Machine Learning for Data Analysis

Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. What do these events all have in common? It’s artificial intelligence that has guided all these decisions. And the force behind them all is machine-learning algorithms that use data to predict outcomes.

Now, before we look at how machine learning aids data analysis, let’s explore the fundamentals of each.



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Early Access to Course 2 of Our Data Product Manager ND Launches Today

In keeping with our mission to upskill the workforce of tomorrow, we’re excited to announce that we’re giving early access to the second course in our upcoming Data Product Manager Nanodegree program.

Access to the first course, Applying Data Science to Product Management, has been available since June. As of today, Establishing Data Infrastructure, the second course in the Nanodegree program, is also open for enrollment at 30% off!



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What To Expect as an Entry Level Data Engineer

Big data is growing fast. According to IBM, 2.5 quintillion bytes of data are generated every single day. In fact, big data is growing faster than companies can keep up with it. Last year, the CTO of IBM said that only 1 out of every 10 big data projects gets released into production.

What this shows is a disconnect between the vast amounts of available data and the people with the skills to analyze that data. Enter the data engineer.



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Data Product Managers: A Must-Have in the Era of Big Data

It’s no surprise that data product manager roles are increasingly coveted in the tech industry as the total amount of data created worldwide is expected to reach 175 zetabytes by 2025. After all, someone needs to manage the reams of data being created on a daily basis.

If you’re wondering what a data project manager does and why they’re so important, this blog post will fill you in.



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Why Non-Programmers Should Learn SQL

In today’s digital age, information is constantly being created, collected, stored, and analyzed. Every aspect of customer behavior can be translated into data points and interpreted by different technologies. With the unstoppable expansion of the data universe, organizations need more of their employees to have the analytical skills to comprehend the ubiquitous amount of data and transform it into actionable insights.

To analyze data, it first needs to be extracted from databases. Currently, the most popular language used for querying and manipulating databases is SQL. While we often think of SQL as a tool used in technical roles, such as programmers and data scientists, many people today in “non-technical” roles such as marketing and sales are being trained in SQL to better leverage data and extend their professional capabilities. 



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Why You Should Learn SQL to Boost Your Career

Out of 13 million new jobs that have been created in the United States in the last 10 years, over 8.5 million have required skills in technology. And as the future of work moves forward, the tech skills necessary to succeed in these new roles will only become more advanced. Skilled workers need to be able to work with technology beyond an internet browser or word processing application.

Jobs like Human Resources (HR), that never seemed to need technology before, are now specifically looking for applicants with skills in data analysis. In a survey done by SHRM, over half of survey responders require data analysis when hiring for their HR department.



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