Course Staff
Instructor
Matt Higger (he/him) |
For the CS side of Data Science, be sure that you use many small milestones in your development, change just one thing at a time and then test to ensure things work before continuing on. This approach is succesful because bugs don’t have much room to hide in your little, incremental changes so they’re easier to find! For the Machine Learning side of Data Science, be sure to focus on the needs of your application. Don’t assume that complex math yields more value than simple math: heavy-duty ML algorithms don’t necessarily offer more value (and they’re certainly tougher to build and maintain!). The more removed your expertise is from an application the more important it is that you ask strong questions and listen to what others have to say. |
Coordinator
Alex Gonzalez (he/him) |
As a Khoury Academic Coordinator, I coordinate and manage teaching assistants in large-enrollment courses and assist instructors with administrative tasks. I may not have any computer science knowledge or experience, but I do enjoy providing support in CS courses because I get to witness the drive, ambition, perseverance, and self-advocacy of students, and learn a lot from faculty and staff. Please do not hesitate to get in contact with me throughout the semester. I look forward to working with/getting to know you all. Wishing you all the best this Spring! |
Teaching Assistants
Mahek Aggarwal (she/her) |
Hi! I’m a combined data science and behavioral neuroscience major with a minor in math. Some advice I have for this course is to start DS homework early so you have time to work through bugs. |
Nidutt (No) Bhuptani (he/him) |
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Venkatesh Gopinath (Gopi) Bogem (he/him) |
The three components I think it’s essential to get our head around to learn Python better are first, Understand CRUD: If you can understand how to create, read, update, and delete an object in Python, you’re well on your way to understanding that component. The only objection to this is that it’s essential to understand how to loop through elements, so maybe CRUDL is a better initialism. I digress. Secondly, Get familiar with using any IDE (Jupyter, Spyder, GoogleColab): I feel Jupyter/GoogleColab are probably the best interfaces to use when starting, especially when it comes to Data Science workloads. The latter is less burden to our systems and makes it easier to work within groups. Lastly, Start working on projects before you think you’re ready. The best way to break through that rut of unending tutorial trap is to start making and breaking stuff with Python. Find an easy enough tutorial just outside the boundary of your skill, give it a go, and start working on a project. Lastly, I would like to emphasize the most crucial thing, Practice-Practice-Practice. There is no shortcut for this. Happy coding.! |
Shalvi Desai (she/her) |
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Unnat Goenka (he/him) |
Hello. I am a senior studying Data Science and FinTech. I have been on 2 co-ops and have been involved in the MOSAIC community on campus. Feel free to reach out if you want to know more! The part about Data Science that excites me the most is that it gives you an edge over others by using data and predictive analysis rather than gut and guesswork. |
Rohith Bhat (Rohith Bhat) Kuthyar (he/him) |
Practice.. Practice… Practice !! It’s important to keep in mind that learning Python / DS is an ongoing process, and it’s important to stay curious and open to new ideas and approaches. The most exciting part of data science projects is often the opportunity to learn and make discoveries based on the data, and to use these insights to solve a real-world problem. Start by learning the basics of Python programming and practice by working on small exercises or projects. This will give you a strong foundation for learning more advanced concepts later on. Don’t hesitate to reach out to people to discuss / Share your ideas/approach with others. This will help you understand other possible ways to visualize the problem. |
Claudia Levi (she/her) |
Hi everyone! If I had to learn how to code all over again, the advice I would give myself is to learn to read libraries’ documentation. Being able to implement something you’ve never done before from the documentation is immensely helpful and really broadens what sorts of projects you’re able to produce. |
Emily Liu (she/her) |
I’m a second year Data Science student that’s passionate about all things pertaining to data. What sparked my interest in DS was the ability to share information through the usage of data. Without data, it would not be possible to explain many concepts and phenomenon that are crucial to our understanding of the world. By taking this course and asking for help from your TA’s, I hope you find Data Science as fascinating as I do. |
Jonathan (Jo) Paul Dande (he/him) |
If I were to learn all my skills in Python and data science over again, I would give myself the following advice: 1). Have a strong grip on the basics: Having a solid foundation in the basics of Python will make it much easier to delve into more complicated topics later on 2). Build projects: While going through tutorials and books, it’s better to practically implement the concepts and work on real-world projects. This will help to gain a deeper understanding on the concepts 3). Learn to effectively use various libraries: It is crucial to understand the basics of libraries like numpy, scikit, matplotlib, pandas as they are predominantly used in most data science projects. |
Laura Morehead (she/her) |
Hi, I’m Laura and I’m a second year computer science major from New Jersey. Some advice I’d like to give is to not be afraid to use your resources when you get stuck since you won’t always know how to do everything the first time around. Remember that data science is a collaborative field, so bounce your ideas off classmates, sift through the documentation, and come to office hours to ask your TAs! |
Akhil Krishna (Akhil) Nair (he/him) |
