience, at Udemy.
PROS:
- Covers a wide-range of advanced reinforcement learning topics.
- Excellent and advanced topics.
- Good details and manual coding.
- Concise details about each algorithms.
CONS:
- Should have more explanation on the theory.
- Need to improve coding lectures.
- Course platform: Coursera
- Offered by: NRU
- Level: Advanced Level
- Price: FREE (Certificate paid)
- Flexible deadlines
- Subtitle in 6 languages
- Class length: Approx. 10 Months.
★★★★★
More details +The score is based on the quality of the course content & user experience as rated by the learners.
PROS:
- In terms of quality of the material this course will absolutely meet your requirements.
- One with graduate level math skills can also commence with this course.
- Assignments are good for getting to know python tools.
CONS:
- Need to improve the quality of content in Bayesian methods for machine learning course.
- Require quite a bit of probability theory knowledge.
- The teachers should put more time into explaining the models and their details.
- Course platform: Edureka
- Level: Advanced
- Price: Paid course (with Certificate)
- Class length: Approx. hrs.
★★★★★
More details +The score is based on the user experience, rated by the learners
PROS:
- Overall well-presented modules, materials and homework helps you gain expertise in this domain.
- Comprehensive content and highly knowledgeable instructors.
- Real-life case studies on a project to implement techniques you learn.
- Each module contains practical assignments.
CONS:
- One should be familiar with Basic Understanding of R.
- Length elementary content.
- Course Platform: Udacity
- Level: Beginner
- Price: Paid Course
- Class length: Approx. 3 Months
★★★★★
More details +The score is based on the user experience, rated by the learners.
PROS:
- Excellent course for learning the fundamentals of AI.
- As compared to other training programs it is clear and meaningful.
- Concise and beginner friendly course material provided.
- Math concepts explanations are awesome.
CONS:
- Lesson on matplotib and seaborn needs more explanation.
- Very lengthy elementary content.
- Course platform: Codecademy
- Level: Beginner
- Price: Paid course (With Certificate)
- Class Length Approx. 10 weeks
- Course platform: Codecademy
- Level: Beginner
- Price: Paid course (With Certificate).
- Class Length Approx. 35weeks
- Course platform: Coursera
- Offered by: MICHIGAN University
- Level: Intermediate
- Price: FREE (Certificate paid)
- Flexible deadlines
- Class length: Approx. 5 Months.
★★★★★
More details +The score is based on the quality of the course content & user experience as rated by the learners.
PROS:
- Programming assignments are packed with coding questions that will help you revise what you have learned.
- Overall you will find good introduction to python for data science.
- Content covered in this specialization program would definitely meet your expectations for learning Data science with Python.
CONS:
- The video content is too general and has little connection with assignment.
- Need to put efforts into designing a curriculum.
- Course platform: Udemy
- Level: Intermediate
- Full lifetime access
- Price: Paid course
- Class length: Approx. 13hrs
★★★★★
More details +The score is based on the user experience, rated by the learners
PROS:
- Excellent veteran instructor’s explanation and teaching process.
- All math is thoroughly explained and ambiguities clarified.
- It will absolutely clear your theory and math concept.
CONS:
- Should give focus on more use of animations and visual learning.
- Need improvement in balance between theory of RL and practical’s.
- Course platform: Simplilearn
- Level: Advanced
- 192 hours of instructor-led training
- 19 hours of self-paced learning
- $1,200 worth of IBM cloud credits
- 15+ real-life projects providing hands-on industry training
- In collaboration with IBM
★★★★★
More details +The score is based on the user experience rated by the learners.
PROS:
- Online self-learning videos & trainers assistance with their enriched knowledge helps you understand the content easily.
- All the courses are well structured with self-learning, live classes, and assessment.
- Excellent industry-related projects and will help you to put into practice what you learned in the class.
CONS:
- Should have basic knowledge of mathematics.
- Need to be familiar with the fundamentals of Python programming.
- Course Platform: Udacity
- Level: Advanced
- Flexible deadlines
- Price: Paid Course
- Class length: Approx. < 3 Months
★★★★★
More details +The score is based on the user experience, rated by the learners
PROS:
- Well organized instructions with concrete and familiar examples.
- Projects and reviewers will give you precious feedback.
- Unique, well-paced and well-structured content.
CONS:
- Should have deeper dive into machine learning algorithms.
- You need to be familiar with programming libraries like Numpy & pandas.
- Course platform: Edureka
- Lifetime Access
- Level: All Level
- Price: Paid course (with Certificate)
- Class length: Approx. 30 hrs
★★★★★
More details +The score is based on the user experience, rated by the learners
PROS:
- Excellent content will clear your conceptually doubts.
- Good collaboration of practical & theoretical knowledge.
- Systematic, well organized and supportive faculties.
CONS:
- Lengthy elementary content.
- Need brief information about R programming.
Best seller 
- Course platform: Udemy
- Level: All Levels
- Full lifetime access
- Price: Paid course (With Certificate)
- Class Length: Approx. 29 hrs.
★★★★★
More details +The score is based on the user experience, rated by the learners
PROS:
- Jam-packed with a good balance of technical and practical information.
- Extremely eye-catching design and presentation. The way content is delivered on the screen is very attractive.
- The exercises are very informative, exhaustive and detailed.
- One can commence without any prior programming experience.
- Realistic approach, balanced theories and practice.
CONS:
- Need to expand details on other classifiers such as knn, SVMs and decision trees.
- Need to add basic mathematics calculus.
- Lengthy content on topics such as probability and stats.