Week 1,2
Introduction: Welcome and overview of the course. Introduction to the data science process and the value of learning data science.
Background: A brief background in python or unix to get you up and running.
Jupyter and Numpy: Jupyter notebooks are one of the most commonly used tools in data science as they allow you to combine your research notes with the code for the analysis. After getting started in Jupyter, we’ll learn how to use numpy for data analysis. numpy offers many useful functions for processing data as well as data structures which are time and space efficient.
Week 3,4
Pandas: Pandas, built on top of numpy, adds data frames which offer critical data analysis functionality and features.
Wee 5,6
Visualization: When exploring with and analyzing raw datasets, you often need to visualize your data to gain a better understanding of it. Also, when you reach conclusions about the data, you’ll often wish to use visualizations to present your results.
Exploratory Data Analysis (EDA): We routinely perform exploratory analysis while working with data. Here, we will review basics of EDA and some descriptive analytics methods, e.g., Correlation Analysis, Trends, Volatility, Regression, Min, Max, Mean, Median, Mode, Variance, Standard Deviation.
Week 7,8
Machine Learning: To take your data analysis skills one step further, we’ll introduce you to the basics of machine learning and how to use sci-kit learn - a powerful library for machine learning.
Week 9,10
Working with Text and Databases: You’ll find yourself often working with text data or data from databases. This day will give you the skills to access that data. For text data, we’ll also give you a preview of how to analyze text data using ideas from the field of Natural Language Processing and how to apply those ideas using the Natural Language Processing Toolkit (NLTK) library.