No Math Required:
How to Master Key Data Science Skills without an Engineering Degree
In October 2012 issue, the Harvard Business Review journal referred to Data Science as one of the hottest, emerging fields of the 21st century. If you’re interested in reading this article, I am confident that you are already aware of some of the buzz surrounding data science. Why is this? The progressive innovation of social media and IoT, in particular, has led to a remarkable increase in data availability. In addition to this, processing capabilities and other hardware features have also significantly improved over time. It is not far-fetched to imagine a world where mastery of data literacy skills could become an unspoken requirement to thrive in a hyper-competitive society.
If widespread information literacy becomes a requirement of modern society, it is important to not only evangelize the merits of data science but also illustrate a pathway for accessible training that takes into consideration persons from all fields (and not just the math, engineering, and science majors).
In this article, I will guide you through a resource blueprint that will help you to rapidly acquire the rudimentary principles of data science without bogging yourself down with abstruse mathematical equations. With that being said, foundational knowledge of key math principles (especially in probability and statistics) would considerably help one to connect those intuitive associations faster, and therefore, master key data science skills quicker.
The blueprint is organized as follows:
1) Master Python
2) Master SQL
3) Master Tableau
4) Master Kafka
Feel free to save this guide and refer back to it as needed, I’ve organized each section into three levels of instruction: beginners, intermediate and advanced. So within course themes, I’d advise you to take courses in a progressive order, i.e. from beginner to advanced. However, what is not intrinsically ordered is the sequence in which you can start to learn these skills. Many people start with Python and that’s fine. But you could have just as easily started with the Apache Kafka track especially if you’re more interested in the data engineering aspect of the workflow than the data analysis and visualization portions. Furthermore, if you see yourself more in data analyst roles as opposed to data scientists or data engineers, then I’d definitely start with the SQL theme, and keep building on to my skillset from thereon.
1) Master Python
Beginner
a. (Angela Yu) 100 Days of Code: The Complete Python Pro Bootcamp for 2022
b. (Jose Portilla) 2022 Complete Machine Learning Bootcamp From Zero to Hero in Python
Intermediate
a. (Rajeev Ratan) Data Science & Deep Learning for Business 20 Case Studies
b. (Soledad Galli) Feature Engineering for Machine Learning
c. (Soledad Galli) Feature Selection for Machine Learning
d. (Samuel Hinton) Data Manipulation in Python: A Pandas Crash Course
Advanced
a. (Galli and Samiullah) Deployment of Machine Learning Models
b. (Samiullah and Galli) Testing and Monitoring Machine Learning Model Deployments
2) Master SQL
Beginner
a) (Jose Portilla) The Complete SQL Bootcamp 2022: Go from Zero to Hero
b) (Maven Analytics) SQL for Data Analysis: Beginner MySQL Business Intelligence
Intermediate
a) (Imtiaz Ahmad) Master SQL for Data Science
Advanced
a) (Maven Analytics) Advanced SQL: MySQL Data Analysis & Business Intelligence
b) (365 Careers) Python + SQL + Tableau: Integrating Python, SQL, and Tableau
4) Master Apache Kafka
Beginner
a) (Stephane Maarek) Apache Kafka Series — Learn Apache Kafka for Beginners v2
Intermediate
a) (Stephane Maarek) Apache Kafka Series — Kafka Connect Hands-on Learning
b) (Stephane Maarek) Apache Kafka Series — Kafka Streams for Data Processing
c) (Stephane Maarek and Simon Aubury) Apache Kafka Series — KSQL on ksqlDB for Stream Processing
d) (Stephane Maarek) Apache Kafka Series — Confluent Schema Registry & REST Proxy
Advanced
a) (Stephane Maarek and Gerd Koenig) Apache Kafka Series — Kafka Security | SSL SASL Kerberos ACL
b) (Stephane Maarek) Apache Kafka Series — Kafka Cluster Setup & Administration
c) (Stephane Maarek) Apache Kafka Series — Kafka Monitoring & Operations
Let’s Wrap Up
The garnering of data literacy skills is becoming increasingly more relevant in our hyper-quantitative tracking and monitoring society. The acquisition, diligence, consistent practice, and application of these skills is a salient discussion topic among professionals who are either seeking a career change or who are looking to supplement their existing field with new tools and techniques.
Knowing where to go to find accessible data science training programs is paramount if one desires to maintain relevance in this highly metrics-driven society.
This guide provides such information. It maps out affordable online data science courses that will get you up to speed with the fundamentals of data analysis, visualization, and engineering. Please note that simply taking the courses won’t make you an expert. In fact, the field of data science is so dynamic that you will find yourself in a familiar beginner experience repeatedly throughout your career. However, the continued exploration, experimentation, and application of acquired techniques will help to develop the muscle memory for these skills and contribute to the pursuit of mastery of Data Science skills.
Thank you for reading, and remember “Never say no to yourself.”
Blessed Love Family!
