This is your open course, “Data Science Algorithms In Python,” which can be useful for any skill level. We will also discover the primary concepts supporting today’s technological advances, from an introduction to programming to state-of-the-art machine learning. While studying the course, we will combine the explanation of the subject with its practical application so that you have no questions.
This is your chance to learn basic introductions to data structures and sorting techniques, as well as basic introductions to the Python programming language and many other interesting topics. This course will have everything from basic data cleaning and feature engineering to complex uses of AI, such as NLP. You will be armed with a computer and internet connection so that you will, by and large, have the tools you require following the class and use Google Colab for coding exercises. The objective is not to provide the list of the algorithms but to prepare you for solving actual problems associated with data science and other domains.
When the course “Data Science Algorithms In Python” is completed, one will have adequate knowledge of algorithms and their use. By the end, you can apply that creativity to cause novel solutions in any domain you choose.
Data Science Algorithms In Python Table of Contents:
- Introduction - 11:25
- Course Outline - 00:57
- Intro to Algorithms - 23:05
- Importance of Algorithms - 02:05
- Historical Context and Evolution of Algorithms - 01:34
- The Travelling Salesman Problem (TSP) - 1 question
- Algorithms in Python Basics (Video) - 21:33
- Introduction to Algorithmic Thinking and Problem-Solving (Video) - 31:45
- Python Algorithms with Examples - 03:41
- Algorithmic Thinking and Problem-Solving in Python Programming - 6 questions
- Data Structures in Python - 01:32
- Implement Dijkstra Algorithm in Python (Data Structures) - 2 questions
- Different Algorithms - 3 questions
- Algorithms in Data Science - 03:30
- Identify Key Steps in EDA and Their Importance - 03:16
- Algorithms Data Science Quiz - 5 questions
- Data Cleaning and Preprocessing (Importance of Input) - 02:47
- EDA: Exploratory Data Analysis - 02:52
- The Knapsack Problem - 3 questions
- CASE STUDY: Building an Algorithm for Data Science - 33:02
- EDA Quiz - 5 questions
- Project: Develop a Complete Data Science Project From Scratch - 59:55
- Well Done - 01:21
- Reflect on Journey - 03:26
Who is this course for?
- Freshmen and learners in the field of Programming and Data Science
- The main roles that could be filled are Data Scientists and Machine Learning Engineers.
- Workers who are in quest of Promotions
