Data Analysis

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Machine Learning

Course Contents

Can be used for specific purposes to identify the type of data. Understand the type of pointer variables and can identify. Forming part of the program that calls itself. To understand the methods of sorting and searching. The possibilities of the programming language used to generate the library, and to examine the current library.

Weekly Schedule

1) Introduction - Statistical Methods in the Context of Scientific Research. Sampling. Observational Studies and Experiments. Data Discovery and Analysis. Statistical Inference. Calculation using R.
2) Data Exploration - Data Visualization and Summary Statistics. Variable Types. Exploring Categorical Variables. Exploring Numerical Variables. Data Preprocessing.
3) Exploring Relationships - Visualizing and Summarizing Relationships Between Variables. Relationships Between Two Numeric Random Variables. Relationships Between Categorical Variables. Relationships Between Numerical and Categorical Variables.
4) Probability - Probability as a Measure of Uncertainty. Complement, Un ion and Intersection. Discrete Events. Conditional Probabilities. Independent Events. Bayes Theorem.
5) Random Variables and Probability Distributions - Random Variables. Probability Distributions. Cumulative Distribution Function and quantile.
6) Estimation - Parameter Estimation. Point Estimation. Sampling distribution. Confidence Interval. Error Margin.
7) Hypothesis Testing - Hypothesis Tests Related to Population Mean. Statistical Significance. hypothesis testing using t-tests. Hypothesis Testing for Population Ratio.
8) Midterm exam
9) Analysis of Variance (ANOVA) - Introduction. ANOVA Assumptions.
10) Analysis of Categorical Variables - Pearson's ?2 Test for a Categorical Variable. Pearson's ?2 Test of Independence. Status Tables.
11) Regression Analysis - Linear Regression Models with One Binary Explanatory Variable. Statistical Inference Using Simple Linear Regression Models. Linear Regression Models with Single Numeric Explanatory Variables. Model Assumptions and Diagnosis. Multiple Linear Regression.
12) Clustering - K-means Clustering. Hierarchical Clustering. Standardizing Variables Before Clustering
13) Bayesian Analysis - Introduction. Previous and Next Possibilities. Bayesian inference. Prediction. Hypothesis testing.
14) SPSS usage and examples
15) R language usage and examples in database management systems.
16) General information about data mining.

Recommended or Required Reading

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Demonstration
6) Modelling
7) Group Study
8) Simulation
9) Case Study
10) Self Study
11) Problem Solving
12) Project Based Learning

Assessment Methods and Criteria

Contribution of Midterm Examination to Course Grade	30%
Contribution of Final Examination to Course Grade	70%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required