Data Science Studies // Focus on Statistics First! [2019 Study Plan]

Feature

Redefining the Data Science Roadmap

Shifting from platform-specific coding to a theory-based mastery of data

~2,000

Pages

3

Certifications

~2+

Years Duration

Table of Contents

• ①The Data Science Paradigm Shift
• ②Eventual Skillset
• ③Domain Study Materials
• ④Certification Roadmap
• ⑤Goals
• ⑥Project Updates

Section I

The Data Science Paradigm Shift

One of the biggest eye-openers in my journey has been the realization that Data Science fundamentally revolves around Statistics. There’s a well-established college major focused on statistics, and data science isn't entirely new; it’s a combination of traditional statistical methods with modern computing technologies, utilizing software like R and SAS for analysis.

To truly grasp data science, it's essential not only to learn programming languages but also to understand the underlying principles. As such, I've decided to revise my study plan for 2019, shifting from a language/platform-specific focus to a more theory-based approach that emphasizes core concepts and techniques.

 

Section II

Eventual Skillset

I. Domain

• →Statistics — The core analytical foundation
• →DW/BI — Data Warehouse and Business Intelligence architecture
• →Math — Supporting quantitative theory

II. Tools

• →SQL — Database querying and management
• →Tableau — Data visualization
• →R — Statistical programming
• →Python — General purpose data scripting

Section III

Domain Study Materials

A. Statistics

• →Statistics and Data Analysis (WMU) — A solid introduction to key concepts.
• →Basics of Statistics [BoS] — A great follow-on to reinforce and clarify concepts with alternative definitions.
• →Simple Data Analysis for Biologists — Useful for examples in hypothesis building.

These readings have been straightforward, providing a diverse perspective on statistics and showcasing the universality of concepts like discrete and continuous data.

B. DW/BI

• →Kimball - Data Warehouse 3rd Edition — Essential reading for understanding data warehousing.
• →Guide to Data Modeling (UW 1999) — A foundational text to support the study of Kimball.

C. Math

• →Advanced Calculus Textbook — Higher-level mathematical theory.
• →Probability and Mathematical Statistics — Core probability frameworks.

Section IV

Certification Roadmap

I am proficient in SQL and have completed the MCSA - SQL Server certification. This year, my study plan converges with the following certification goals:

Certification	Focus	Cost (USD)
Tableau Desktop Specialist	Visualization	$150
70-773 Microsoft R	Big Data	$165
98-381 Python	Intro	$127

Section V

Goals

• →Read All the Books — Roughly 2,000 pages to cover.
• →Practical Hands-On Experience — Apply the concepts learned.
• →Get the Certifications — Achieve formal recognition of my skills.

Given the extensive scope of this plan, I anticipate that it will take several years to achieve my goals. Two years feels optimistic, especially since I have already dedicated much of 2018 to studying.

Section VI

Project Updates

Update 11/14/18

Finished the WMU Statistics and Data Analysis textbook. It was a manageable read that helped clarify basic concepts while introducing new material for future study. Next, I move on to Basics of Statistics.

Update 11/18/18

Finished Basics of Statistics [BoS]. It served as an excellent counterpoint to the WMU text. Concepts like Central Limit Theory (CLT), confidence intervals, null hypotheses, and p-values are becoming clearer. I also skimmed Simple Data Analysis for Biologists to better understand hypothesis formulation.

Update 11/20/18

Began Kimball Data Warehouse 3rd Edition. I wish I had explored it sooner. My prior studies in statistics are helping me appreciate how to design data warehouses to support analytical models. The Guide to Data Modeling provided a great foundation for this, specifically regarding ERDs and essential vocabulary.

This retooled plan aligns my learning path with my long-term goals in data science, combining theoretical knowledge with practical skills.