Data Science Week 1

This week I focused mainly on getting set up and all the course intro stuff out of the way.   I’ve taken statistics in the past as it was a math requirement for me to finish my associates in business years ago. Though it’s been many years, I’m thankful that I still remember the basics and this isn’t all brand new to me.

Most of the course sections I’ve completed so far consist of short videos with multiple choice quizzes after each video.

Some of the key topics covered so far… constructs, operational definition, data, sample average, randomness, casual inference, benefits/downsides of surveys, control groups, blind & double blind experiments and drawing conclusions.

At the bottom of this post is a list of all the lessons. Within most of the lessons are multiple videos and quizzes (usually 20+) so the list below really just lists the general categories. Anything that is crossed out  means I’ve completed that section.

I tend to listen to videos at 1.5x the speed to get through them fast and danger like Ill Mitch. After a few minutes my mind normalizes the quicker pace of the video and processes it just fine. Helps me plow through content faster especially handy when I’m short on time.

I’m excited to get into the SQL & Python stuff. I’ve used very basic SQL at work to pull data years ago. Right now I don’t use SQL at all because we’re a MERN stack ( MongoDB, Express, React, Node.js) development team. No SQL database only MongoDB. Writing queries against a MongoDB is different than SQL.   Anyway I’m getting off topic…

My goal is to get through everything up to Python within the next 3 weeks then spend the next month working on SQL & Python. Not sure if that’s realistic or not.  If I can get done with the course a bit early I can spend any remaining time to participate in the community (forums, slack & facebook)

Intro to Descriptive Statistics
Intro to Research Methods
PS 1A: Intro to Research Methods
OPT PS 1B: Additional Practice
Visualizing Data
PA 2A: Visualizing Data
OPT PS 2B: Additional Practice
Google Spreadsheet Tutorial
Central Tendency
PS 3A: Central Tendency
OPT PS 3B: Additional Practice
Variability
PS 4: Variability
Standardizing
PS 5A: Standardizing
OPT PS 5B: Additional Practice
Normal Distribution
PS 6: Normal Distribution
Sampling Distributions
PS 7: Sampling Distributions
Advanced concepts: Python & SQL
Why Python Programming
Data Types & Operators
Control Flow
Functions
Scripting
Basic SQL
SQL Joins
SQL Aggregations
SQL Subqueries & Temproary Tables
SQL Data Cleaning