Data Science: Key Programming, Stats, & Computing Skills

Data science & visualization course designed for students with weak computing, statistics, or programming skills.

What you'll learn

Write python programs following correct syntax using variables, data structures, control statements, and functions(built-in and user-defined)

Properly use statistics to summarize data sets and extract actionable metrics from them

Import, analyze, clean, explore, and manipulate tabular data sets using pandas and other relevant libraries

Create insightful and story-telling graphs to visualize different types of data using Matplotlib

Requirements

No programming or statistical skills required. Course is designed to take you from scratch.

Entry to mid level computer skills

Description

A course dedicated to introducing data science & analytics concepts in the easiest, most practical way possible. The course accounts for the fact that Data science is a multi-disciplinary field that requires computing, programming, and statistical knowledge. Good computing skills mean the data analyst is able to efficiently manage the computer resources, navigate the different types of software(and hardware) and troubleshoot any issues as fast as possible. On the other hand, statistics help the data analyst understand the mathematical meaning and implications of the analysis results so that actionable insights can be developed rather than producing misleading conclusions. Lastly, good programming skills in at least 2 programming languages are key, because not always data analysts will have access to analysis tools with fancy graphical interfaces, not to mention that increasingly more complex analyses require customized code scripts to build them up.As a senior consultant in real retina analytics and a long-time educator in the field of data science, I know exactly what skills are required to succeed in this field and I know what new professionals entering the field might struggle with. Therefore, the various videos in this course are created and ordered in such a way that introduces the right foundational concepts at the right time so that more advanced concepts discussed after that are way more digestible.

Overview

Section 1: Introduction

Lecture 1 What skills you are expected to learn in this series?

Section 2: Key computing concepts

Lecture 2 Key computing concepts relevant to Data science

Section 3: Key statistical concepts

Lecture 3 Part 1 - the measures of central tendency, dispersion, and position

Lecture 4 Part 2 - Data distribution (center, spread, shape, and unusual features)

Lecture 5 Part 3 - Charts

Lecture 6 Part 4 - populations vs samples

Section 4: Python for data science

Lecture 7 Part 1 - Introduction to python

Lecture 8 Environment setup to start writing python programs.

Lecture 9 Part 2 - Data Structures

Lecture 10 Part3 - Conditionals

Lecture 11 Part 4 - Loops

Lecture 12 Practicing conditionals and loops

Lecture 13 Part 5 - Functions

Lecture 14 Part 6 - Code Tracing - Important

Lecture 15 Part 7 - How to write a program the right way!

Lecture 16 Part 7 - Practicing program writing - important

Lecture 17 Part 8 - Object-Oriented Programming

Lecture 18 Midpoint project - putting together what we learned

Section 5: Data analysis using Pandas

Lecture 19 Introduction to the Pandas library

Lecture 20 Pandas and Jupyter-notebooks setup - How to install Python libraries using pip

Lecture 21 The "Series" - manipulating tabular data

Lecture 22 DataFrames - manipulating tabular data - Part 1

Lecture 23 DataFrames - manipulating tabular data - Part 2

Section 6: Data visualization using the Matplotlib

Lecture 24 Matplotlib Library - Intro

Lecture 25 Matplotlib - Creating graphs

Section 7: Final project - putting it all together

Lecture 26 Part 1 - collecting, exploring, cleaning, analyzing, and visualizing car data

Lecture 27 Part 2 - collecting, exploring, cleaning, analyzing, and visualizing car data

Beginners wishing to enter the field of data science and analytics without statistics or programming experience.,Dedicated to introducing data science & analytics concepts in the easiest, most practical way possible. The course accounts for the fact that Data science is a multi-disciplinary field that requires computing, programming, and statistical knowledge. Therefore, the various videos are created and ordered in such a way that introduces the right foundational concepts at the right time so that more advanced concepts discussed after that are way more digestible.