One of the questions we often receive is "I don't have any experience with Python, would I able to follow the courses if I take a basic Python course before?". The answer is YES, but let us help you with that.
Dataweekends focus on data techniques and they assume some familiarity with Python and programming languages. Since they only last two days, they cannot be a substitute for a bootcamp or a university degree. They are designed to speed up your learning curve in data science, machine learning or deep learning giving you enough knowledge for you to then be able to continue learning on your own.
Over the years we've trained software engineers, business intelligence and analytics professionals, product managers, phd students and managers. Everyone completed the weekend successfully and they all enjoyed it. Some of them did some extra prep work before the weekend in order to fully take advantage of it.
So what are the things you can do to get ready for dataweekends?
- Brush up your Python: learn about Data structures, flow control, functions, classes, packages and "pythonic" constructs like list comprehension, iterators and generators. 3 resources to get started are:
- Familiarize yourself with Jupyter Notebook. This is the environment we will use for most of the course. It's very easy to use, but can be disorienting at first, especially if you are used to work with an IDE or have never coded before. Here are a couple of resources to learn the basics:
Besides these, you can also read about a few libraries we use in every class. It is not strictly necessary, because we gradually introduce each of them, but it can help to have some familiarity, especially if you don't plan to attend all weekends in a row, but only a few of the advanced ones. Remember, Dataweekends are 50% hand-on, with plenty of time to practice your skills.
- Pandas: the standard library to manage tabular data in python. We will use it a lot. Here's a 10 minutes introduction to it. -
- Matplotlib: the library for plotting in Python. We will use it to display data.
Finally, here are a couple of articles on machine learning that you may use to wet your appetite. We encourage our students to ask questions during class, so, if you read a little about things like: supervised & unsupervised learning - model validation - feature engineering and cost functions you may have a few questions that we'll be happy to address.
Here are a few links:
We are excited to get you started in your data science journey and look forward to seeing you in class!