The SQL and R Programming skills required for Data Science
Deriving useful insights from data is what is termed as Data Science. Data Science involves extracting, processing, and analyzing tons of data. SQL can be used to store, access and extract massive amounts of data in order to carry out the whole Data Science process more smoothly. R Programming can be used to solve the statistical problems in the data reporting.
SQL Skills are required for Data Science :
- Relational Database Model
- SQL commands
- Null Value
- Indexes
- Joins
- Primary & Foreign Key
- Sub Query
- Creating Tables
Relational Database Model:
A Relational Database Model System (RDBMS) is the primary and foremost necessary concept for an aspiring Data Scientist. In order to store structured data, you must know RDBMS in-depth. You can then access, retrieve and manipulate the data through SQL. An RDBMS is a standard for every data platform. Even the advanced big data platforms consist of an RDBMS section for processing structured information.
SQL commands:
- Data Query Language
- Data Manipulation Language
- Data Definition Language
- Data Control Language
Null Value:
Null is used to represent a missing value. A field that contains Null value is blank in a table. However, a Null value is different than a zero value or a field that contains blank spaces.
Indexes:
With the help of special lookup tables, a database search engine can locate values in a row easily. With SQL indexing, we can quickly load the data into the database.
Joins:
Table joins are the most important concepts of relational databases that a data scientist must know. There are two types of joins – Inner Join and Outer Join. They are then further divided into Inner, Left, Right, Full etc.
Primary & Foreign Key:
A primary key represents unique values in a database. With the help of a primary key, we are able to distinguish each line and record from the database. A Foreign Key, on the other hand, is used to connect two tables together.
Sub Query:
A sub query is the nested query that is embedded in another query. There are four important sub queries in SQL – SELECT, INSERT, UPDATE and DELETE. It will return the information to the primary query.
Creating Tables:
Data Science makes use of organized relational tables, and therefore, it is necessary to know how to create tables in SQL.
R programming skills required for Data science:
- Regression Analysis statistics
- R-Chi square Test
R-Logistic Regression:
The Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. It actually measures the probability of a binary response as the value of response variable based on the mathematical equation relating it with the predictor variables.
Regression analysis statistics:
Regression analysis is a very widely used statistical tool to establish a relationship model between two variables. One of these variable is called predictor variable whose value is gathered through experiments. The other variable is called response variable whose value is derived from the predictor variable.
Regression analysis statistics in R as below
- R-Linear Regression
- R-Multiple Regression
- R-Logistic Regression
R-Chi square Test:
Chi-Square test is a statistical method to determine if two categorical variables have a significant correlation between them. Both those variables should be from same population and they should be categorical like ? Yes/No, Red/Green etc.