Diving Into Data Analytics

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Diving Into Data Analytics

It doesn't matter if you're a data scientist or a content editor — key aspects of your job more than likely revolve around data analytics. Check out this article to learn everything you need to know about this exponentially growing field.

· Big Data Zone ·
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Regardless of whether your job revolves around big data, you probably use data analytics every day. From checking pageviews on your website's articles using a platform like Google Analytics to checking downloads of your open-source software project on GitHub, data analytics makes its way into your life every day. 

That being said, there is a lot to learn about data analytics — and some information is more pertinent to certain use cases over others. No matter how you're using data analytics, this article can help you enhance your analytical skills and guide digital transformation. 

Check out this deep dive to learn everything you need to know about data analytics.

Data Analytics on DZone

Check out some of the top analytics-related articles on DZone to learn best practices, see how data analytics can help you develop pretty much anything, and how big data analytics can even help in the realm of scalability. 

  1. Big Data Analytics, Tools, and Tech by Mitul Makadia. Big data analytics involves extracting useful information by analyzing different big datasets. Check out a few of the many tools and technologies involved in this practice!
  2. 4 Ways to Implement Data Analytics Best Practices by Gur Tirosh. Data analytics implementation best practices include deciding on key metrics, avoiding common data modeling mistakes, and creating dashboards that actually work.
  3. How Data Analysis Can Help You Develop by Ephy Behar. Anything that can be measured can later be analyzed. Check out how data analytics can answer all of your burning questions about your users, downloads, and more!
  4. Using Data Analytics to Enhance App Development by Amit Manchanda. I told you that data analytics can be applied to pretty much anything! In this article, check out how each mobile app development domain has its own way of playing with and interpreting data.
  5. Redefining Scalability in the Era of Big Data Analytics by Rehan Ijaz. Yes, analytics can be applied to scalability, too! Scalability has long been a concern, but now it's taking on new dimensions. Here are some critical growht considerations for a landscape dominated by big data.

PS: Are you interested in contributing to DZone? Check out our brand new Bounty Board, where you can apply for specific writing prompts and win prizes! 

Analytics Abroad

Let's journey outside of DZone and check out some recent news, conferences, and more that should be of interest to data analytics newbies and experts alike.

Dive Even Deeper Into Data Analytics

DZone has Guides and Refcardz on pretty much every tech-related topic, but if you're specifically interested in Kafka, these will appeal the most to you. 

  1. The DZone Guide to Big Data: Data Science and Advanced Analytics. Explore the critical capabilities in next-generation self-service data preparation tools and dive deep into applications and languages affiliated with big data.

  2. R Essentials: The Language for Data Manipulation and Statistical Analysis. R is a highly extensible, open-source programming language used mainly for statistical analysis and graphics. Check out why it's become so popular and see exactly how it can help you analyze your mountains of data.

Jobs for the Data Analytics Aficionado

Below are a few jobs that may pique your interest if you're a data analytics aficionado. Check them out and apply today!

Data Scientist
Red Hat
Location: Bangalore, Karnataka, India
Experience: 1-3 years of working experience; experience developing with a language like Java, JavaScript, Ruby, Python, or C++; exposure to open-source development, projects, and processes is a plus!

Sr. Data Engineer
Location: Anywhere! 
Experience: 8+ years of software engineering experience with 5 years of Java applications development experiencel strong expreience implementing ETL solutions; strong database and data manipulation skills working with relational and non-relational models. 

That's all for this month's Big Data post! What would you like to learn about next time? Let us know in the comments!

big data ,data analytics ,big data analytics ,scalability ,app development ,r ,data manipulation ,statistical analytics

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