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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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