Introduction to Data Science using Python (Module 1/3)
- This course does not have any pre-requisities. All you need is a Windows or a MAC machine.
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.
The other modules will cover more complex concepts.
- Anyone who wants to learn about Data Science from absolute scratch.
- Anyone who wants to switch or make a career in Data Science and Analytics
- Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.
- What is Data Science and Machine Learning?
- Getting Started with Python and Scikit-Learn
- Types of Machine learning algorithms
- Playing Around with Anaconda and Jupyter
- Playing with some Python Code
- Fitting a Machine Learning Model (KNN Algorithm) - Part 1
- Fitting a Machine Learning Model (KNN Algorithm) Part 2
- Fitting a Machine Learning Model (Logistic Regression Algorithm)
- Validation using Model Selection (Train and Test)
- Finalizing Your optimum algorithm (K-Fold Cross Validation)
- Data Science - What's Next?
I am a coder, manager, educator and a gamer. I love data and analytics. In my day job, I work with database technologies including SQL , Big Data and Tableau. I am passionate about technologies and love coding and managing teams. In my spare time I like to teach Big Data analytics, Databases, Programming etc. I am currently working on certain machine learning and Data Science projects and love to explore more in the Statistics field.