Data science / Data scientist jobs / courses

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• published: 09 Apr 2016
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Subscribe to see more. How to Be a Data Scientist: Data Science Skill Development : In general terms, Data Science is the extraction of knowledge from large volumes of data that aren't structured, which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD). Technology industry is a great career option, no matter how you look at it: Interesting work, high salaries and lots of opportunity. The data used by a Data Scientist can be both structured (metrics or raw numbers) and unstructured (e-mails, images, videos, or social data). The emerging Data Scientist needs to develop business domain knowledge. Frequently, candidates with stellar academic records fail on the job because they fail to apply their knowledge in real-world situations. Candidates must demonstrate quick aptitude for Data Science tools like R, Python, Hadoop, or SAS. Successful Data Scientists are usually convincing story tellers. They ought to be able to communicate the “story” hidden behind their findings. Visualization eases understanding. As Data Scientists often have to create algorithms to extract insights from such complex data, these “data magicians” are expected to be equipped with a variety of skills and experience levels. A Data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization. Your job might consist of tasks like pulling data out of MySQL databases, becoming a master at Excel pivot tables, and producing basic data visualizations (e.g., line and bar charts). Data Scientists enjoy a median salary of $113,000. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset. A data scientist with a software engineering background might excel at a company, where it’s more important that a data scientist make meaningful data-like contributions to the production code and provide basic insights and analyses. Data Skills to Get You Hired : 1. Basic Tools 2. Basic Statistics 3. Machine Learning 4. Multivariable Calculus and Linear Algebra 5. Data Munging 6. Data Visualization & Communication 7. Software Engineering 8. Thinking Like A Data Scientist 9. Guide to Python and R, and which one is best Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. Python : Python is a versatile programming language that can do everything from data mining to plotting graphs. Its design philosophy is based on the importance of readability and simplicity. From the The Zen of Python: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. As you can imagine, algorithms in Python are designed to be easy to read and write. Blocks of Python code are separated by indentations. Within each block, you’ll discover a syntax that wouldn’t be out of place in a technical handbook. Collect raw data Python supports all kinds of different data formats. You can play with comma-separated value documents (known as CSVs) or you can play with JSON sourced from the web. You can import SQL tables directly into your code. You can also create datasets. The Python requests library is a beautiful piece of work that allows you to take data from different websites with a line of code. It simplifies HTTP requests into a line of code. You’ll be able to take data from Wikipedia tables, and once you’ve organized the data you get with beautifulsoup, you’ll be able to analyze them in-depth. You can get any kind of data with Python. If you’re ever stuck, google Python and the dataset you’re looking for to get a solution. Also know about Python vs R and its Benefits. http://thenextweb.com/dd/2016/04/08/start-using-python-andor-r-data-science-one-best/#gref Thus, advanced analytics and Machine Learning skills are high on any industry leader’s list of wanted skills when looking for a Data Scientist to hire. The future business environment will also expect more speed of execution with Data Science projects, so future Data Scientists will not only be expected to have traditional math, statistics, and Machine Learning skills, but also sound knowledge of and experience in using data productivity tools, such as those to automate data cleaning or data modeling.