Data Science Curriculum from Scratch 2018 (Part 1)

The purpose of the curriculum is to serves as a guideline for you to get started on data science even if you lacked the technical knowledge.Introduction to Data ScienceData Science A-Z from UdemyStatisticsFundamentals of Statistics by MITx MicroMasters ProgramProbabilityProbability – The Science of Uncertainty and Data by MITx MicroMasters ProgramCalculusMathematics for Machine Learning: Multivariate Calculus from CourseraMultivariable Calculus by Khan AcademyLinear AlgebraMathematics for Machine Learning: Linear Algebra from CourseraLinear Algebra by Khan AcademyEssence of Linear Algebra by 3Blue1BrownLinear Algebra by MIT 18.06Python ProgrammingComplete Python Bootcamp: Go from Zero to Hero in Python from UdemyIntroduction to Computer Science and Programming Using Python by MITx 6.001xIntroduction to Computation Thinking and Data Science by MITx 6.002xMachine LearningMachine Learning by Andrew NgIntroduction to Machine Learning for Coders by fast.aiMachine Learning with Python: From Linear Models to Deep Learning by MITx MicroMasters ProgramDeep Specialization from CourseraPractical Deep Learning for Coders by fast.aiThere is no hard and fast rule for learning such a complex topic..The beauty of online learning is that you get to choose what you need and what excite you.For this part 1 of the series, I will review the maths and python fundamental courses that I had taken..Please note that these are my personal opinion which might or might not resonate with you..Nevertheless, let’s get going. to Data ScienceI like to give a special mention to Data Science A-Z by Kirill Eremenko and the SuperDataScience Team..This is my first course breaking into the world of Data Science and Kirill Eremenko did an excellent job piquing the interest of all data science enthusiast..Do not expect to learn any tools or technical skills from this course but instead, appreciate the bigger picture Kirill Eremenko tries to paint as he brings you through the entire workflow of a standard Data Science project.You get to play around with a few datasets, doing simple tasks such as data cleaning, modeling, model selection, basic SQL and data presentation..It is as complete as it can get for an introductory data science course and leaves you craving for more..Furthermore, this course also serves as a reality check for you..If this is not what you expect as a data scientist, maybe pursuing data science is not your best choice..Overall recommended if you still have doubt about the role of a data scientist.StatisticsMoving on, maths fundamental required for machine learning..You might have realized that I had included the Statistics and Data Science MicroMasters program from MITx but that is totally up to your preference..You will do just fine without the program.. More details

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