Jedha is a data science bootcamp that aims to help anybody go from zero knowledge to expert data scientist. While this may seem like a daunting task, Jedha successfully trains job-ready data scientists by breaking their program into two courses, so that students may master the basics before moving on to more challenging work. Their 40-hour program focuses on teaching the fundamentals of data science, while their 300-hour course aims to help students reach an expert level and become job-ready.
A significant piece of the program tackles machine learning, an increasingly in-demand area of data science. Students at Jedha become experts through a very hands-on curriculum that teaches them to build powerful models using Python.
1. From Supervised Machine Learning to Deep Learning
If you are interested in becoming a machine learning expert with Jedha, it's important to first understand the different applications of the field. This area of data science is used in many ways, and it is Jedha’s goal to teach machine learning skills that are applicable in real-world situations. On the job, most machine learning experts are tasked with making predictions (i.e predicting an exact price for a house) or creating groups (i.e mapping different types of consumers for a given product). Machine learning specialists whose work involves making predictions practice an area called supervised machine learning. For tasks that involve describing the structure of “unlabeled” data and creating groups, you’ll need to know unsupervised learning.
Finally, deep learning is another sub-field of machine learning that repeatedly combines different algorithms to gain precision on predictions and groups. As deep learning becomes more common, it is being used in many interesting ways: for example, deep learning is now the most common application for image recognition.
These three subfields are difficult to master, because their difficulty greatly increases as a student progresses. That is why, at Jedha, we first teach supervised machine learning, then unsupervised learning, and we finish with deep learning.
2. Practice, Practice, Practice .... And a little bit of theory
A common cliché of data science is that you need to be a master in statistics to build a career in this field. Of course, you need to know some statistics, and we cover all the math basics a good data scientist will need. However, companies typically don’t ask their data science team to implement very complex statistical models. Insteads, employers need someone capable to extract, clean and analyze data no matter where it comes from. This is called data preprocessing, and it’s a skill that is more reliant on coding than statistics.
For this reason, Jedha focuses more on coding than statistics, and coding skills require a lot of practice. To give students plenty of opportunities to hone their coding skills, Jedha’s curriculum focuses on machine learning projects on Kaggle or real-world data science projects that companies give us.
3. Python Over R
Rather than teaching both Python and R, we chose to teach only one programming language. This is because we find that it's better to master one language rather than being "good" in two.
We chose Python over R is for the variety of applications that Python offers. Whether it's in data science or web development, Python is a great programming language and the ecosystem around it is very mature. It's also an easy language to start with. Since some our students never programmed before, it's an efficient way to start learning.
4. Project-Oriented Curriculum
As we mentioned already, practice is key in learning data science. It's always going to be very hard and frustrating but as you keep practicing, your work will greatly improve and you’ll find that it gets easier and easier. Therefore, it requires a lot of motivation at the beginning to become a good Data Scientist.
In order to help students keep their motivation high, Jedha’s curriculum is very project-oriented. Working on projects is a very good way for students to remain engaged, as they are able to choose a topic that greatly interests them. Students are also much more attractive to recruiters when they have projects to showcase. A standout portfolio of projects is an excellent way for students to prove that they have the required skills to succeed at any new data science job.
Finally, at Jedha we always prioritize real-world projects that companies give us because recruiters are even more interested in our students if they have worked on current problems. Also, company projects can help to motivate students and make them feel like their work is contributing something and will be used to significantly help the organizations we work with.
Hands-on learning is the key foundation of all Jedha courses, and our curriculum also introduces theory as students learn more complex concepts. We deeply believe that hands-on learning is one of the best ways to learn technical skills such as Data Science.
Want to learn more about Jedha’s courses? Check out our reviews on SwitchUp, or learn more at www.jedha.co.