Course Overview
Starting on Monday, December 9th, 2024
In today’s data-driven world, the ability to clean, transform, and prepare data is an essential skill for anyone working in analytics, data science, or machine learning. Data wrangling forms the backbone of every successful data project, turning raw, messy datasets into structured, meaningful insights.
This course empowers you with the tools and techniques to confidently handle real-world data challenges.
Learning Outcomes
By the end of this course, participants will have mastered essential data wrangling techniques using Python libraries like pandas and NumPy. They will be skilled in cleaning, transforming, and structuring datasets, handling missing values and inconsistencies, and performing advanced operations like string manipulations and DateTime transformations. Additionally, participants will learn to preprocess data for machine learning with Scikit-Learn.
Schedule
| START DATE | MONDAY, December 9th, 2024 |
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| Periodicity | Meet Monday to Thursday for 4 evenings |
| Schedule | From 7 PM to 9 PM PST |
| Session Location | Zoom (Supportvectors facility is also available for attendance) |
| Session Type | Evening interactive Zoom sessions |
Prerequisites
Python
Syllabus details
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The curriculum of this Data Wrangling with Python course is designed to provide you with the essential skills for working with real-world data. The course starts with an introduction to pandas and NumPy, covering basic data manipulation techniques like column and row operations. You’ll then move on to more advanced topics such as data assembly, string manipulation, and DateTime handling. The curriculum also focuses on tidy data strategies and missing value treatment, key for preparing clean datasets. In the final phase, you’ll learn data preprocessing for machine learning, preparing you for hands-on projects and real-world applications.
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Start the course with an introduction to Python data tools. Set up your environment and explore foundational concepts in pandas and NumPy. Learn to manipulate rows and columns for data preparation and analysis.
Topics Covered:
- Introduction to the course
- Setting up your Python environment
- Basics of pandas and NumPy
- Column and row operations
Dive into advanced data assembly techniques and transform your data with string and DateTime manipulations. Strengthen your understanding with practical exercises and Homework 1.
Topics Covered:
- Data assembly techniques
- String manipulation in pandas
- DateTime manipulation in pandas
- Homework 1: Practice and reinforce concepts
Learn how to clean and restructure messy datasets into a tidy format. Address missing data effectively to prepare clean datasets for analysis. Practice your skills with Homework 2.
Topics Covered:
- Tidy data strategies
- Restructuring messy data
- Missing value treatment
- Homework 2: Apply and refine skills
Conclude the course with advanced data techniques and preprocessing for machine learning.
Topics Covered:
- Split-apply-combine technique in pandas for advanced data analysis.
- Data preprocessing for machine learning using Scikit-Learn.
- Final Q&A and wrap-up of course concepts.
The teaching faculty for this course comprises the instructor, a supportive staff of teaching assistants, and a course coordinator. Together, they facilitate learning through close guidance and 1-1 sessions when needed.
Teaching Faculty
Asif Qamar
Chief Scientist and Educator
Background
Over more than three decades, Asif’s career has spanned two parallel tracks: as a deeply technical architect & vice president and as a passionate educator. While he primarily spends his time technically leading research and development efforts, he finds expression for his love of teaching in the courses he offers. Through this, he aims to mentor and cultivate the next generation of great AI leaders, engineers, data scientists & technical craftsmen.
Educator
He has also been an educator, teaching various subjects in AI/machine learning, computer science, and Physics for the last 32 years. He has taught at the University of California, Berkeley extension, the University of Illinois, Urbana-Champaign (UIUC), and Syracuse University. He has also given a large number of courses, seminars, and talks at technical workplaces. He has been honored with various excellence in teaching awards in universities and technical workplaces.
Chandar Lakshminarayan
Head of AI Engineering
Background
A career spanning 25+ years in fundamental and applied research, application development and maintenance, service delivery management and product development. Passionate about building products that leverage AI/ML. This has been the focus of his work for the last decade. He also has a background in computer vision for industry manufacturing, where he innovated many novel algorithms for high precision measurements of engineering components. Furthermore, he has done innovative algorithmic work in robotics, motion control and CNC.
Educator
He has also been an educator, teaching various subjects in AI/machine learning, computer science, and Physics for the last decade.
Teaching Assistants
Our teaching assistants will guide you through your labs and projects. Whenever you need help or clarification, contact them on the SupportVectors Discord server or set up a Zoom meeting.
Kate Amon
Univ. of California, Berkeley
Kayalvizhi T
Indira Gandhi National Univ
Ravi Sati
Kalasalingam Univ.
Harini Datla
Indian Statistical Institute
Kunal Lall
Univ. of Illinois, Chicago
In-Person vs Remote Participation
Plutarch
Education is not the filling of a pail, but the lighting of a fire. “For the mind does not require filling like a bottle, but rather, like wood, it only requires kindling to create in it an impulse to think independently and an ardent desire for the truth.
Our Goal: build the next generation of data scientists and AI engineers
The AI revolution is perhaps the most transformative period in our world. As data science and AI increasingly permeate the fabric of our lives, there arises a need for deeply trained scientists and engineers who can be a part of the revolution.
Over 2250+ AI engineers and data scientists trained
- Instructors with over three decades of teaching excellence and experience at leading universities.
- Deeply technical architects and AI engineers with a track record of excellence.
- More than 30 workshops and courses are offered
- This is a state-of-the-art facility with over a thousand square feet of white-boarding space and over ten student discussion rooms, each equipped with state-of-the-art audio-video.
- 20+ research internships finished.
Where technical excellence meets a passion for teaching
There is no dearth of technical genius in the world; likewise, many are willing and engaged in teaching. However, it is relatively rare to find someone who has years of technical excellence, proven leadership in the field, and who is also a passionate and well-loved teacher.
SupportVectors is a gathering of such technical minds whose courses are a crucible for in-depth technical mastery in this very exciting field of AI and data science.
A personalized learning experience to motivate and inspire you
Our teaching faculty will work closely with you to help you make progress through the courses. Besides the lecture sessions and lab work, we provide unlimited one-on-one sessions to the course participants, community discussion groups, a social learning environment in our state-of-the-art facility, career guidance, interview preparation, and access to our network of SupportVectors alumni.
Join over 2000 professionals who have developed expertise in AI/ML
Become Part of SupportVectors to Inculcate In-depth Technical Abilities and Further Your Career.
