SQL in Financial Engineering (Optimize data handing in VBA/R/MATLAB)
Please write 10 main points about the course.
1. Sensitization to Data Analytics and trends. Requires no knowledge
of programming or database.
2. How to make queries your own setup of SQL Database and other
scripts. Introduction to calling SQL from VBA, MATLAB and R
3. Playing with missing data is the most important things and for
that I will show five important commands. Missing data types: NaN, blank, 0 and
how they are used.
4. Contains right blend of learning and practice (Ratio 6:4). Highly
flexible and tailored as per needs of individual based on his preferred choice
of investment theme
5. The more your reduce data before pulling the easier it would be do
the computation. Utility functions for data cleaning, data ready for charting, avoiding
looping, error handling will be explained.
6.
Essential Aggregate
command, sub-TABLE, VIEWS and PIVOT. Exploring applications in Equity and CMBS
(for linking all properties linked) Fixed Income Analytics.
7. Optional: Introduction to Regression, clustering, Charting, Monte
Carlo Simulation, Map Objects for Financial Modelling
8. Optional Bonus: Essential SQL Queries – Linking SQL with Excel
using VBA
Explain 3 main points on how this
course will benefit the student?
1. Getting ready for the next data revolution in Analytical SQL.
2. Avoiding commands that will slow down SQL. Understand the basics
of all major languages used for data handling in SQL, SAS and MATLAB.
3. Understanding the
importance of handling missing data, optimization of speed using novel methods.
Class Number
|
Topic
|
Duration
|
1
|
Introduction
to SQL. Saving, organizing and reading simple data.
|
0.5 Hours
|
2
|
SQL
applications in Equity, Fixed Income, Risk [with some reference to other
tools like SAS, Excel, MATLAB]
|
0.5 Hours
|
3
|
What
and what not to do in SQL. Loops, Logics, Datatypes. How to make data
handling faster
|
0.5 Hours
|
4
|
SQL
aiding in Quantitative computations: Making data ready for Quant Tech like
Regression, Charts, Clustering & Monte Carlo Simulation in Python
|
0.5 Hours
|
5
|
Project
on SQL: SQL for structured Data Analysis like Joins, nested queries
|
0.5 Hours
|
6
|
Inner,
Outer, Cross Joins and Self Joins
|
0.5 Hours
|
7
|
Sorting
Data: Filtering Data with a WHERE Clause. Filtering with the TOP and
OFFSET-FETCH Options
|
0.5 Hours
|
8
|
Using
Aggregate Functions like GROUP BY Clause, and HAVING
|
0.5 Hours
|
9
|
Using
Set Operators, Writing Queries with the UNION Operator, Using EXCEPT and
INTERSECT, and Using APPLY
|
0.5 Hours
|
10
|
Pivoting
and Grouping Sets: Writing Queries with PIVOT and UNPIVOT and Working with
Grouping Sets
|
0.5 Hours
|
11
|
Writing
Queries with Built-In Functions and
Using Conversion Functions
Using
Logical Functions and Using Functions to Work with NULL
|
0.5 Hours
|
