Addicted to Tech

Data Science v/s Artificial Intelligence v/s Machine Learning v/s Deep Learning What is Data Science? Let’s break the term into its composite parts – data and science.  Science works fundamentally through the formulation of hypotheses – educated guesses that seek to explain how something works and then finding enough reasonable evidence through observations in the real world to either prove the hypothesis right, or falsify it. Data, on the other hand, refers simply to numbers and statistics which we gather for the sake of analysis. By combining these two, we get data science. What exactly does it mean? Data science is an umbrella term for all techniques and methods that we use to analyze massive amounts of data with the purpose of extracting knowledge from them. Example of Data Science: Let’s say you are crazy about Cricket, which I am sure you are, and there is an ongoing series between India and Australia. India loses the first two matche...

Learning Path for Data Scientists Fundamental of Python and R(10Hrs) Basics of Python and R Conditional and loops String and list objects. Functions & OOPs concepts. Exception handling. Database programming. Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R. Data Wrangling(16hrs) Reading CSV, JSON, XML and HTML files using Python NumPy & pandas Relational databases and data manipulation with SQL Scipy libraries Loading, cleaning, transforming, merging, and reshaping data Once you have the core skill of programming covered – dip your feet in the nitty-gritty of working with data by learning how to wrangle and visualize them. Statistics and Probability(8Hrs) Probability mass functions Probability distribution functions Cumulative distribution functions Modeling distributions Inferential statistics Estimation Hypothesis testing Implementation of sta...

Data Science — including machine learning, data analysis, and data visualization First of all, let’s review what machine learning  is . I think the best way to explain what machine learning is would be to give you a simple example. Let’s say you want to develop a program that automatically detects what’s in a picture. So, given this picture below (Picture 1), you want your program to recognize that it’s a dog. Picture 1 Given this other one below (Picture 2), you want your program to recognize that it’s a table. Picture 2 You might say, well, I can just write some code to do that. For example, maybe if there are a lot of light brown pixels in the picture, then we can say that it’s a dog. Or maybe, you can figure out how to detect edges in a picture. Then, you might say, if there are many straight edges, then it’s a table. However, this kind of approach gets tricky pretty quickly. What if there’s a white dog in the picture with no brown hai...