Python for Data Science
Python has become the go to language for machine learning and data science. This fast-paced, interactive course introduces people with experience in python and other programming languages to the fundamentals of scientific python. You will build projects like computing your own heart rate from an ECG, building a Shazam music identifier clone, and more while learning how to use core python libraries like Numpy, Scipy, Scikit, and Matplotlib.
Sridevi Pudipeddi
Lecturer at UC Berkeley
Dr. Sridevi Pudipeddi has been an educator for 10 plus years. She holds a PhD in Mathematics, and has taught at several top universities. She currently works as an instructor at the University of California Berkeley and also at the University of California Berkeley Extension. She has more than 10 years of experience in image processing, data science, machine learning and Python. She has co-authored a book on image processing and a book on Python programming. She is thrilled to be teaching Python for ML on co:rise, and is looking forward to diving into this rapidly growing area with all of you.
Python has become the go to language for machine learning and data science. This fast-paced, interactive course introduces people with experience in python and other programming languages to the fundamentals of scientific python. Over our four weeks together, we will explore the foundational pillars of the ML for python world with pandas, numpy, scipy, probability, and statistics. We will use plotting libraries like matplotlib, and ML libraries like scikit. Each week, we will work on a fun and engaging homework assignment using datasets that allow you to explore different python libraries.
We will start our journey by learning pandas, a library that gives us superpowers in manipulating and working with spreadsheet-like or tabular data. In week 2, we’ll dive a bit deeper into numpy, which is a matrix manipulation library used by Pandas and most machine learning libraries. In week 3, we’ll look at scipy, which provides us with different utilities for doing scientific computations using python. And in week 4, we’ll close out the course by brushing up on the fundamentals of probability and statistics while learning more about A/B tests.
This course is designed by people with both industry and academic experience in data science, machine learning, and deep learning. We hope you enjoy it as much as we enjoyed creating it!
- create and use the data structures such as DataFrames and Series
- reading and writing to files from and to a DataFrame
- transform a series or DataFrame
- visualize data in a Series or DataFrame
- perform statistical analysis, and
- build a larger project using all these functionalities
- Creating and populating numpy ndarray
- ndarray attributes
- ndarray manipulation
- Universal functions
- boolean operations
- build a larger project using all these functionalities
- Filtering
- Enhancement
- Edge Detection
- Thresholding
- Segmentation
- Permutation and combination
- Probability distribution
- Hypothesis testing
- Statistics
- A/B testing
I thoroughly enjoyed Sri's effectiveness and passion for teaching. Providing for open ended exploration she offers her own well-prepared, self-contained learning materials, complete with numerous examples, exercises, and resources. And, Sri really cares for each student's learning process, gives a lot of encouragement, and with delightful humor!
I have attended many training classes over the years and Sri's class was easily one of the best in my experience - highly highly recommended.
I strongly recommend Prof. Sri for Python and Machine learning courses. When taking her class, she displayed great talents in managing her classes, teaching concepts to all learning styles, developing a genuine love of learning in her students and encouraging students to explore new possibilities.
Anyone with some programming knowledge interested in becoming a Data analyst, Data scientist or a Machine learning engineer.
Software engineers skilled in programming looking for a career change into data science.
Anyone like a financial analyst, accountants etc. interested in crunching numbers.
Comfortable programming in a modern programming language like Java, Javascript, Python etc.
Experience with basic programming in python, you can write functions, loops and look up documentation for different python packages.
