Data Science Fundamentals - Data Science - ثاني ثانوي
1. Introduction to Data Science
2. Data Collection and Validation
3. Exploratory Data Analysis
4. Predictive data modeling and forecasting
Mathematics Needed to Become a Data Scientist
Linear Algebra
Discrete Mathematics
Probability and Statistics
Calculus
Python for Data Science
Python
Introduction to Jupyter
Tools for Data Science
Popular tools for data science steps
Data Science jobs
Professions related to Data Science
Data Science Online Communities
Kaggle
IBM Data Community
Online communities
Remember to always check the online reputation of the content contributor before using a dataset, code or tools.
Kaggle.com home page
Read the sentences and tick True or False.
Explain how Python can help a Data Science professional.
Explain how Jupyter can help a Data Science professional.
Mention the most important tools for Data Science. How exactly do they contribute to each Data Science step?
Why is understanding statistics a fundamental skill for a data scientist? Can you think of an example involving data analysis?
Python is a versatile programming language. Is it enough for data science projects?
On the internet, find three Python libraries that are very popular among data scientists. Briefly explain why.
Compare and contrast an IDE and Jupyter Notebook. What are the main ways they differ?
You are learning to become a data scientist and have mastered Python coding. What other tools will you need for your data science toolkit?
In this lesson there is a list of professions related to data science. Which one would you prefer to follow and why? What challenges do you think you would face in this profession?
Visit an online data science community and search for a simple self-study training course to enhance your knowledge of data science. Evaluate how appropriate the course is to your level of kn
