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 wrangling" was an interesting phrase to hear in the machine learning (ML) presentations at Microsoft Ignite. Interesting because data wrangling is from business intelligence (BI), not from artificial intelligence (AI). Microsoft understands ML incorporates concepts from both disciplines. Further discussions point to another key point: Microsoft understands that business-to-business (B2B) is just as fertile for ML as business-to-consumer (B2C). ML applications with the most press are voice, augmented reality and autonomous vehicles.
So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. If you already have some background, or don't need all the components, feel free to adapt your own paths and let us know how you made changes in the path.
Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What is flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms; they should apply machine learning just as liberally in the early cleansing stages instead of relying on people to grapple with gargantuan data sets, according to Andy Palmer, co-founder and chief executive officer of Tamr Inc., which helps organizations use machine learning to unify their data silos.