MDN-Web-Dokumentation Mahi / DAT7 · GitLab DAT7 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (6/1/15 - 8/12/15). Instructor: Kevin Markham Course Project Python Resources Codecademy's Python course: Good beginner material, including tons of in-browser exercises.DataQuest: Similar interface to Codecademy, but focused on teaching Python in the context of data science.Google's Python Class: Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions).A Crash Course in Python for Scientists: Read through the Overview section for a quick introduction to Python.Python for Informatics: A very beginner-oriented book, with associated slides and videos.Beginner and intermediate workshop code: Useful for review and reference.Python 2.7x Reference Guide: Kevin's beginner-oriented guide that demonstrates a ton of Python concepts through short, well-commented examples.Python Tutor: Allows you to visualize the execution of Python code. What's next?
CS109 Data Science Predicting Hubway Stations Status by Lauren Alexander, Gabriel Goulet-Langlois, Joshua Wolff Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. We will be using Python for all programming assignments and projects. All lectures will be posted here and should be available 24 hours after meeting time. The course is also listed as AC209, STAT121, and E-109. Lectures and Sections Lectures are 2:30-4pm on Tuesdays & Thursdays in Science Center B First week collective section Friday 9/4/ 10am-12pm in MD G115 Section times on schedule page Staff
Data Science Cheat Sheet I will update this article regularly. An old version can be found here and has many interesting links. All the material presented here is not in the old version. This article is divided into 11 sections. 1. Hardware A laptop is the ideal device. Even if you work heavily on the cloud (AWS, or in my case, access to a few remote servers mostly to store data, receive data from clients and backups), your laptop is you core device to connect to all external services (via the Internet). 2. Once you installed Cygwin, you can type commands or execute programs in the Cygwin console. Figure 1: Cygwin (Linux) console on Windows laptop You can open multuple Cygwin windows on your screen(s). To connect to an external server for file transfers, I use the Windows FileZilla freeware rather than the command-line ftp offered by Cygwin. You can run commands in the background using the & operator. $ notepad VR3.txt & A few more things about files Other extensions include File management 3. Examples Miscellaneous 4.
Free Python for Data Science Courses from the Best Educators Last Updated 11 months ago Python is a high-level programming language that is becoming more and more popular for doing Data Science and Machine Learning. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. The capability to evaluate data using Python is analytical and it is very efficient for doing data science and machine learning. In this piece, my goal is to simply suggest the best and free online courses that will equip you to understand basics and master the key concepts to be more successful in your data science career. Below, i’ve curated a list of free online courses to learn Python for Data Science so that you can become a data scientist or data analyst to make informed (and hence better) decisions. Also, this compilation will be updated at least once every quarter. Go ahead & save this one in your pocket/ bookmarks Learn Python — Kaggle Is it right for you?
Biologische Mittel gegen Mehltau, Braunfäule, Rost Sternrußtau Alle Jahre wieder schleichen sich Pilzerkrankungen wie Mehltau, Braunfäule, Sternrußtau und Rost nach starken Regenperioden durch die Beete und Töpfe. Besonders ärgerlich bei Gemüsepflanzen! Aber gerade da will man keine Fungizide oder kupferhaltigen Mittel verwenden. Was also machen? Mittel gegen Mehltau Mehltau erkennst du an einem mehlartigen Belag auf der Blattoberseite, der die befallenen Blätter allmählich braun werden und abfallen lässt. Gegen Mehltau helfen gleich mehrere natürliche Methoden: 1. Hierfür benötigst du fettarme Frischmilch oder Molke, die du in Glasflaschen im Bioladen oder in Supermärkten bekommst. Molkemischung: 1 Tasse Molke und 1 Tasse WasserMilchmischung: 1 Tasse frische, fettarme Milch und 3 Tassen Wasser 2. Die im Ackerschachtelhalm (nicht verwechseln mit dem für Tiere giftigen Sumpfschachtelhalm!) Die Kieselsäure festigt und härtet die Pflanzenzellen und macht sie so für Schädlinge und Krankheiten weniger anfällig. Du benötigst: 3. Und so setzt du es an: 4. 1.
Courses Free Data Science Courses | Data Science Academy Free Data Science Courses The Little List of Free #DataScience Courses Free Online Data Science Courses & Data Science Training Click on the free data science courses links below: The Open Source Data Science Masters Harvard CS109 Data Science Introduction to Data Science by Jeff Hammerbacher at UC, Berkeley Introduction to Data Science @coursera Introduction to Data Science @UofWashington Data Science Course @ColumbiaUni notes by @mathbabe An Introduction to Data Science at Syracuse University ( pdf) Applied Data Science: An Introduction @SyracuseUni Data Science and Analytics at UCBerkeley Process Mining: Data Science in Action @TUEindhoven Learning from Data at California Institute of Technology Statistical Thinking and Data Analysis @MIT Data Analysis and Statistical Inference @DukeUni Introduction to Data Mining @MIT Mining Massive Datasets @Stanford Pattern Discovery in Data Mining @UoIllinois Introduction to Data Wrangling at the School of Data Making Sense of Data @Google Openintro to Statistics
Data Science Book Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. This guide discusses the essential skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. About the Author Dr. Summary 39