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One question that always comes up when students are first being introduced to such tables is: “Do I just interpolate linearly between the nearest entries on either side of the desired value?” Not that these exact words are used, typically.

Current R jobs Job seekers: please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Core Data Science @Facebook – PhD Intern (London 2018) Tal Galili London England, United Kingdom 21 Dec 2017 Full-Time R Shiny Dashboard Engineer in Health Tech Castor EDC – Posted by Castor EDC Amsterdam-Zuidoost Noord-Holland, Netherlands 12 ...

So when I look back at a very busy 2017 and think about how far we’ve come in our transformation as a company, I take pride in knowing that the numbers all add up — this past year was rich with accomplishments and progress.

The New Yorker wrote an article about a retired reporter using algorithm to sort through murder statistics to identify link murders to the same serial killer.

For the past seven years, he has been collecting municipal records of murders, and he now has the largest catalogue of killings in the country—751,785 murders carried out since 1976, which is roughly twenty-seven thousand more than appear in F.B.I.

Blogging and social media for introverts – How to spot an introvert You may have seen David Robinson’s recent post encouraging R users to start blogging.

Simply say “we’re all going to take part in a team bonding exercise” and watch to see whose eyes point to the floor while everyone else leaps to their feet.How about “icebreakers”?

This blog started as a way towrite short pieces about using R for finance and promote mybook in an organic way.Today, I’m very happy with my decision.

She decided to leave the corporate world and move into freelancing to take advantage of the flexibility and work-life balance it offers.

When it comes down to the absolute basics, marketing is all about one thing – getting the right offer in front of the right person at exactly the right moment.

7 awesome data science newsletters to keep you informed

In a fast-paced and rapidly growing industry like data science, keeping up is essential.

Data Science Weekly Since 2013, Hannah Brooks (business strategy expert) and Sebastian Gutierrez (data viz & D3.js ninja) have been compiling Data Science Weekly, hand selecting the very best articles and sending them out each Thursday morning.

Each issue of Data Science weekly starts with their Editors Picks fo the best articles from the previous week, followed by longer lists for data science articles and tutorials, providing the perfect balance to help you keep your finger on the data science pulse.

Many data scientists and software engineers learned to program from O'Reilly publications, with their distinctive animal covers and their quality considered second to none.

Since as far back as the 1980's, O'Reilly have been publishing books on data, however in the last 6 years with the introduction of their Strata + Hadoop World conferences, O'Reilly has become a powerhouse in the data world.

Because of what they do, the articles tend to cover the more analysis topics (including lots of SQL) and because of this I find them highly practical - I'm always learning something from the articles they post that can improve my day-to-day work.

Our interactive learning enviroment helps you learn by using real-life data sets, and we teach you the theory behind the algorithms to give you a solid foundation.

Our blog posts contain short tutorials designed to help you learn data science, improve your skills and find a data science job.

Here are a few of our favorite and most read posts: How to actually learn data science How to get into the top 15 of a Kaggle competition using Python Building a data science portfolio: Storytelling with data Python & JSON: Working with large datasets using Pandas To subscribe, simply fill in your email in the box at the bottom of the page.

The key to building a data science portfolio that will get you a job

An example would be analyzing ad click rates, and discovering that it's much more cost effective to advertise to people who are 18 to 21 then to people who are 21 to 25 -- this adds business value by allowing the business to optimize its ad spend.

Try to pick something that interests you personally -- you'll produce a much better final project if you do Pick a question to answer using the data Explore the data Identify an interesting angle to explore Clean up the data Unify multiple data files if you have them Ensure that exploring the angle you want to is possible with the data Do some basic analysis Try to answer the question you picked initially Present your results It's recommended to use Jupyter notebook or R Markdown to do the data cleaning and analysis Make sure that your code and logic can be followed, and add as many comments and markdown cells explaining your process as you can Upload your project to Github It's not always possible to include the raw data in your git repository, due to licensing issues, but make sure you at least describe the source data and where it came from The first part of our earlier post in this series, Analyzing NYC School Data, steps you through how to create a complete data cleaning project.

Try to pick something that interests you personally -- you'll produce a much better final project if you do Explore a few angles in the data Explore the data Identify interesting correlations in the data Create charts and display your findings step-by-step Write up a compelling narrative Pick the most interesting angle from your explorations Write up a story around getting from the raw data to the findings you made Create compelling charts that enhance the story Write extensive explanations about what you were thinking at each step, and about what the code is doing Write extensive analysis of the results of each step, and what they tell a reader Teach the reader something as you go through the analysis Present your results It's recommended to use Jupyter notebook or R Markdown to do the data analysis Make sure that your code and logic can be followed, and add as many comments and markdown cells explaining your process as you can Upload your project to Github The second part of our earlier post in this series, Analyzing NYC School Data, steps you through how to tell a story with data.

If you're having trouble finding a good dataset, here are some examples: Lending club loan data FiveThirtyEight's datasets Hacker news data If you need some inspiration, here are some examples of good data storytelling posts: Hip-hop and Donald Trump mentions Analyzing NYC taxi and Uber data Tracking NBA player movements Lyrics mentioning each primary candidate in the 2016 US elections (from the first project above).

Here are the steps you'll need to follow to build a good end to end project: Find an interesting topic We won't be working with a single static dataset, so you'll need to find a topic instead The topic should have publicly-accessible data that is updated regularly Some examples: The weather Nba games Flights Electricity pricing Import and parse multiple datasets Download as much available data as you're comfortable working with Read in the data Figure out what you want to predict Create predictions Calculate any needed features Assemble training and test data Make predictions Clean up and document your code Split your code into multiple files Add a README file to explain how to install and run the project Add inline documentation Make the code easy to run from the command line Upload your project to Github Our earlier post in this series, Analyzing Fannie Mae loan data, steps you through how to build an end to end machine learning project.

If you need some inspiration, here are some examples of good end to end projects: Stock price prediction Automatic music generation Explanatory Post It's important to be able to understand and explain complex data science concepts, such as machine learning algorithms.

Create an outline of your post Assume that the reader has no knowledge of the topic you're explaining Break the concept into small steps For k-nearest neighbors, this might be: Predicting using similarity Measures of similarity Euclidean distance Finding a match using k=1 Finding a match with k > 1 Write up your post Explain everything in clear and straightforward language Make sure to tie everything back to the 'scaffold' you picked when possible Try having someone non-technical reading it, and gauge their reaction Share your post Preferably post on your own blog If not, upload to Github If you're having trouble finding a good concept, here are some examples: k-means clustering Matrix multiplication Chi-squared test Visualizing kmeans clustering.

If you need some inspiration, here are some examples of good explanatory blog posts: Linear regression Natural language processing Naive Bayes k-nearest neighbors Optional portfolio pieces While the key is to have a set of projects on your blog or Github, it can also be useful to add other components to your project, like Quora answers, talks, and data science competition results.

good place to look is your own portfolio projects and blog posts Whatever you pick should fit with the theme of the meetup Break the project down into slides You'll want to break the project down into a series of slides Each slide should have as little text as possible Practice your talk a few times Give the talk!

Upload your slides to Github or your blog If you need some inspiration, here are some examples of good talks: Computational statistics Scikit-learn vs Spark for ML pipelines Analyzing NHL penalties Data science competition Data science competitions involve trying to train the most accurate machine learning model on a set of data.

Top 75 Data Science Blogs And Websites For Data Scientists

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