Python For Data Science: From Fundamentals To Advanced
Master Python Data Science starts with fundamentals of Python programming and walking through Numpy & Pandas.
Development,Python,Data Science
Lectures -96
Resources -5
Duration -6.5 hours
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Course Description
Python is a general-purpose programming language that is increasingly popular for data science. It is easy to learn and use, and it has a rich ecosystem of libraries and tools for data analysis and machine learning.
This online video course will teach you everything you need to know to use Python for data science, from the fundamentals to advanced topics. You will learn about the different Python libraries and tools used for data science, such as NumPy, Pandas, matplotlib, and sci-kit-learn. You will also learn how to use Python to perform common data science tasks, such as data cleaning, data exploration, and data visualization.
Python For Data Science: From Fundamentals To Advanced Course Overview
Python For Data Science: From Fundamentals To Advanced course is designed to prepare learners to use Python for Data Science.
We begin by outlining the basics of Python programming. Variables, data types, data structures (lists, sets, tuples, dictionaries), decision and looping structures, and functions are among the concepts you will study.
The handling of nested data, nested iteration, and list comprehension are all covered in detail. These subjects expand on the principles and are more complex.
The next step is to work with libraries that are designed for data analysis, data manipulation, and data science and are constructed on top of "pure" or "base" Python. The purpose of these libraries is to facilitate easier and more adaptable data science work. You will use Pandas and NumPy.
Who this course is for:
You should start learning from the basics. You should become familiar with Python (standard/base) first, and then build on that knowledge by studying the libraries that are pertinent to data science.
Beginners in Python
Those just starting out using Python for data science
Goals
What will you learn in this course:
Understanding how to build and use variables, data structures, looping structures, decision structures, and functions.
how to convert and filter nested data using list comprehension, iterate through nested data, and work with nested data.
Create and manipulate arrays using Numpy.
Create and manipulate Series and DataFrame, the two primary data structures, using Pandas. You will concentrate on the second and learn how to apply it to data science and data manipulation.
Prerequisites
What are the prerequisites for this course?
We'll start right from scratch on the Python learning journey. Prior to looking at libraries that are pertinent to data science, you will first understand the standard version of Python.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
- Introduction 01:39 01:39
- Resources (Data-sets and Notebooks)
Installation/Jupyter/Comments (Windows and MacOS/Jupyter Notebook)
8 Lectures
Python Fundamentals
14 Lectures
Decision and Looping Structures
7 Lectures
Functions
11 Lectures
Nested Data, Nested Iteration and List Comprehension
13 Lectures
Numpy
19 Lectures
Pandas
17 Lectures
Activity Solutions
4 Lectures
Instructor Details
Ermin Dedic
All Things Data.I have a passion for anything data, whether it is applying statistical methods to data more generally, or utilizing a data-driven approach in the Healthcare or Finance/Banking industries.
I studied Psychology for 6-years, including 2 years of Graduate school, where I was training to be a Child/School Psychologist. I had an opportunity to experience a blend of course work and clinical work but also recognize some of the problems facing the mental health system and graduate school system.
While I did ultimately decide to voluntarily leave the Grad program, it is via academics that I fell in love with statistics and statistical software like SPSS/SAS.
It was my Graduate school experience that solidified my interest in teaching, and where I received a lot of great feedback on my teaching and teaching style.
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