Welcome! We will prepare you to use Python for Data Science.
We start by illustrating Python programming fundamentals. You will learn about variables, data types, data structures (lists, sets, tuples, dictionaries), decision and looping structures, and functions.
Next, I have a whole section on how to work with nested data, nested iteration and list comprehension. These are more advanced topics that build on the fundamentals. This section makes it much easier to understand the specific data science libraries covered next.
We then turn to working with libraries that are built on top of 'pure' or 'base' Python and are used for data analysis, data manipulation, and data science. These libraries were designed to help make data science work easier and more flexible. You will work with Numpy and Pandas.
Who this course is for:
- You want to learn from the very beginning. You want to first understand Python (standard/base) and then build from there by learning the libraries relevant for Data Science.
- Beginners to Python
- Beginners to using Python for Data Science
What you'll learn
- How to create and work with variables, data structures, looping structures, decision structures, and functions.
- How to work with nested data, iterate through nested data, and utilize list comprehension for transformation and filtration.
- Numpy: create and work with arrays.
- Pandas: create and work with the two main data structures: Series and DataFrame. You will focus on the second and see how to use it for data manipulation and data science.
Our goal is to offer quality and affordable training to individuals and companies that want to outsource their training. We can easily accommodate a major purchase where we will provide all employees with their own individual access to course content.
We also have many students that are individuals who want to up-skill on their own because they are in the process of switching careers.
Course Curriculum
-
Windows - Download Anaconda Distribution(includes Python)
(2:42)
-
Windows - Install Anaconda Distribution
(4:46)
-
Windows - Setting Up Environment
(4:53)
-
Windows - Opening Jupyter Notebook
(2:18)
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MacOS - Anaconda Download and Install
(3:31)
-
MacOS - Conda Environment
(4:31)
-
MacOS -Jupyter Notebook
(2:09)
-
Jupyter Notebook Interface and Shortcuts
(8:02)
-
How to Use Markdown Cells(Adding Headers, Links and Images)
(2:04)
-
Comments - Inline and Block Comments
(4:40)
-
Python Indentation
(3:37)
-
Writing Single and Multiple Lines of Code
(3:08)
-
Understanding Variables
(2:28)
-
Main Data Types(Integer, Float, String, List, Dictionary)
(5:36)
-
Lists - How To Use
(11:09)
-
Dictionaries - How To Use
(4:25)
-
Creating A Tuple
(1:09)
-
Tuple - How To Use
(5:21)
-
Create A Set
(2:13)
-
Set - How To Use
(3:08)
-
Operators
(12:04)
-
Fill+in+Activity+(Fundamentals)
-
Introducing Functions
(4:31)
-
Functions - General Syntax
(4:17)
-
+ 1 Function
(3:00)
-
Fav Band Function
(3:34)
-
Celsius to Fahrenheit Function
(3:42)
-
Optional Return Statement(and comparing to Print Statement)
(4:48)
-
Defining a Function vs Calling(including different ways to call)
(13:20)
-
Practical, Readl World Example - Function to Get Reddit Data
(15:28)
-
Lamba Intro(Anonymous Functions)
(2:38)
-
Formal Function vs Lambda for splitting strings
(5:41)
-
Fill+in+Activity+(Looping+&+Functions)
-
Introducing you to Nested Data and Iteration
(1:58)
-
Simple Nested Example
(4:43)
-
Double Indexing
(3:41)
-
Assigning Values
(2:27)
-
List of Dicts and Dicts of Dicts Example
(4:25)
-
Nested Iteration - Iterating through List of Lists
(4:23)
-
Defining List Comprehension and Syntax
(5:04)
-
List Comprehension - Simple Examples
(4:01)
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List Comp as an Alternative to Loops
(4:04)
-
PracticalReal World Example - Using Common Mathematical Notation
(3:20)
-
Practical, Real World Example - Creating a Constrained ID
(4:52)
-
Activity - Building Intuition(Loops, Nested Data, Iteration and List Comp)
(5:06)
-
Fill+in+Activity+(Nested+and+List+Comprehension)
-
Introducing Numpy
(2:36)
-
Creating Our First Numpy Array
(3:21)
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Shaping An Array
(4:27)
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Creating a Sequence of Integers and Floats
(2:04)
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Element-Wise Operations
(4:51)
-
A Range with a Shape(arrange function with reshape function)
(2:00)
-
Numpy Indexing
(5:09)
-
Numpy Slicing
(2:46)
-
Indexing and Slicing with Breast Cancer Wisconsin Data-set
(4:44)
-
Delete Elements
(6:40)
-
Append
(3:03)
-
Insert Elements
(8:01)
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Reshape - 1 Feature
(5:32)
-
Flatten
(2:26)
-
Transpose
(2:31)
-
Concatenate
(6:36)
-
Splitting
(4:28)
-
Aggregate, Statistical Functions
(3:18)
-
Numpy+Fill-In+Activity
-
Introducing Pandas
(1:30)
-
For SAS Programmers - Analogous Terms in Pandas(Python)
(7:23)
-
Using Series As Input Into DataFrame
(3:51)
-
Comparing Series and DataFrame
(7:28)
-
Importing TSLA Dataset
(2:45)
-
Index Based Selection(iloc)
(2:35)
-
Label Based Selection(loc)
(4:17)
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Conditional Selection
(5:08)
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Summary Functions
(3:14)
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Grouping(groupby)
(3:41)
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Sorting
(2:17)
-
Checking Data Types and Converting
(3:20)
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Dealing With Missing Values
(6:37)
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Dropping Columns-Variables and Records-Rows
(2:51)
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Renaming Columns-Variables and Records-Rows
(3:40)
-
Concat Function + Pop Quiz
(14:01)
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Real World Activity - Add new Columns and Predict Stock Movement
(3:56)
Instructor
Frequently Asked Questions
When does the course start and finish?
The course has no start-dates or end-dates. You start when you want, take breaks when you want, and can complete the course when you are able to.
How long do I have access to the course?
When you buy this course, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We offer a 30 day money back guarantee. If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.
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