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Development Data Science Python

Data Science with Python for People in a Hurry.

Learn the very basics data science with Python with hands on examples.
Rating: 3.5 out of 53.5 (2 ratings)
25 students
Created by Kelechi Ikegwu
Published 1/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Understand the Basics of Data Science.
  • Learn how to debug and fix machine learning models.
  • Know and understand how to use popular data science tools.

Course content

8 sections • 40 lectures • 3h 2m total length

  • Preview01:00
  • How to Navigate Through This Course.
    00:31
  • Installing Software on Mac OS.
    15:21
  • Installing Software on Windows OS
    10:00
  • Cram Session with Python
    00:22

  • Section Resources [The Basics of Data Science.]
    00:08
  • Preview07:02
  • Creating Statistical Visualizations.
    08:03
  • The Machine Learning Paradigm.
    02:40
  • Splitting your dataset (Part 1).
    05:32
  • Building Our Pipeline.
    14:45
  • Trying out a Few Machine Learning Models with our Pipeline.
    01:54
  • Splitting our Dataset (Part 2).
    05:36
  • Lesson Recap.
    01:33

  • Section Resources[Data Is Everything.]
    00:08
  • Tougher Dataset
    06:46
  • Running Our Pipeline.
    02:06
  • How do I get rid of ?
    09:09
  • Rerunning our Pipeline
    02:55
  • How to get better results.
    08:42

  • Section Resources[Really! Data! Data! Data!]
    00:08
  • Building a Dataset
    08:41
  • Running our Pipeline
    01:44
  • Preprocessing our Data and Rerunning our Pipeline.
    06:12
  • Lesson Recap.
    02:49

  • Section Resources[Finding the Right Model Parameters.]
    00:08
  • Setting up our Data and Pipeline
    02:54
  • Searching Over Parameters
    12:58
  • Lesson Recap.
    03:05

  • Section Resources and Exercises [Debugging in Data Science]
    00:08
  • Setting Up Our Data and Creating a Not-so-good Model.
    02:13
  • Debugging our Not-so-good Model
    13:34
  • Debugging our Model Hyper-parameters.
    05:36

  • Section Resources and Exercises [Creating Data Science Presentations]
    00:08
  • Preview02:00
  • Creating a Presentation.
    08:41
  • The Exercise.
    00:39
  • The Solution.
    04:04
  • Lesson Recap.
    01:51

  • Where to go from here?
    00:17

Requirements

  • Know how to use a computer (MAC or Windows).
  • Exposed to Computer Programming (in any language) preferably Python.
  • Know Basic Statistics (mean, std, median, etc.)

Description

This is not your typical Data Science course.

We aim to show you the very basics of Data Science with practical examples. We require you to be familiar computer programming in general but not an expert. This course includes hands on lectures demonstrating fundamental concepts of data science, and useful python libraries for data science. For each hands on lecture we provide you with the code, documentation, and excercises. 

We present the information in an understandable format, so if you have no experience with Data Science but are interested in learning more about it in a short time period you are in the right place.

Why should you take this course?

  • You are a entrepreneur or business owner and you want to learn about how Data Science and/or Analytics can make your business thrive.
  • You are a student and you feel like you really do not understand the basics of Data Science.
  • You dread taking a course about Python and Data Science because it sounds too complicated and just want a simple explanations
  • You want to see how to implement projects in data Science

This course Introduces:

  • Jupyter Environment
  • Preprocessing Data
  • Statistical Visualizations
  • Machine Learning
  • Debugging Machine Learning Models
  • Creating Data Science Presentations

Who this course is for:

  • Anyone who wants to learn the basics of modern data science.
  • This course is NOT for someone who has no experience computer programming.

Instructor

Kelechi Ikegwu
Data Scientist
Kelechi Ikegwu
  • 3.5 Instructor Rating
  • 2 Reviews
  • 25 Students
  • 1 Course

Kelechi Ikegwu has a BS and is currently pursuing his PhD in Informatics at The University of Illinois at Urbana Champaign. He has worked at NASA Research Centers, other government facilities, and academia in positions all related to Data Science. 

Over the course of his career he has developed a skill set in analyzing and gaining insights from data using computational methods. Feel free to contact him on LinkedIn for more information about him.

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