# Statistics for Data Science

## Objectives

The objective of data analysis is to extract the relevant information from the data, which can then be used to solve a given problem. The purpose of this course is to provide and solid background in exploratory data analysis and statistical inference.

## General characterization

200178

7.5

##### Responsible teacher

Bruno Miguel Pinto Damásio

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

### Prerequisites

Statistics and linear algebra (recommended)

### Bibliography

• Lock, Robin, H., Lock, P. F., Morgan, K.L., Lock, E.F.; Lock, D.F. (2017) Statistics: unlocking the power of data. Second edition, Wiley.
• Wooldridge, J. M., Introductory econometrics: A modern approach, 6th Edition. South-Western, Cengage Learning, 2016;
• Heiss, F., Using \textbf{R} for Introductory Econometrics, 1st Edition; CreateSpace (Independent publishing platform), 2016.
• Greene, W. H., Econometric analysis, 7th edition, Pearson, 2012;
• Stock, J. H and Watson, M. W, Introduction to Econometrics, 3rd. Edition, Pearson, Addison Wesley, 2011

### Teaching method

The course is based on theoretical-practical and practical classes. Practical classes and problem-solving oriented.

### Evaluation method

• (50%) Final exam (1st and 2nd rounds)
• (50%) Project (optional)

A minimum grade of 8.5 is required in the final exam to pass.

## Subject matter

1. Collecting data
2. Statistical distributions
3. Describing data
4. Confidence intervals
5. Hypothesis testing
6. Inference
7. The Nature of Econometrics. Correlation vs Causality
8. The Multiple Linear Regression Model (MLRM)
9. MLRM: Inference
10. Heteroskedasticity
11. Asymptotic properties of the OLS
13. Functional Form Misspecification
14. MLRM with qualitative information

## Programs

Programs where the course is taught: