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Mohammad Nurdin Notes Posts
Below are some of the useful techniques that will save you time and energy whenever you’re using pandas! These are the useful techniques I have learned based on my consultation using the pandas library.
In this tutorial, I will show you how to deploy the DeepAGI web scraper. Let’s get started.
Due to their usually huge size (often several gigabytes or more) and their probable origin from various, heterogeneous sources, the real-world databases of today are highly susceptible to noisy, incomplete, and unreliable information.
This tutorial explains how to extract data using Selenium and Python without the Facebook Graph API. The reason why we use Selenium instead of Facebook Graph API is that Facebook could possibly modify or disable any endpoint accesses to the API at any time. One reason is the Cambridge Analytica fiasco, abusing their gains on the Facebook platform.
Reddit seems to be a haven for everything you can imagine on the internet. A lot of things that often go overlooked. However, there is a post about the so-called AI guru named Siraj Raval exploiting students with charged them $199 for machine learning course that copy-pasted other people’s GitHub code and banning students from asking for a refund. There are a few lessons that we can learn from this story so we are aware of this problem and make sure it won’t happen on us in the future especially to naive and AI practitioners that self claim as ‘guru’.
You’re going to want to understand the following: what kinds of characteristics or areas make up your data? What are the values of each attribute? What are the discrete characteristics and which are continuously valued? What is the look of the data? How are the values distributed? Can we visualize the data in order to get a better sense of it all? Can we find outliers? Can we evaluate the resemblance between some data objects and others? The subsequent assessment will assist to gain such insight into the data. Knowledge of your data is helpful for pre-processing data, the first important task of data analysis.
- Why Data Science?
- What is Data Science?
- What is Data Science process?
- What Kinds of Data Can Be Analyzed?
- What Kinds of Patterns Can Be Analyzed?
- Which Technologies are Used?
- Which Kinds of Applications Are Targeted?
- Major issues in Data Science
- Data Science and Society
Why Data Science?
We live in a world where a vast amount of data are collected daily. It is a significant necessity to analyze such data to discover knowledge from it.
We live in the information age
It is a popular saying, but in fact, we live in the information age. Every day, terabytes or petabytes of data flow into our computer networks, the World Wide Web (WWW), and various data storage devices from the company, society, science and engineering, medicine, and almost every other aspect of everyday life. Powerful and versatile tools are badly required to automatically discover and convert precious information from enormous quantities of data into structured knowledge.