2 answers
Sean
Student
New Market, Maryland
1
Question
Asked
170 views
How can I prep for my careeras a data practitioner?
A career on data science and machine learning and AI
Login to comment
2 answers
Adit Sheth
Senior Software Engineer
98
Answers
Bellevue, Washington
Updated
Adit’s Answer
If you're gearing up for a future as a data professional in areas such as data science, machine learning, and AI, here are some friendly tips to help you on your journey:
Brush Up on Math and Stats: Having a solid grasp of linear algebra, calculus, and statistics is crucial for understanding the algorithms and models used in AI and data science.
Dive into Programming: Python and R are the go-to languages for data science. Familiarize yourself with libraries like Pandas, NumPy, Scikit-learn, and TensorFlow for machine learning.
Become a Pro at Data Handling: Get to know SQL and how to operate databases. Managing data effectively is key.
Gain Real-World Experience: Tackle projects that use actual datasets to hone your practical skills. Kaggle is an excellent platform for getting your hands dirty.
Discover Machine Learning and AI: Begin with supervised and unsupervised learning, then venture into deep learning methods.
Enroll in Online Courses: Websites like Coursera, edX, and Udacity provide top-notch courses in data science and AI.
Stay in the Loop: Regularly read blogs, research papers, and stay abreast of the latest trends and tools in the industry.
With ongoing learning and practice, you'll be well on your path to becoming a proficient data professional!
Brush Up on Math and Stats: Having a solid grasp of linear algebra, calculus, and statistics is crucial for understanding the algorithms and models used in AI and data science.
Dive into Programming: Python and R are the go-to languages for data science. Familiarize yourself with libraries like Pandas, NumPy, Scikit-learn, and TensorFlow for machine learning.
Become a Pro at Data Handling: Get to know SQL and how to operate databases. Managing data effectively is key.
Gain Real-World Experience: Tackle projects that use actual datasets to hone your practical skills. Kaggle is an excellent platform for getting your hands dirty.
Discover Machine Learning and AI: Begin with supervised and unsupervised learning, then venture into deep learning methods.
Enroll in Online Courses: Websites like Coursera, edX, and Udacity provide top-notch courses in data science and AI.
Stay in the Loop: Regularly read blogs, research papers, and stay abreast of the latest trends and tools in the industry.
With ongoing learning and practice, you'll be well on your path to becoming a proficient data professional!
Sean Dvorak
Data Scientist
7
Answers
New York, New York
Updated
Sean’s Answer
Exciting that you are becoming a data professional!
Adit - gave some great answers above, and I will add a few:
- Look to GenAI as a starting point: GenAI can be a helpful tool to generate starting blocks of code that you can build on for a model. You should be familiar with the underlying principles of the code and not just use GenAI, but it can help you test more things rapidly.
- Remember your end goal: Always take a step back and remember the problem you are trying to solve for, and how you will action the results of it. It is very easy to get deep in a fancy model, but when you get down to it, it cannot be applied in real life because of real world constraints. For example, you have a swimwear business that does better with warm weather. You could build a great model to forecast weather, but if you cannot change how quickly you can produce / ship the swimwear, it may only be useful as a sales forecast and less actionable.
Best of luck in your future endeavors!
Adit - gave some great answers above, and I will add a few:
- Look to GenAI as a starting point: GenAI can be a helpful tool to generate starting blocks of code that you can build on for a model. You should be familiar with the underlying principles of the code and not just use GenAI, but it can help you test more things rapidly.
- Remember your end goal: Always take a step back and remember the problem you are trying to solve for, and how you will action the results of it. It is very easy to get deep in a fancy model, but when you get down to it, it cannot be applied in real life because of real world constraints. For example, you have a swimwear business that does better with warm weather. You could build a great model to forecast weather, but if you cannot change how quickly you can produce / ship the swimwear, it may only be useful as a sales forecast and less actionable.
Best of luck in your future endeavors!