Python for Data Science

Introduction
Introductory run through of Python basics

Why Python?

04:07
What is Data Science?

04:38
Quiz 1 – Basic Introduction

IDE set up and package installation
This topic will touch on the different types of integrated development environments (IDEs), their installation and set up. We will also cover creation of virtual environments and installation of Python packages.

Must-know fundamentals – 1
In this topic, we will cover fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover topic such as functions, positional and keyword arguments, and *args and **kwargs.

Must-know fundamentals – 2
In this topic, we will continue on the fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover list, list comprehension, and tuples.

Must-know fundamentals – 3
Still building on the fundamental topics that are critical parts of a Data Scientist's toolbox, we will cover dictionaries, lambda expressions, and sets.

Packages for Data Science
In this topic, we will now touch the the most popular and powerful Python packages for data science, including Numpy, Pandas, Matplotlib and Scikit Learn

Python for Data Science

Course Content

Introduction
Introductory run through of Python basics

Why Python?

What is Data Science?

Quiz 1 – Basic Introduction

IDE set up and package installation
This topic will touch on the different types of integrated development environments (IDEs), their installation and set up. We will also cover creation of virtual environments and installation of Python packages.

Integrated Development Environment

Package Installation

Must-know fundamentals – 1
In this topic, we will cover fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover topic such as functions, positional and keyword arguments, and *args and **kwargs.

Functions

Positional and keyword arguments, *args and **kwargs

Must-know fundamentals – 2
In this topic, we will continue on the fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover list, list comprehension, and tuples.

Lists and List Comprehension

Tuples

Must-know fundamentals – 3
Still building on the fundamental topics that are critical parts of a Data Scientist's toolbox, we will cover dictionaries, lambda expressions, and sets.

Dictionaries

Lambda Expressions

Sets

Packages for Data Science
In this topic, we will now touch the the most popular and powerful Python packages for data science, including Numpy, Pandas, Matplotlib and Scikit Learn

Numpy (Numerical Python)

Pandas

Matplotlib

Scikit Learn (sklearn)

Concluding Remarks
Here, we will talk about the logical next step and briefly touch on some advanced topics to give you some possible directions.

Course Content

Introduction Introductory run through of Python basics

Why Python?

What is Data Science?

Quiz 1 – Basic Introduction

IDE set up and package installation This topic will touch on the different types of integrated development environments (IDEs), their installation and set up. We will also cover creation of virtual environments and installation of Python packages.

Integrated Development Environment

Package Installation

Must-know fundamentals – 1 In this topic, we will cover fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover topic such as functions, positional and keyword arguments, and *args and **kwargs.

Functions

Positional and keyword arguments, *args and **kwargs

Must-know fundamentals – 2 In this topic, we will continue on the fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover list, list comprehension, and tuples.

Lists and List Comprehension

Tuples

Must-know fundamentals – 3 Still building on the fundamental topics that are critical parts of a Data Scientist's toolbox, we will cover dictionaries, lambda expressions, and sets.

Dictionaries

Lambda Expressions

Sets

Packages for Data Science In this topic, we will now touch the the most popular and powerful Python packages for data science, including Numpy, Pandas, Matplotlib and Scikit Learn

Numpy (Numerical Python)

Pandas

Matplotlib

Scikit Learn (sklearn)

Concluding Remarks Here, we will talk about the logical next step and briefly touch on some advanced topics to give you some possible directions.

Student Ratings & Reviews

Introduction
Introductory run through of Python basics

IDE set up and package installation
This topic will touch on the different types of integrated development environments (IDEs), their installation and set up. We will also cover creation of virtual environments and installation of Python packages.

Must-know fundamentals – 1
In this topic, we will cover fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover topic such as functions, positional and keyword arguments, and *args and **kwargs.

Must-know fundamentals – 2
In this topic, we will continue on the fundamental topics that are critical parts of a Data Scientist's toolbox. In this first part, we will cover list, list comprehension, and tuples.

Must-know fundamentals – 3
Still building on the fundamental topics that are critical parts of a Data Scientist's toolbox, we will cover dictionaries, lambda expressions, and sets.

Packages for Data Science
In this topic, we will now touch the the most popular and powerful Python packages for data science, including Numpy, Pandas, Matplotlib and Scikit Learn

Concluding Remarks
Here, we will talk about the logical next step and briefly touch on some advanced topics to give you some possible directions.