Data Science/Analytics: personalised training plan
Syllabus 2016 (Summary)
1. Programming stream
A. Java
B. Python
C. SQL
D. VBA
(E. XML – data exchange protocol)
F. R
(G. Hadoop – machine learning / big data)
(H. Spark – machine learning / big data)
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2. Mathematics stream
A. Linear algebra including matrix algebra
B. Multivariate calculus
C. Optimisation techniques
… and probably a lot more …
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3. Probability/Statistics stream
A. Basics of statistics
B. Basics of probability theory
C. Descriptive statistics
D. Inferential statistics
E. Statistical analysis
F. Advanced statistics
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4. Computer Science/Algorithms stream
A. Algorithms as found in various libraries and APIs
B. Computational thinking
C. Developing good programming style
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5. Data Science stream
A. Introduction to data science
B. Doing data science
C. Handling large data sets
D. Applied statistics for data science
E. Machine learning fundamentals for data science
[refer Machine Learning stream]
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6. Practical Analytics stream
A. Applications
B. Advanced data science for business applications
C. Visualising, transforming and analysing data in various programming languages
[in tandem with Programming Stream]
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7. Data Visualisation stream
A. Foundations of data visualisation
B. Tools, techonologies and platforms
C. Story-telling with data
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8. Machine Learning stream
A. Introduction to machine learning
B. Mathematical foundations of machine learning
[in tandem with Mathematics Stream]
C. Machine learning techniques and algorithms [for data science]
D. Deep learning
E. Advanced machine learning principles
F. Using Python for machine learning
G. Using R for machine learning
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