9+ Hours of Video Instruction
While there are resources for Data Science and resources for Machine Learning, there's a distinct gap in resources for the precursor course to Data Science and Machine Learning. This complete video course fills that gap--it is specifically designed to prepare students to learn how to program for Data Science and Machine Learning with Python. This is the antidote to the …
9+ Hours of Video Instruction
While there are resources for Data Science and resources for Machine Learning, there's a distinct gap in resources for the precursor course to Data Science and Machine Learning. This complete video course fills that gap--it is specifically designed to prepare students to learn how to program for Data Science and Machine Learning with Python. This is the antidote to the over-complicated universe of these hot new, growing technologies. With this course, students will learn the fundamentals of Python and get prepared specifically for Data Science.
Noah Gift and Kennedy Behrman take students with zero programming background through enough Python to prepare them for their Data Science curriculum. Companies are looking for developers who can create insight-driven systems, as they are now becoming critical to business success. Very few professionals are adequately trained to handle both large-scale software engineering and Machine Learning/AI. This is an emerging field, and we are developing the training to meet this need in the marketplace.
Description
Notebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 9-hour video provides foundational training on the Python language for the novice or beginner programmer looking to start in the Data Science field. The video serves as the 100-level course for a Data Science undergraduate or graduate program.
The course has been designed around Colab notebook-based learning. Students would be able to run every exercise shown in the videos. The material focuses on a smaller, easier subset of Python that is needed just for Data Science coding.
Skill Level
Introduction
Python for Data Science Complete Video Course Video Training: Introduction
Lesson 1: Python Past and Future
Learning objectives
1.1 History of Python in data science
1.2 Overview of Python data science libraries
1.3 Future trends of Python in AI, ML, and data science
Lesson 2: Introduction to Colab
Learning objectives
2.1 Create your first Colab document
2.2 Manage Colab documents
2.3 Use magic functions
2.4 Understand compatibility with Jupyter
Lesson 3: Fundamentals of Python
Learning objectives
3.1 Write procedural code
3.2 Use simple expressions and variables
3.3 Work with the built-in types
3.4 Learn to Print
3.5 Perform basic math operations
3.6 Use classes and objects with dot notation
Lesson 4: Strings in Python
Learning objectives
4.1 Use string methods
4.2 Format strings
4.3 Manipulate strings: membership, slicing, and concatenation
4.4 Learn to use unicode
Lesson 5: Python Data Structures
Learning objectives
5.1 Use lists and tuples
5.2 Explore dictionaries
5.3 Dive into sets
5.4 Work with the numpy array
5.5 Use the Pandas DataFrame
5.6 Use the Pandas Series
Lesson 6: Data Conversion Recipes
Learning objectives
6.1 Convert lists to dicts and back
6.2 Convert dicts to Pandas Dataframe
6.3 Convert characters to integers and back
6.4 Convert between hexadecimal, binary, and floats
Lesson 7: Execution Control
Learning objectives
7.1 Learn to loop with for loops
7.2 Repeat with while loops
7.3 Learn to handle exceptions
7.4 Use conditionals
Lesson 8: Functions in Python
Learning objectives
8.1 Write and use functions
8.2 Learn to use decorators
8.3 Compose closure functions
8.4 Use lambdas
8.5 Advanced Use of Functions
Lesson 9: Data Science Libraries
Learning objectives
9.1 Learn NumPy
9.2 Learn SciPy
9.3 Learn Pandas
9.4 Learn TensorFlow
9.5 Use Seaborn for 2D plots
9.6 Use Plotly for interactive plots
9.7 Specialized Visualization Libraries
9.8 Learn Natural Language Processing Libraries
Lesson 10: Functional Programming
Learning objectives
10.1 Understand functional programming
10.2 Apply functions to data science workflows
10.3 Use map/reduce/filter
10.4 Use list comprehensions
10.5 Use dictionary comprehensions
Lesson 11: Lazy Evaluation
Learning objectives
11.1 Use generators
11.2 Design generator pipelines
11.3 Implement lazy evaluation functions
Lesson 12: Pattern Matching
Learning objectives
12.1 Perform simple pattern matching
12.2 Use regular expressions
12.3 Learn text processing techniques: Beautiful Soup
Lesson 13: Sort in Python
Learning objectives
13.1 Sort in Python
13.2 Create custom sorting functions
13.3 Sort in Pandas
Lesson 14: I/O in Python
Learning objectives
14.1 Read and write files: file, pickle, CSV, JSON
14.2 Read and write with Pandas: CSV, JSON
14.3 Read and write using web resources (requests, boto, github)
14.4 Use function-based concurrency
Lesson 15: Sharing Your Work
Learning objectives
15.1 Share with Github
15.2 Create Kaggle Kernels
15.3 Collaborate with Colab
15.4 Post public graphs with Plotly
Lesson 16: Case Studies
Learning Objectives
16.1 PyTest
16.2 Visual Studio Code
16.3 Vim
16.4 Ludwig (Open Source AutoML)
16.5 Sklearn Algorithm Cheatsheet
16.6 Recommendations
Summary
Summary