Wednesday, 26 September 2018

SQL for Data Science

Here are some great SQL Resources to take your hashtagDataScience game to the next level: 1. Learn the Basics DataCamp – Intro to SQL for Data Science https://lnkd.in/gyb4fAr W3Schools – SQL Tutorial https://lnkd.in/gxkbRzW SQLZoo https://sqlzoo.net/ SQLTeaching https://lnkd.in/gtwvazf SQLBolt – Interactive Tutorial https://sqlbolt.com/ SQL Guide https://towardsdatascience.com/sql-cheat-sheet-for-interviews-6e5981fa797b 2. Analytical Use-Case Problems Mode – SQL Business Analytics Training https://lnkd.in/gM8nMNP Kaggle – SQL Scavenger Hunt https://lnkd.in/gU6q7wf 3. SQL & Interviews SQL – Basic Real World Scenarios https://lnkd.in/gHJeyQA How to Ace Data Science SQL Interviews https://lnkd.in/gEEAAKn 45 Essential SQL Interview Questions and Answers https://lnkd.in/gqUHGfP - - - SQL is one of the most powerful tools for many data scientists and will definitely help you build a foundation for your data science career. We'll be providing a lot more resources for you, so get ready! And remember, we're always here to help :)!

Who are looking for Python and ML online resource.

Here are some great Python Resources to learn hashtagDataScience and hashtagMachineLearning: - - - Basics of Python Programming a. Lists, Tuples, Dictionaries, Conditionals, Loops, etc... https://lnkd.in/gWRbc3J b. Data Structures & Algorithms https://lnkd.in/gYKnJWN d. NumPy Arrays: https://lnkd.in/geeFePh c. Regex: https://lnkd.in/gzUahNV Practice Coding Challenges a. Hacker Rank: https://lnkd.in/gEufBUu b. Codeacademy: https://lnkd.in/gGQ7cuv c. LeetCode: https://leetcode.com/ Data Manipulation a. Pandas: https://lnkd.in/gxSgfuQ b. Pandas Cheatsheet: https://lnkd.in/gfAdcpw c. SQLAlchemy: https://lnkd.in/gjvbm7h Data Visualization a. Matplotlib: https://lnkd.in/g_3fx_6 b. Seaborn: https://lnkd.in/gih7hqz c. Plotly: https://lnkd.in/gBYBMXc d. Python Graph Gallery: https://lnkd.in/gdGe-ef Machine Learning / Deep Learning a. Skcikit-Learn Tutorial: https://lnkd.in/gT5nNwS b. Deep Learning Tutorial: https://lnkd.in/gHKWM5m c. Kaggle Kernels: https://lnkd.in/e_VcNpk d. Kaggle Competitions: https://lnkd.in/epb9c8N - - - Hope this helps! Python is definitely a handy tool to learn right now! Feel free to share your list of resources below. Stay tuned for more :)

Kiran Vasadi

Saturday, 22 September 2018

Google Cloud Architect Exam Study Materials

Google Cloud Architect Exam Study Materials

After recently completing the Google Cloud Architect certification, I wanted to share the preparation materials that I used. Due to the newness of the exam, one challenge is there is not the same abundance of preparation material as there is for other Cloud exams. The official Exam Guide leaves a bit to be desired and there is no official Practice Exam at the current time, so I hope this material is helpful for folks preparing.

I prepared through a combination of methods outlined below (in addition to real-world GCP usage):
  • Official GCP Documentation
  • Google Cloud Next ’17 Conference Videos
  • Coursera
  • Linux Academy
This is probably a little overkill, but none of the aforementioned alone went into the depth in all of the areas that I had hoped. The answer was using the combination, and skipping areas in each that may have been redundant.
Official Documentation
This is the just the official Google Cloud Platform documentation: https://cloud.google.com/docs/.
Side-note: One thing I liked about the exam is that I didn’t feel like any of the questions asked me for point-in-time questions (i.e. what did this feature do when the exam was released versus what it may do now as of July of 2017). As a result, you can prepare well by reviewing the most up-to-date documentation without a fear that your knowledge will be too accurate. That’s kind of a silly thing to say, but other exams from other vendors do have questions where you have to answer them based on a previous point in time, even if the version of the exam and product still match.
Most of these documents are overviews or FAQs. You may want to branch off of them into deeper areas, but I felt the Product Overview and FAQs were solid. I reviewed the product documentation last of all of the study material I used, and mostly used it to fill in gaps of knowledge, though; if you review this material first, you may want to go deeper.

Overview

Compute Engine

App Engine

Container Engine

Storage Decisions

Cloud Storage

Spanner

Cloud SQL

Bigtable

Datastore

Transfer Service

Networking

Stackdriver Logging

Stackdriver Monitoring

Stackdriver Error Reporting

Stackdriver Trace

Stackdriver Debugger

Endpoints

Security Scanner

Identity and Security

Identity-Aware Proxy

KMS

Developer Tools

Resource Management

Deployment Manager

DataProc

DataFlow

Pub/Sub

Google Cloud Next Sessions

The sessions from Google Cloud Next are on YouTube now (217 of them), and if there are any areas where you feel you’d like a little more depth (e.g. App Engine, Cloud Storage, Datastore, Stackdriver), these sessions can be helpful. They’re also just all really good sessions in general. Even if you’re not preparing for the exam, I’d recommend watching as many of the videos as you can. I like to watch them at 2.0x speed for maximum productivity.

Coursera

The Coursera courses are made available by Google to partners but are available for anyone. They have a couple of options for courses, but if you are a current AWS Certified Architect Professional then there is a course available based on that, which is what I took. It compares GCP products with AWS products and is slimmed down from the other option.
I thought it was a valuable course, but lecture videos were fairly short. Total lecture time was 121 minutes, but there are quizzes and labs. The non-AWS specific course appears to be a little longer.

Linux Academy

I subscribe to Linux Academy because they have a ton of great courses in general even outside of GCP, but they also have several Google Cloud Platform courses available. Honestly, I only skimmed through the course because I had done all of the other preparation prior, but the material seems solid.

Final Thoughts

Overall, I thought the exam was done well. It’s not too long, and the questions are good Architect-level questions: it’s about being able to architect solutions, not necessarily memorizing every low-level command. With that said, a few final thoughts as you prepare for the exam:
  • As the official exam guide notes, there are Case Studies as part of the exam. I recommend preparing yourself by reading those and figuring out how to define the requirements in each and how those requirements match up to GCP services.
  • Know this decision tree well, and understand when to use what:


  • Don’t forget about services like Dataflow, Dataproc, Pub/Sub, and other Big Data concepts.
  • Like with similar exams, there will be several good options and the best option. Make sure to really dive into each question to understand specific requirements that will help you determine which is best. You really need to know when to use X and when to use Y, even if Y would kind of work.
  • Get into Google Cloud Platform and build something! There’s nothing better than real-world experience, and Google makes it really easy to get in and use the platform for free.