In this series of blog i will explain how you can use Graph API using python to make simple request and make some data analysis with it.
So what is Graph API?
It is a API developed by facebook so you can use it to gate data from facebook for example data from pages, groups or even on your personal pages. Facebook contain huge amount of valuable data that you want as a data analyst ,scientist or a researcher we will use python and panda for this series.
We will make some simple example on pages and groups and gets things like what are more interesting posts or comments and when hope you will enjoy. Continue reading
What is Regression?
In simple word it’s a relationship!! exactly what we have in facebook but here it is not between two human beings between two set of numbers.
Examples: Sales ~ price
what will happen to sales if price increase/decrease. If price goes up sales goes down kind of relationship most of us know. We know it from our past experience. But if i will tell you neurofibromin ~ helix-loop-helix(HLH) what will happen if i increase HLH what will going to happen with neurofibromin. (This is what most of us don’t know from our past experience).Regression will tell us what is the relation ship between sales ~ price or neurofibromin ~ helix-loop-helix(HLH) if one increases what happen to others that is the relationship what we can find out. Continue reading
Table: A NoSQL key-value store for rapid development using massive semi-structured dataset. Highly scalable to PBs, and has dynamic scaling based on load. Has fast key/value lookups. We can consider this one for alternative for relational DB which is highly scalable and schema less.
Queue: When applications absorb unexpected traffic bursts and can prevent servers from being overwhelmed by a sudden flood of requests. Instead of getting dropped, incoming requests are buffered in the queue until servers catch up so traffic bursts don’t take down your applications. Continue reading
What is Azure Data Lake:
It is a new flavor of Azure Blob Storage which can handle streaming data (low latency, high volume, short updates), data-locality aware and allows individual files to be sized at petabyte scale. It a basically is a HDFS as a service. We can store all type of data here (structure data like relational DB, unstructured like logs, and semi structure data like json and xml file). While storing data inside azure data lake no need to define the schema. It only support schema on read.
Data science used number and name which are also called category and level to predict answer to question, there are basically 5 question data science answers
- Is this A or B?
- Is this weird?
- How much – or – How many?
- How is this organized?
- What should i do next?
Each of this question is answer by set of machine learning methods called algorithm.