Some of these top knowledge and skills for Data Analysts/Data Scientists/AI Engineers/… The suggestions here are based on my experience and interviews with dean’s office team, industry advisory board, industry alumni, external consulting and professors.
1. Knowledge: Applied Math and Statistics, Machine Learning
¨1. Knowledge will be learned from the courses. including
- Registered courses,
- Audit a course,
- online study, (LinkedInLearning)
- workshop and seminars,
- …
Later on your resume, you should not just list the courses you studied. You also need to list the methods you used in projects and coop (PCA, Markov Chain, SVM, A/B testing, Ridge/Lasso, Neural Network, etc.)
2. Computer Skills
Computer skills students need to know to work in data science.
- Python(pandas, numpy, statmodels, matplotlib, etc.), TensorFlow, PyTorch, SQL,
- R, MATLAB, (Some company use R and MATLAB), Maple, Mathematica,
- Excel, PowerPoint,
- High-performance computing skills (discovery, cluster at Northeastern)
- Cloud computing: AWS, Google Cloud
- Data Visualization(Tableau)
- GitHub
- C/C++/HTML/Java (not essential for regular data scientist, but essential for data/machine learning engineer)
- Visual Basic for Applications(VBA) for Excel (not essential for regular data scientist)
- If you used any others, add it, e.g., SAS, WPS, SPSS, (e.g., SAS is essential for BIOSTATISTICS)
- Take advantage of the new technology such as ChatGPT and related coding editors to master more computer languages.
Suggestions:
1.Mostly will be (learned and) practiced from computer labs in the course work, like Python, R, Matlab, High-performance computing.
2.Some skills are learned and used in undergraduate, like Excel, PowerPoint, SQL. If you have not used SQL or Excel or Tableau, learn it from some free online courses.
- YouTube
- Coursera (Free for non-certificate)
- Edx, MOOC,
- Udemy,
- LinkedIn Learning (Free for Northeastern students)
- https://www.codecademy.com/
3. Soft Skills
- The following skills are “soft” and “hard” to learn directly from one course. However, they are the most important for find a job. You can develop those skills along your study. (e.g., presentation in class, ask and answer questions, learn extra topics, be on time for assignments, …)
- The company hire a team member but not a student.
- Interview is communication but not an exam, even for the coding test.
1.) Presentation Skills
2.) Communication Skills
3.) Critical and Analystical Thinking
4.) Self-Study ability
5.) Good Attitude
6.) Curiosity/Business Mindset/Adaptability/leadership
7.) Network
Suggestions:
1.Attend classes, communicate with your professors
2.Ask and answer questions in class
3.Find all opportunities to practice
4.Learn SQL or Tableau from some online courses. It builds your basic computer skills and it also shows your self-study ability and attitude.
5.Audit a class, learn some online courses
6.Attend Data Workshop, actively involved in in the workshop.
7.Attend Research seminars.
8.Communicate with people, build your network.
9.Find your strength and weakness.
Communication, presentation, problem solving skills are so important!!!) Students should have more presentations in all possible opportunities, explain their labs and projects. Show implications, e.g., confusion matrix, F1-score, ROC, AUC curves. Less equation, more explanation. Use plain English for machine learning. Understand clearly. Why P-value, Why F1-score? Why we do this? Reason, Conclusion.
Everyone has their own strength. Your non-math background will be useful. For example, if you have a better coding skills (C++, Java), you may try to find a job as data engineer or software engineer. If you good at biology, you may consider to be an data scientist in biotech or bioinformatic. If you good at statistics and SAS, you may consider to be an BioStatistician.
If you do not have all the skills, it is not late today.
Prepare your Resume from today: (Set your ideal Resume and study the skills.)
Search your dream jobs and look at the Responsibilities and Requirements and build them in the next two years.
- Create a GitHub account and built a personal website.
- Create a Kaggle account and join a competition.
- Create a LinkedIn account and connect people. (Start with me)
- Create a blog account and start writing blogs. For example, in Medium.com(towardsdatascience)
- Create a Leetcode account to practice coding skills in Pythons and SQL
- HackerRank for practicing SQL
Write your own blogs, like your understanding of some skills/topics in Probability, Statistics, Machine learning, etc. You can also write reviews of papers. Especially you can start with your project in the class.
Follow the current progress of machine learning and artificial intelligence, it will be helpful when you talk with people in seminar or interview. For example,
https://www.tableau.com/learn/articles/blogs-about-machine-learning-artificial-intelligence
For example, here are some students’ website:
Sakshi Suman (Coop at LinkedIn)
https://www.linkedin.com/in/sakshisuman12/
Sai Nikhil Thirandas (Graduate in 2022 and start work at Amazon)
Tigran Mekonian (2022) Coop-Amazon
https://www.linkedin.com/in/tigranmelkonian/
Rohit Thakur (2021) Data Scientist @ Nestlé
https://www.linkedin.com/in/thakur-ro/
Esha Srivastava -Data Scientist Intern -Quantori
https://www.linkedin.com/in/esha-srivastava/
Try to make you LinkedIn information as complete as possible.
“In industry/company, if you see some people updating their linkedin information, that means they are looking for change jobs.”
Q: How much math is enough?
A: The more the better. However, we don’t have time to learn for all of them. If you want to apply PhD, you should have a solid foundation of math, starting from Algebra1,2 Analysis1,2, Probability, Mathematical Statistics, etc. If you want to find a coop and a job in two years, you need to quickly master some key industry skills. (Survival first, then developing.)
Q: When to learn the soft skills?
Currently, finding an intern/coop or a job in data science is not as easy as a few year ago. We need to work hard finding a dream job, not just receive A in courses. The computer skills and soft skills are very important. When the job market is good, you may be able to learn those skills after you start working. In recent years, company will not give you time to learn those even in internship.
https://www.northeastern.edu/graduate/blog/data-analyst-skills/
https://www.northeastern.edu/graduate/blog/data-analytics-vs-data-science/
https://www.northeastern.edu/graduate/blog/what-does-a-data-analyst-do/
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