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Data are the answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek? This course, part of the Data Science MicroMasters program, it will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success

 

Course Credit: C.U

Course Curriculum

Course Introduction 00:02:00
Getting to know your instructors: Leo Porter 00:03:00
Course Overview 00:04:00
Video 00:03:00
Data Science: Getting value out of data 00:15:00
Why Python for Data Science 00:05:00
Case Study: Soccer Data Analysis 00:16:00
A fun introduction to SDSC 00:05:00
How Does Data Science Happen 00:05:00
Asking the Right Question 00:03:00
Steps in Data Science 00:04:00
Step 1: Acquiring Data 00:07:00
Step 2A: Exploring Data 00:04:00
Step 2B: Pre-Processing Data 00:08:00
Step 3: Analyze Data 00:09:00
Step 4: Reporting Insights 00:05:00
Step 5: Turning Insights into Action 00:04:00
Conclusion 00:01:00
Background on Python 00:01:00
Python Overview 00:03:00
Video 00:04:00
Python: Variables 00:03:00
Python: Objects Part 1 00:04:00
Python: Objects Part 2 00:02:00
Python: Variables Quiz Explanation 00:01:00
Python: Loops 00:03:00
Python: Loop Quiz Explanation 00:02:00
Python: Conditions 00:03:00
Python: Functions 00:05:00
Function Quiz 1 Explanation 00:01:00
Python Function Quiz 2 Explanation 00:01:00
Python: Scope 00:01:00
Data Structures and Basic Libraries in Python 00:01:00
String Functions 00:08:00
Lists in Python 00:06:00
Reference Quiz Explanation 00:02:00
Tuples in Python 00:03:00
Dictionaries in Python 00:08:00
List and Dictionary Comprehension 00:03:00
Sets in Python 00:03:00
Introduction to UNIX 00:10:00
Live Code: Intro to UNIX 00:05:00
Basic UNIX Commands 00:03:00
Live Code: Basic UNIX Commands 00:06:00
Redirecting Standard IO 00:05:00
Live Code: Redirecting Standard IO 00:11:00
Pipes and Filters 00:06:00
Live Code: Pipes and Filters 00:09:00
Useful UNIX Commands for Data Science 00:27:00
Why Jupyter 00:02:00
Juypter: Getting Started 00:01:00
Live Code: Getting Started 00:13:00
Documenting Analysis with Markdown Text 00:01:00
Live Code: Documenting Analysis with Markdown Text 00:07:00
Jupyter: Additional Tips 00:01:00
VideoLive Code: Additional Tips 00:08:00
Using UNIX in Jupyter 00:13:00
Why Numpy 00:03:00
Numpy: ndarray basics 00:06:00
Numpy: ndarray indexing 00:08:00
Numpy: ndarray boolean indexing 00:03:00
Numpy: ndarray Datatypes and Operations 00:03:00
Numpy: Statistical, Sorting, and Set Operations 00:05:00
Numpy: Broadcasting 00:04:00
Numpy: Speed Test ndarray vs. list 00:02:00
Satellite Image Example 00:06:00
Live Code: Satellite Example Part A 00:16:00
Live Code: Satellite Example Part B 00:14:00
Why pandas 00:04:00
Live Code: Why pandas 00:22:00
pandas: Data Ingestion 00:03:00
Live Code: Data Ingestion 00:11:00
Pandas: Descriptive Statistics 00:04:00
Live Code: Descriptive Statistics 00:11:00
pandas: Data Cleaning 00:06:00
Live Code: Data Cleaning 00:03:00
Pandas: Data Visualization 00:02:00
Live Code: Data Visualization 00:04:00
pandas: Frequent Data Operations 00:04:00
VideoLive Code: Frequent Data Operations 00:13:00
pandas: Merging DataFrames 00:11:00
pandas: Frequent String Operations 00:09:00
pandas: Parsing Timestamps 00:14:00
pandas: Summary of Movie Rating Notebook 00:02:00
Data Visualization 00:01:00
Role of Visualization 00:07:00
Types of Visualizations 00:05:00
Matplotlib 00:03:00
World Development Indicators 00:03:00
Basic Plotting in Matplotlib: Part 1 00:09:00
Basic Plotting in Matplotlib Part 2 00:04:00
Matplotlib Additional Examples 00:02:00
Folium Example 00:04:00
Case Study 1: Cholera 00:08:00
Case Study 2: Napoleon’s March 00:05:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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