The Analytics Setup Guidebook Review. Everything you need to build a scalable analytics platform in 2020
I’m shocked to be telling you this next sentence: I read a free ebook from a company and actually loved it. I normally have a low opinion of free ebooks, seeing them as either overly long marketing pitches or too vague to be useful. For instance Snowflake’s For Dummies book on data warehouses is 60 pages long and yet is so dedicated to being abstract it never mentions Redshift, BigQuery or even Snowflake.
The Analytics Setup Guidebook from Holistics is a totally different story. It offers an overview of the different parts of the analytics stack: data warehouses, importing data, transforming and reporting it. (Note: it doesn’t cover more in depth applications like machine learning). Crucially it doesn’t just describe these parts abstractly, it discusses and compares the tools and services available today (it was published in July 2020).
If you’re already familiar with modern analytics and data tools there won’t be much new here. Rather than break new ground, what this book aims to do is give a lay of the land overview on the different parts of the analytics stack and how they all fit together, as well as different tools available and how they compare. Where relevant, it gives historical information so you can understand where past approaches came from, why they were used and why they may no longer be appropriate. It really succeeded in its aim for me personally, greatly helping me organise things in my head.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Intro to Data Engineering for Data Scientists: An overview of data infrastructure which is frequently asked during interviews
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.
We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.