The more data you collect, the better your models, but what if the data you want resides on a website? This is the problem of social media analysis when the data comes from users posting content online and can be very unstructured. While there are some websites who support data collection from their web pages and have even exposed packages and APIs (such as Twitter), most of the web pages lack the capability and infrastructure for this. If you are a data scientist who wants to capture data from such web pages then you wouldn't want to be the one to open all these pages manually and scrape the web pages one by one. To push away the boundaries limiting data scientists from accessing such data from web pages, there are packages available in R.
Over the years, almost every organization has understood the importance of analyzing data. In fact, it would not be an overstatement to say that "No organization will be able to survive today's cut-throat competition if it does not analyze data." Data analysis as we know it is the process of taking the source data, refining it to get useful information, and then making useful predictions from it. In this Learning Path, we will learn how to analyze data using the powerful toolset provided by Python. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
Classification software: building models to separate 2 or more discrete classes using Multiple methods Decision Tree Rules Neural Bayesian SVM Genetic, Rough Sets, Fuzzy Logic and other approaches Analysis of results, ROC Social Network Analysis, Link Analysis, and Visualization software Text Analysis, Text Mining, and Information Retrieval (IR) Web Analytics and Social Media Analytics software. BI (Business Intelligence), Database and OLAP software Data Transformation, Data Cleaning, Data Cleansing Libraries, Components and Developer Kits for creating embedded data mining applications Web Content Mining, web scraping, screen scraping.
Data Science has demonstrated to be a boom to both the IT and the business. The innovation incorporates getting value from information, understanding the information and its examples and afterwards foreseeing or producing results from it. Data science is much popular by organizations to analyze their enormous volume of data sets and generate optimized business insights from them, in this manner expanding profits for the organization. Picking the correct seller and solution can be an entangled procedure, one that requires in-depth research and regularly boils down to something other than the solution and its technical abilities. To make your hunt somewhat simpler, we've profiled the best data science platforms and tools.
The Internet generates millions of useful data every day. All of this data is recorded and stored, making the Internet an easily accessible hub that hosts an overwhelming volume of data, generated at immense speed with every passing moment. This data can be extracted to study recurring patterns and trends to assist in the deduction of useful insights and predictions. When a large amount of information is aggregated in an organized manner, it can be used to help a company drive its business decisions. Of course, there is too much data online to do this manually and efficiently.