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Creating random time stamps and saving it in a file

from random import randrange import datetime import numpy as np def random_date(start,l):    current = start    while l >= 0:       curr = current + datetime.timedelta(minutes=randrange(60))       yield curr       l-=1 startDate = datetime.datetime(2013, 9, 20,13,00) f = open('filepath name\filename.txt','w') for x in random_date(startDate,500):     f.write((x.strftime(""%d/%m/%y"","" %H:%M,"))) f.close()
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80+ Free Data Science Books An archive of all O'Reilly data ebooks is available below for free download. Dive deep into the latest in data science and big data, compiled by O'Reilly editors, authors, and Strata speakers: There are several selections starting from 2012 Ebooks to 2016 Ebooks.  To download O'Reilly data ebooks,  click here .
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7 Data-Powered Business Tools That Make Big Data More Actionable

Big data is an incredibly useful platform for any business, big or small. It allows brands to delve deeper into the information and insights that fuel their products, services and processes. For example, you can use data collected on past product performance to make a more informed decision about a future launch or development cycle. That said, big data as a whole  isn’t exactly what you’d call accessible . For starters, you need to deploy the systems and processes to collect useful data. Then, you need to have a team of data analysts and scientists to sort through it all and find actionable intel. Finally, you need someone to take that practical data and put it to good use. A company executive just might not have a clear plan for, or understand the applications of, a niche data set. This doesn’t mean applying big data is impossible. It just means it’s a potentially involved and time-consuming process. Naturally, this can give organizations and decision makers enough doub
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Top 30 Big Data Tools for Data Analysis

There are thousands of Big Data tools out there for data analysis today. Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. To save your time, in this post, I will list 30 top big data tools for data analysis in the areas of  open source data tools, data visualization tools, sentiment tools, data extraction tools and databases. Open Source Data Tools 1.  Knime KNIME Analytics Platform is the leading open solution for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist. 2.  OpenRefine OpenRefine (formerly Google Refine) is a power
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