Data Science: Key Programming, Stats, & Computing Skills
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.98 GB | Duration: 4h 36m
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.98 GB | Duration: 4h 36m
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.