Data Analytics Resources for Students and Recent Grads

Data Analytics Resources for Students and Recent Grads

You can learn some of the top platforms at home and for FREE!

Congratulations upcoming grads! You’ve sat through countless hours of classes, written your sorting algorithms from scratch, analyzed your lab data in Excel, and you’re about to receive that piece of paper stating that maybe you know a thing or two about your STEM fields.

Regardless of your field, I don’t need to tell you that with the new volume, veracity, velocity, and availability of data today just about every industry is integrating data collection and analytics into everything that they do.

This is happening even in fields that are outside of the cloud compute giants! Civil Engineers going into construction will need to learn to use data from previous contracts to forecast risk and make financial projections for a customer. Cars are becoming computers on wheels and are producing more data and telemetry than ever before. Car designers can use this data to improve future models and make preventative maintenance predictions. Just about every major U.S. government body and defense/intelligence agency that you could possibly work for has released new strategies stating that data and analytics are top priorities for staffing and training moving forward.

If you’re like me, right after I graduated, then you’re probably thinking “I’ve just been using Excel to analyze maybe a few thousand rows for the past few years, how the heck do I get started with all this crazy data stuff?”

Have no fear! I’ve strategically navigated and touched just about every major enterprise analytics tool available during both my professional and graduate studies and I’ve compiled a list of platforms that are:

  1. Some of my favorites, and

  2. Very friendly to students who still have access to their .edu email.

Some of them even have very friendly free-tiers regardless of your student status.

Qlik

Qlik provides end-to-end data analytics solutions for huge enterprise customers like Cisco, Samsung, and Deloitte. I personally have used the tool and it will likely meet 99% of your data analytics needs for your organization. With a comprehensive licensing model and scalable backends, it’s not unusual to see the suite at a lot of major enterprise companies and Federal agencies. Even if your future employer isn’t using Qlik-specific products, the skills and foundational knowledge you would get from building on the platform will likely translate across other analytics platforms as well.

Currently they have an incredibly permissive Qlik Academic Program that is targeted at students and educators. The program offers one year of:

  • Access to the Qlik Continuous Classroom which provides education on the Qlik platform as well as foundational knowledge for anyone to get started in data and analytics.

  • Free access to their enterprise Qlik Sense platform to practice your data ETL and visualizations, all in your browser! No download or installation required.

  • Pathways to get Certified and Qualified on their Qlik products and foundations of data and analytics. These are valuable, industry-recognized milestones to provide new trust in your resume.

If you’re looking for a place to get started in enterprise-sized Business Intelligence and analytics, it’s hard to beat Qlik!

Databricks

Databricks is widely recognized as the go-to platform for data science and machine learning on the biggest and most complex datasets. Through a combination of the Apache Spark engine and distributed high-performance compute clusters, users can run ETL and analysis jobs in seconds to minutes that would usually take hours to days. It’s no wonder the platform is trusted by major clients like Shell and Nielsen.

Currently, they offer the Databricks University Alliance program which is one of the most generous offerings I’ve seen from platforms operating in these domains. Signing up with your .edu email gets you:

  • Free access to the Databricks Community Edition: a sandbox space that should allow you to exercise just about all functionality in the platform.

  • Self-paced Databricks courses. This is the most generous part of the offering, as these courses would otherwise cost thousands of dollars if you were purchasing them as a company or independent party.

The Databricks platform allows you to interact and push compute jobs to your clusters using Scala (a higher-level Java derivative), Python, pure SQL, and even R! It is the perfect way to get started all the way from the basics of Data Science all the way to Machine Learning operations on big datasets.

Tableau

Another major player in Business Intelligence and Visualization is Tableau. Where platforms like Qlik may fall behind on visualization, Tableau has been the king of this domain for over a decade. The platform is a trusted BI component for many big players like Verizon and Charles Schwab. If you need a beautiful, awesome visualization or interface then Tableau is the platform for you.

Currently they are offering through their Tableau for Students program:

  • A free one-year license to their power Tableau Desktop and Tableau Prep software.

  • Unlimited access to the Tableau Public platform (a lot of the same functionality, only your visualizations have to be public on the web).

  • Comprehensive free at-home learning resources.

  • 20% off the cost to take certification exams.

If your interests lie primarily in crafting beautiful data visualizations then Tableau is the platform for you!

Power BI

Microsoft has its own BI and visualization platform, PowerBI, for customers who really lean on the company. PowerBI boasts proven patterns for BI and visualization of data derived from other Microsoft applications and platforms such as Azure and Dynamic CRM. It’s the preferred BI platform for companies like Adobe and Nokia.

  • Microsoft has a permissible model even if you’re not a student! The only catch is that the browser version of Power BI Pro is $10 a month while Power BI desktop is free… if you’re running a Windows PC.

  • Microsoft also maintains a wealth of learning material including free material to help you achieve the DA-100 Certified: Data Analyst Associate certificate.

PowerBI is a strong, entirely free option that’s well-respected in the industry for learning the ropes around data analysis.

Colaboratory

I don’t think Google needs an introduction; it should come as no surprise that they are an industry leader in Data Science and Machine Learning. Only a few years ago they productized their internal Jupyter Notebook platform and made it available for public release as Colaboratory.

Regardless of your status as a student, Colaboratory is available for free usage to anyone with a Google account, in a model similar to Google Docs. It offers one-click deployment of Jupyter python notebooks run and hosted on their servers, no credit card required!

  • Connection to GPU/TPU runtimes

  • Connect to your Google Drive to work on larger datasets

  • In-app search library for popular code snippets

  • Publish your Colaboratory apps to Github to show them off in one-click deployments

  • Much more!

Colaboratory lowers the bar immensely for getting started with Python-based data science and machine learning by providing reproducible environments to use and exercise tools and frameworks like Tensorflow, Keras, and Scikit-Learn!

Deepnote

One of the newest entries to the Data Science and Machine Learning set of tools is Deepnote.

Deepnote hopes to be the data science platform for you and your team, offering some of the most comprehensive workflows I’ve seen in the industry. While the company is still growing, they offer one of the most generous free-tiers I have ever seen in a platform. You can even run your free-tier compute for over 700 hours/month, making it effectively free to run a basic python application indefinitely!

  • Upload your own data or use some of their pre-built connectors for popular datastores like S3 or MongoDB.

  • Run your own Docker container environments pulled from any popular image registry. For those who don’t understand what that means, learn a little Docker 101.

  • Connect your project to a Github repository to push/pull proper versioning.

  • Invite collaborators who can modify and comment on your shared work!

While Deepnote is a smaller, new start to the Data Science community, I am very impressed with what they’ve built and encourage everyone to check them out!

This is a cross-post from my LinkedIn. Read and connect with me on there: https://www.linkedin.com/pulse/data-analytics-resources-students-recent-grads-nic-acton/

 
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