Hello guys, I am sure that you are ready to dive deep into Data Science. I will cover the complete course with essential code from 0 to 100 in this series of blogs. So, let’s begin...
Topics covered in this blog:
1) Differencing these technologies (AI vs ML vs DL)
2) Defining these technologies
3) Prerequisite to become a Data Scientist
4) Life-cycle of Data Science Project
How AI & ML are different from each other?🤔
What we find from this picture is that Artificial Intelligence (AI) is the main part or the parent set. Machine learning (ML) is the subset of AI and further Deep Learning (DL) is the child set of ML. Thus AI is broader part that includes ML and DL. Data Scientist is a person who will use all these technologies in his day to day work to complete his goals. Big Data engineer mostly works on handling the big data using a bit of DL. Now, let’s see what these terms mean.
Artificial Intelligence, in simple words, is a teaching machine to think like humans. AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Thus, ML, in general, is making a machine to learn from the given data.
Deep learning (DL), as the name, suggests, making the machine learn a given data in depth. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning themselves from the data by making a neural network, similar to the network present in our BRAIN.
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data(big data) to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Prerequisite before opting Data Science
1. You need some basic knowledge of MATHS topics like — Statistics, Probability, Algebra & Calculus. If you don’t know, don’t worry, I will try to explain all of the necessary math with explanation in upcoming blogs.
2. Keep Learning: Yes, in Data Science you are required to be updated with new technologies (algorithms). Learning is a never-ending task in this field.
3. You will surely require a good pc(or laptop) with at least 8Gb ram and i5 processor, also GPU, but lastly, GPU depends on your budget. A good pc is always a necessity in this field.
4. Data Science is 50% learning and 50% working on projects. You will deal with multiple projects during your journey to Data Scientist.
It is also important for you to know the type of work you will perform in your project. Here I have listed the common steps that you will perform in each data science project.
Life-Cycle of Data Science Project
1) Collecting dataset
2) Pre-processing dataset
3) Visualize data with a graph
4) Make necessary changes in the given data
5) Choose a suitable model(algorithm) for your task
6) Predict on a test dataset
Now, since you know what is data science and the type of work you will be involved in, so you are all set to begin your journey in Data Science. Make sure you follow me to continue reading the second part.