PROBABILTYAbsolutely, probability theory is a fundamental aspect of data science and is essential for understandinguncertainty and randomness in data. Here are some key topics within probability theory that areparticularly relevant for data science:1. 1D Random variablethe function of one random variable2. joint probabilty distribution3. discrete distribution- Binomial(python,R)- bernoulli- geometricand many morePROGRAMMINGA strong foundation in programming is essential for success in data science. Here’s why proficiency inprogramming concepts such as data structures and algorithms is crucial, along with the significance ofvarious programming languages commonly used in data science:• Python:• Python Basics• List• Set• Tuples• Dictionary• Function• NumPy• Pandas• Matplotlib/SeabornMACHINE LEARNINGmachine learning (ML) is indeed a cornerstone of data science, and staying updated with advancementsin ML algorithms is crucial for data scientists. Here’s why understanding the basic algorithms ofsupervised and unsupervised learning is essential, along with the significance of popular libraries inPython and R for implementing these algorithms:• Introduction:• How Model Works• Basic Data Exploration• First ML Model• Model Validation• Underfitting & Overfitting• Random Forests• scikit-learnDEPLOYMENTdeployment is a crucial step in the data science workflow, regardless of one’s level of experience. Here’swhy deployment is necessary and how it adds value to a data scientist’s work:• Microsoft Azure• Heroku• Google Cloud Platform• Flask• DJango