Contents

Mathematics Knowledge

Calculus

Linear Algebra

Probability

READ

Here is the Probability Reading Schedule(Dec, 2018).

Probability, Markov Chain, Queuing and Simulation(Part II and III)
  1. Markov Process and Markov Chains
  2. Discrete-Time Markov Chains: Definition
  3. The Chapman-Kolmogorov Equations
  4. Classification of States
  5. Irreducibility
  6. The Potential, Fundamental and Reachability Matrices
  7. To be cont.(Markov Chains)
  8. Introduction and Basic Definition(Queuing Theory)
  9. Birth-Death Processes: The M/M/1 Queue
  10. General Birth-Death Processes
  11. Multiserver System
  12. To be cont.(Queuing Theory)
Stochastic Process
  1. Introduction
  2. Markov Processes
  3. To be cont.

WRITE

Here is the Probability Writing Schedule(Dec, 2018).

Stochastic Process
  1. Introduction to Stochastic Process
  2. DTMC: Discrete Time Markov Chains
  3. k-denpendent Markov Chains
  4. Markov Chain Example: Ehrenfest Model
  5. Time Dependency of DTMC
  6. To be cont.

Statistics

Basic Analytic Tools

Excel

SPSS

SQL

Python

READ

Here is the Python Reading Schedule(Dec, 2018).

Python Data Analysis
  1. Getting Started with Python Libraries
  2. Numpy Arrays
  3. Statistics and Linear Algebra
  4. To be cont.
Think Bayes: Statistical Modeling in Python
  1. Bayes’s Theorem
  2. Computational Statistics
  3. Estimation
  4. More Estimation
  5. Odds and Addends
  6. Decision Analysis
  7. Prediction
  8. Observer Bias
  9. Two Dimensions
  10. To be cont.

R

READ

Here is the R Reading Schedule(Dec, 2018).

R Data Analysis: Methods and Application
  1. Introduction to R
  2. Data Structure and Basic Operation
  3. Function and Optimization
  4. Random Numbers and Sampling Simulation
  5. Data Import/Export and Preprocessing
  6. Exploratory Data Analysis
  7. To be cont.
Introductory Statistics with R
  1. Basics
  2. R Environment
  3. Probability Distribution
  4. Descriptives Statistics and Diagrams
  5. To be cont.

Advance Analytic Technology

Machine Learning

READ

Here is the Machine Learning Reading Schedule(Dec, 2018).

Machine Learning and Practice in Pyhton
  1. Introduction
  2. Basics
  3. Advanced
  4. Practice

Cloud Computing

Industry Insight

Reading and Writing Schedule

gantt title Machine Learning Reading Schedule(Dec, 2018) dateFormat YYYY-MM-DD section Machine Learning and Practice in Pyhton Introduction :done, 2018-12-01, 1d Basics :active, 1d Advanced : 2d Practice : 3d
gantt title Python Reading Schedule(Dec, 2018) dateFormat YYYY-MM-DD section Python Data Analysis Getting Started with Python Libraries :active, 2018-12-01, 1d Numpy Arrays : 2d Statistics and Linear Algebra : 4d To be cont. :done, 24d section Think Bayes - Statistical Modeling in Python Bayes's Theorem :active, 2018-12-01, 1d Computational Statistics :active, 2018-12-01, 1d Estimation : 2018-12-02, 1d More Estimation : 2018-12-02, 1d Odds and Addends : 1d Decision Analysis : 1d Prediction : 1d Observer Bias : 1d Two Dimensions : 1d To be cont. :done, 24d
gantt title R Reading Schedule(Dec, 2018) dateFormat YYYY-MM-DD section R Data Analysis - Methods and Application Introduction to R :active, 2018-12-01, 1d Data Structure and Basic Operation :active, 2018-12-01, 1d Function and Optimization : 2018-12-02, 2d Random Numbers and Sampling Simulation : 2018-12-02, 2d Data Import/Export and Preprocessing : 2d Exploratory Data Analysis : 2d To be cont. :done, 24d section Introductory Statistics with R Basics :active, 2018-12-01, 1d R Environment : 1d Probability Distribution : 2018-12-02, 3d Descriptives Statistics and Diagrams : 3d To be cont. :done, 24d
gantt title Probability Reading Schedule(Dec, 2018) dateFormat YYYY-MM-DD section Probability, Markov Chain, Queuing and Simulation(Part II and III) Markov Process and Markov Chains :done, 2018-12-01, 1d Discrete-Time Markov Chains - Definition :active, 2018-12-01, 1d The Chapman-Kolmogorov Equations : 1d Classification of States : 1d Irreducibility : 1d The Potential, Fundamental and Reachability Matrices :done, 7d To be cont.(Markov Chains) :done, 20d Introduction and Basic Definition(Queuing Theory) : 2018-12-05, 1d Birth-Death Processes - The M/M/1 Queue : 1d General Birth-Death Processes : 1d Multiserver System :done, 4d To be cont.(Queuing Theory) :done, 20d section Stochastic Process Introduction :active, 2018-12-01, 2d Markov Processes : 5d To be cont. :done, 24d
gantt title Probability Writing Schedule(Dec, 2018) dateFormat YYYY-MM-DD section Stochastic Process Introduction to Stochastic Process :done, 2018-12-01, 5d DTMC - Discrete Time Markov Chains :done, 2018-12-01, 5d k-denpendent Markov Chains :active, 2018-12-01, 1d Markov Chain Example - Ehrenfest Model : 3d Time Dependency of DTMC : 3d To be cont. :done, 24d