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.
This course provides a thorough understanding of each of the key Python libraries used for data science -- NumPy, Pandas, Matplotlib and Scikit-learn, known as the Python data stack. We will perform data exploration, analysis, visualization and modeling.
In six, half-day sessions of hands-on training, you can quickly become a knowledgeable, productive, and efficient Data Science professional and earn a Stanford Technology Training Certificate of Proficiency in Data Science.
We will begin by discussing the data science process and how to effectively work through a data science problem. We'll talk about how to clean, transform, and prepare data for analysis. We will also cover descriptive and inferential statistics which will enable you to perform hypothesis testing so that you can better interpret the significance of your analysis. We will also focus on machine learning and predictive analytics. We'll discuss the various ways to measure model performance, how to select the best model for your project, and ways to refine that model.
Learning Objectives
During this course, you will have the opportunity to:
- Install Anaconda on a personal computer
- Have a clear understanding of data science and its role
- Understand the data science process
- Understand foundational descriptive statistics
- Understand foundational inferential statistics
- 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
- Interact with and manipulate data arrays and matrices using NumPy
- Perform exploratory data analysis using Pandas
- Use Matplotlib and Seaborn to perform data visualization
- Properly clean and prepare data for machine learning
- Apply machine learning on a variety of datasets
- Complete a data science project, end to end
- Understand the big picture and the importance of data science in industry, research and technology
