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I've been studying Python off and on for years, but really got into it when I forced myself to use it at work; that seems to be the key to learning something new, actually finding a reason to use it.  And its paying dividends as I'm asked to do more stuff with Python. For now, I'm using it primarily for scripting and automating some basic tasks.  I'm still relying heavily on Window Task Scheduler to call my Python scripts and hope to someday decouple from that and use something else for scheduling (if that is even feasible or advisable). It beats having to write very ugly batch command line or even Power Shell; my primary reason for embracing and getting comfortable with Python in the first place. I feel comfortable with my trifecta of skills:  T-SQL, Tableau and Python.  I'm learning R, but have yet to feel confident that I'm a practitioner. Find a passion, find ways to practice and you'll be happy.

It's taking a while and I just completed Chapter 6 of Kimball's 3rd Edition of Data Warehouse.  It's probably one of the best books I've ever read in the 'Data Analytics' domain.  I started in November 2018 and six months later have only cracked a portion of this book. I'll keep at it and will try to improve the pace by hitting Chapter 7 on Sunday as this is something I need to finish and focus on in my life.

One of the biggest eye-openers was the realization that 'Data Science' really means 'Statistics' and there is a time honored college major and discipline that focuses on statistics.  In many ways, data science is not new, but rather it appears to be so as it leverages modern computing technology with statistical analysis software like R or SAS. To that end, studying programming languages and writing code is one thing, but to understand why is probably equally important.  I'm spending some time to study statistics textbooks and found some decent, free material online. I've retooled my Study Plan for 2019 accordingly (original 2018 plan was more Language/Platform specific, but weak on Theory/Technique).  My Study Plan // Eventual Skillset: I. Domain: Statistics DW/BI (Data Warehouse / Business Intelligence)  Math II. Tools: SQL Tableau R Python I. Domain A. Statistics: Statistics and Data Analysis (WMU - Statistics 160 Textbook)  //

I've set my sights on my 1st Certification in my journey to the Data Analytics Realm.  With the Microsoft MTA Exam - 98-381 - Intro to Programming with Python . My Preparation Books Intro to Python 5th Edition - Mark Lutz Programming in Python - Mark Lutz Self Practice Writing Python Apps Timeline 180 Days (Dec 2017 ~ May 2018) Tentative Exam Date - Early May 2018 NOTE I won't feel competent in Python until I've spent at least a full year writing serious code.  For now I want to understand the basics and apply them at work.  I have some small projects in mind to 'cut my teeth' with Python and plan to apply them in 2018.  I consider this the 'foundation' step to at least tuck Python knowledge under my arm. Update 2/1/18 Got busy @ work and have to push back the Python thing.  Still studying, but progress is slower than I had hoped.  But keep plugging away...   Update 7/4/20 Such is life...I never got around to taking this certificat

It's time to start studying again and I've decided against pursuing .NET/Web Programming in favor of Data Science.  I've only begun researching what I need to learn to get better acquainted with 'Data Science' and for now I'll study Python Programming. I'm already pretty good with SQL and Relational Databases (SQL Server, Oracle), but there is much more and beyond Math & Statistics, I wanted to understand how to play around with unstructured data. I. Subject List So my initial study list (subject to change as I learn what I need; and in no particular order): Python Language R Language MongoDB / noSQL Big Data (Hadoop, Hive ) Cloud Tools ( Amazon S3 ) I'll also need to brush up on my Math & Stats skills (it's been a few years since Uni). II. Reading Estimate 4,000 Pages [ 5 ~ 6 books * 500 ~ 800 pages ] 10 months [ 100 pages / week ]  // ETC = October 2018 III. Resources Python Intro to Python 5th Edition - Mark Lutz P