Enroll in this training and receive a one-month complimentary e-learning subscription with access to 40+ courses.This course is provided by Big Data Trunk for Stanford Technology Training Program but a limited few seats available to the public.
Students of this class may have opportunity to be considered for Internship with Big Data Trunk.
Python is the language of data science, and this class will expose you to the most important libraries (i.e., NumPy, Pandas, Matplotlib, and Scikit-learn) that will enable you to effectively do data science using Python.
Prerequisite: Basic Python Programming
In this course, you will have an opportunity to:
- Install Anaconda on a personal computer
- Understand the various options for performing data science
- Understand the reasons for Python's popularity in data science
- Learn the primary libraries for data science in Python including NumPy, Pandas, Matplotlib and Scikit-learn
- Perform exploratory data analysis using Pandas
- Use Matplotlib and Seaborn to perform data visualization
- Prepare data for machine learning
- Apply machine learning on a variety of datasets
- Understand the data science process
- Understand the big picture and the importance of data science in business, industry, and technology
We will begin by installing Anaconda, which provides the libraries required for most data problems. We will discuss the focus and strengths of the most important libraries and how they enable data analysis and the application of machine learning to defined data problems. We will then use these libraries to perform data exploration, visualization, analysis and modeling on a variety of datasets as we work through the data science process.
Topics covered in this class include:
- Course Introduction
- Overview of data science
- Understand the reasons for Python's popularity in data science
- Installing Anaconda
- Milestone 1: Learn how to use Jupyter Notebooks
- The data science process
- Essential Python data science libraries- NumPy- Pandas- Matplotlib- Scikit-learn
- Data Visualization- Line Chart- Scatterplot- Pairplot- Histogram- Density Plot- Bar Chart- Boxplot
- Customizing Charts- Prepare data for machine learning- Milestone 2: Perform exploratory data analysis using Pandas- Milestone 3: Apply machine learning algorithms using Scikit-learn- Conclusion: Data Science in the real world, next steps
