Data Science I (BA 2nd-3rd grade)

“Information Procesing Theory I” in K.G.

Objective

In this class, we will learn the statistical analysis method necessary for data science and practice it by “R” which is the programming language for statistical analysis. Data science is the study of analyzing big data, modeling phenomena, and creating new services and management strategies. In this class, we will learn the fundamental study necessary for statistical analysis such as analytics, linear algebra mathematics, statistics (up to descriptive statistics of one variable), and aim to be able to calculate those by computer programming .

Goal

- You can explain the meaning of the fundamental function of analytics.
- You can calculate values using basic functions of analytics.
- You can explain the meaning of the basic operation of linear algebra.
- You can conduct basic calculation of linear algebra.
- You can explain the meaning of descriptive statistics of one variable.
- You can conduct descriptive statistics of one variable.
- You can write a program code of R using basic control structure of programming.

Schedule

-Section 1: Analytics-

Day 1: Basic function (square root, absolute value, power, logarithmic function, trigonometric function)

Day 2: Type of variable

-Section 2: Linear algebra-

Day 3: Scalar, vector, dot product

Day 4: Matrix, transpose, unit matrix, rotation matrix

Day 5: Determinant, inverse matrix

Day 6: Eigenvalue, eigenvector, dimension reduction

Day 7: Data frame, CSV file

-Section 3: Control structure of programming-

Day 8: Function, conditional branch

Day 9: Loop (iteration)

Day 10: Recursive call

-Section 4: Descriptive statistics-

Day 11: Frequency distribution, histgram

Day 12: Arithmetic mean, geometric mean, harmonic mean

Day 13: Variance, standard deviation, unbiased variance, normalization

Day 14: Summary