Python is a very powerful and fascinating language. A large part of my journey learning python was through hands-on project work and I believe that projects, no matter how simple, can go a long way in teaching the language and concepts around it. Reaching out to our peers and the community is a great way to find help whenever we are stuck. I have spent countless hours on problems that could have been solved in no time if I had reached out to a peer or mentor. Do not hesitate to reach out with questions when in doubt, as it will play a significant role in our journey learning python and data science. |
Vaidehi (Vaidehi or V) Parikh (she/her) |
Hello, everyone! My name is Vaidehi Parikh and I am a graduate student at Northeastern University majoring in Data Science. I obtained my bachelors degree in Computer Engineering and after that I was working as a Data Processing Specialist at a market Research Firm. I completed two internships: one as a Data Analyst and one as a Data Scientist. I extend a warm welcome to the entire DS 2500 class. I hope you all learn and strengthen your DS/Python foundation. Things I would tell myself if I had to start over: be proactive in reading articles about DS/ Python; it will help you gain in-depth knowledge. Things I would tell myself if I had to start over: be proactive in reading articles about DS/ Python; it will help you gain in-depth knowledge. When you learn a concept in class, make sure to practice it on hackerrank or leetcode to improve your coding skills. The most exciting part for me in a DS project is Data Cleaning and EDA. This is something that is often overlooked while working with dummy data but these two are the most important factors when it comes to industry because the real world data is meditated like kaggle. strong foundation in Cleaning Pipeline and EDA helps a lot in later parts of DS. |
Aveek Saha (he/him) |
The most exciting part of Data Science for me is the endless potential for application in the real world. It’s one thing to study the theory behind a concept, and a completely different thing to use it to analyze or solve something practically. With the wealth of data that surrounds us today, I believe Data Science is the branch of computer science with the most practical applications. This is what intrigues and motivates me to continue to explore this subject. Python being an intuitive and succinct language in it’s syntax makes it a lot more approachable than a lot of it’s verbose counterparts. This makes it the perfect tool for getting started with machine learning or data science. The scriptable nature makes it perfect for prototyping and iterating while developing. As a kinesthetic learner, I believe the best way to start learning something is to get your hands dirty and just start writing code. |
Manikhanta Praphul (Sama) Samavedam (he/him) |
Hello everyone, My name is Samavedam Manikhanta Praphul, prefer to go by Sama. I am currently pursuing my masters in Artificial Intelligence at Northeastern University after having worked as data scientist for 4 years at companies like UBS, BNY Mellon. I would love to have conversations around AI, Python and assist students in achieving their dreams. If I were to give advice to myself about learning Python/Data Science skills, it would be simultaneously do practical to gain sense of the function/topic rather waiting to combine the topics learned once in a single project. This will not only hone your technical skills, but also provide READMEs to go through for quick revision and the cherry on cake is that you will have repositories to show for your recruiters as well. The most exciting part for me in a data science project with Python, is the story telling of the quick insights derived. The insights derived are like gold for any project success so getting to them sets off the work went to extracting and refining data. |
Akshi Saxena (she/her) |
The most interesting part of Data Science, to me, is understanding the nuances in data and exploring different possibilities to use it. Don’t be overwhelmed and make sure to reach out whenever in doubt. Get as much hands-on practice and you will make good progress. Try to be active and have meaningful discussions to discover a new perspective. |
Charenvishwa (Vishwa) Senthil Kumaran (he/him) |
Begin working on projects before you believe you are prepared!! You can learn as you go along. Most importantly, have fun with your data!!! |
Parth (Parth) Shah (he/him) |
Prioritize learning the fundamentals: Before moving on to more advanced concepts, ensure that you fully understand the fundamentals of programming and data science. Data types, control structures, and algorithms are examples of this. Practice, practice, and more practice: Getting hands-on experience through exercises and projects is the best way to improve your skills. Make time in your schedule to practice your skills regularly. Don’t be afraid to ask for assistance: You will encounter times when you are stuck or don’t understand something. Don’t be afraid to seek assistance from more seasoned programmers or data scientists. Keep up with new technologies and tools: Because the field of programming and data science is constantly evolving, it is critical to stay current with new technologies and tools. This could entail learning new programming languages or frameworks, as well as staying up to date on new data analysis and visualization tools. Take on challenges: Don’t be afraid to take on projects or tasks that are outside of your comfort zone. These challenges will assist you in growing and improving your skills. |
Kimberly (Kim) Stochaj (she/her) |
I love the fact that Data Science can be used to explore the connections between various attributes acting sometimes as a way to add significance to what logic leads us to believe is true and other times working to disprove what may be a common misconception. While some of the tools necessary to do this - use of non-primitive data structures, common data cleaning practices, visualization skills, etc - were developed in the prior course, the machine learning algorithms covered in this course will allow you to further support similar types of investigations. |