Introduction to Data Science with Python
- Life Time Access
- Certificate on Completion
- Access on Android and iOS App
This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.
This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs.
- Writing simple Python scripts to do basic mathematical and logical operations.
- Loading structured data in a Python environment for processing.
- Creating descriptive statistics and visualizations.
- Finding correlations among numerical variables.
- Using regression analysis to predict the value of a continuous variable.
- Building classification models to organize data into pre-determined classes.
- Organizing given data into meaningful clusters.
- Applying basic machine learning techniques for solving various data problems.
This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48]
This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45]
Introduction and Classification
This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34]
This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44]
From 0 to 1: Hive for Processing Big DataLoony Corn