Skip to main content

Databases and SQL for Data Science

Under Maintenace !

Some topics may work...
Don't forget to check out those...

1. Introduction to Databases and Basic SQL

    1.1 Introduction to Databases
  • Welcome to SQL for Data Science
  • Introduction to Databases
  • How to create a Database instance on Cloud
    1.2 Basic SQL
  • Create Table Statement
  • SELECT Statement
  • COUNT, DISTINCT, LIMIT
  • INSERT Statement
  • UPDATE and DELETE Statement

2. Advanced SQL

    2.1. String Patterns, Ranges, Sorting, and Grouping

  • Using String Patterns, Ranges
  • Sorting Result Sets
  • Grouping Result Sets

    2.2. Functions, Sub-Queries, Multiple Tables

  • Built-in Database Functions
  • Date and Time Built-in Functions
  • Sub-Queries and Nested Selects
  • Working with Multiple Tables

3. Accessing Databases using Python

  • How to Access Databases Using Python
  • Writing code using DB-API
  • Connecting to a database using ibm_db API
  • Creating tables, loading data and querying data
  • Analyzing data with Python
4. Data To work with

Comments

Popular posts from this blog

Sampling Techniques

Sampling  helps a lot in research. It is one of the most important factors which determines the accuracy of your research/survey result. If anything goes wrong with your sample then it will be directly reflected in the final result. There are lot of techniques which help us to gather sample depending upon the need and situation. This blog post tries to explain some of those techniques. To start with, let’s have a look on some basic terminology: Population Sample Sampling Population is the collection of the elements which has some or the other characteristic in common. Number of elements in the population is the size of the population. Sample is the subset of the population. The process of selecting a sample is known as sampling. Number of elements in the sample is the sample size. A visual representation of the sampling process There are lot of sampling techniques which are grouped into two categories as: Probability Sampling Non- Probability Sampling