Projects


Summer Coding School

In association with Minerva Statistical Consulting, EFS is organising a Summer Coding School where students will have an opportunity to learn to code in Python. There will be 3 parts:

-Introduction to Python Part I (15-16th June)

-Introduction to Python Part II (29-30th June)

-Introduction to Python for Econometrics (13-14th July)

For detailed information regarding topics and prices please contact EFS at su-efs@bbk.ac.uk

BIRKBECK COLLEGE – 2019 CODING SUMMER SCHOOL

Course Outline

INTRODUCTION TO PYTHON PART I

Day 1

1. Python Basics

Installing Python
Running Python
Hello, World!
Literals
Python Comments
Data Types
Variables
Writing a Python Module
print() Function
Named Arguments
Collecting User Input
Getting Help

2. Functions and Modules
Defining Functions
Variable Scope
Global Variables
Function Parameters
Returning Values
Importing Modules

Day 2

3. Math
Arithmetic Operators
Modulus and Floor Division
Assignment Operators
Built-in Math Functions
The math Module
The random Module
Seeding

4. Python Strings
Quotation Marks and Special Characters
String Indexing
Slicing Strings
Concatenation and Repetition
Common String Methods
String Formatting
Built-in String Functions

INTRODUCTION TO PYTHON PART II

Day 1

5. Iterables: Sequences, Dictionaries, and Sets
Definitions
Sequences
Unpacking Sequences
Dictionaries
The len() Function
Sets
*args and **kwargs



6. Flow Control
Conditional Statements
The is and is not Operators
Python’s Ternary Operator
Loops in Python
The enumerate() Function
Generators
List Comprehensions

7. File Processing
Opening Files
The os and os.path Modules

Day 2

8. Exception Handling
Wildcard except Clauses
Getting Information on Exceptions
The else Clause
The finally Clause
Using Exceptions for Flow Control
Exception Hierarchy

9. Dates and Times
Understanding Time
The time Module
The datetime Module

10. Running Python Scripts from the Command Line
The sys Module
sys.argv

Introduction to Econometrics in python (with stats-models and pandas)

Day 1

Package Installation

Data visualisation (Matplotlib, panda visualisation)

Data visualisations: applications to cross-sectional and time series data

Data manipulation

Day 2

Hypothesis testing

Simple Linear regression model

Multivariate regression model

Residual diagnostics


R Software Training

With R Software training project we aim to help students develop coding skills in R with professional help from Minerva Statistical Consultancy. R is a statistical language and software used worldwide. Our goal is to achieve advanced level skills and understand the importance of coding in today’s world. If you are interested in learning R, get in touch by clicking on“contact us” on our website.


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