Data Science with Python

Data Science with Python

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

Data Science with Python

$32.69

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1 Year Access

Intermediate

7 Students

6 hours, 20 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Data Science with Python will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirement

- This course is available to all learners of all academic backgrounds.
- Learners should be aged 16 or over to undertake the qualification.
- Some basic understanding of the English language and numeracy.

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

- PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Career path

Data Science with Python is a useful qualification to possess and would be beneficial for any related profession or industry.

Course Curriculum

Course Introduction and Table of Contents
	Course Introduction and Table of Contents	00:09:00
Introduction to Python, Pandas and Numpy
	Introduction to Python, Pandas and Numpy	00:07:00
System and Environment Setup
	System and Environment Setup	00:08:00
Python Strings
	Python Strings – Part 1	00:11:00
	Python Strings – Part 2	00:09:00
Python Numbers and Operators
	Python Numbers and Operators – Part 1	00:06:00
	Python Numbers and Operators – Part 2	00:07:00
Python Lists
	Python Lists – Part 1	00:05:00
	Python Lists – Part 2	00:06:00
	Python Lists – Part 3	00:05:00
	Python Lists – Part 4	00:07:00
	Python Lists – Part 5	00:07:00
Tuples in Python
	Tuples in Python	00:06:00
Sets in Python
	Sets in Python – Part 1	00:05:00
	Sets in Python – Part 2	00:04:00
Python Dictionary
	Python Dictionary – Part 1	00:07:00
	Python Dictionary – Part 2	00:07:00
NumPy Library - Introduction
	NumPy Library Intro – Part 1	00:05:00
	NumPy Library Intro – Part 2	00:05:00
	NumPy Library Intro – Part 3	00:06:00
NumPy Array Operations and Indexing
	NumPy Array Operations and Indexing – Part 1	00:04:00
	NumPy Array Operations and Indexing – Part 2	00:06:00
NumPy Multi-Dimensional Arrays
	NumPy Multi-Dimensional Arrays – Part 1	00:07:00
	NumPy Multi-Dimensional Arrays – Part 2	00:06:00
	NumPy Multi-Dimensional Arrays – Part 3	00:05:00
Introduction to Pandas Series
	Introduction to Pandas Series	00:08:00
Introduction to Pandas Dataframes
	Introduction to Pandas Dataframes	00:07:00
Pandas Dataframe conversion and drop
	Pandas Dataframe conversion and drop – Part 1	00:06:00
	Pandas Dataframe conversion and drop – Part 2	00:06:00
	Pandas Dataframe conversion and drop – Part 3	00:07:00
Pandas Dataframe summary and selection
	Pandas Dataframe summary and selection – Part 1	00:06:00
	Pandas Dataframe summary and selection – Part 2	00:06:00
	Pandas Dataframe summary and selection – Part 3	00:07:00
Pandas Missing Data Management and Sorting
	Pandas Missing Data Management and Sorting – Part 1	00:07:00
	Pandas Missing Data Management and Sorting – Part 2	00:07:00
Pandas Hierarchical-Multi Indexing
	Pandas Hierarchical-Multi Indexing	00:06:00
Pandas CSV File Read Write
	Pandas CSV File Read Write – Part 1	00:05:00
	Pandas CSV File Read Write – Part 2	00:07:00
Pandas JSON File Read Write
	Pandas JSON File Read Write Operations	00:07:00
Pandas Concatenation Merging and Joining
	Pandas Concatenation Merging and Joining – Part 1	00:05:00
	Pandas Concatenation Merging and Joining – Part 2	00:04:00
	Pandas Concatenation Merging and Joining – Part 3	00:04:00
Pandas Stacking and Pivoting
	Pandas Stacking and Pivoting – Part 1	00:06:00
	Pandas Stacking and Pivoting – Part 2	00:05:00
Pandas Duplicate Data Management
	Pandas Duplicate Data Management	00:07:00
Pandas Mapping
	Pandas Mapping	00:04:00
Pandas Grouping
	Pandas Groupby	00:06:00
Pandas Aggregation
	Pandas Aggregation	00:09:00
Pandas Binning or Bucketing
	Pandas Binning or Bucketing	00:08:00
Pandas Re-index and Rename
	Pandas Re-index and Rename – Part 1	00:04:00
	Pandas Re-index and Rename – Part 2	00:05:00
Pandas Replace Values
	Pandas Replace Values	00:05:00
Pandas Dataframe Metrics
	Pandas Dataframe Metrics	00:07:00
Pandas Random Permutation
	Pandas Random Permutation	00:08:00
Pandas Excel sheet Import
	Pandas Excel sheet Import	00:07:00
Pandas Condition Selection and Lambda Function
	Pandas Condition Selection and Lambda Function – Part 1	00:05:00
	Pandas Condition Selection and Lambda Function – Part 2	00:05:00
Pandas Ranks Min Max
	Pandas Ranks Min Max	00:06:00
Pandas Cross Tabulation
	Pandas Cross Tabulation	00:07:00
Matplotlib Graphs and plots
	Graphs and plots using Matplotlib – Part 1	00:06:00
	Graphs and plots using Matplotlib – Part 2	00:02:00
Matplotlib Histograms
	Matplotlib Histograms	00:03:00

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Data Science with Python