Data Science and Machine Learning :

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Dr. Thomas Miller of Northwestern University describes data science as “a combination of information technology, modeling, and business management”. Universities have acknowledged the importance of the data science field and have created online data science graduate programs.

Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.

Data science and machine learning are both very popular buzzwords today. These two terms are often thrown around together but should not be mistaken for synonyms. Although data science includes machine learning, it is a vast field with many different tools.

Machine Learning Machine Learning is making the computer learn from studying data and statistics.

Machine Learning is a step into the direction of artificial intelligence (AI).

Machine Learning is a program that analyses data and learns to predict the outcome.

Where To Start? In this tutorial we will go back to mathematics and study statistics, and how to calculate important numbers based on data sets.

We will also learn how to use various Python modules to get the answers we need.

And we will learn how to make functions that are able to predict the outcome based on what we have learned.

Data Set In the mind of a computer, a data set is any collection of data. It can be anything from an array to a complete database.

Example of an array:

[99,86,87,88,111,86,103,87,94,78,77,85,86]

for more datascience :

This data science primer will cover exploratory analysis, data cleaning, feature engineering, algorithm selection, and model training. As you can see, those chunks make up 80% of the pie. They also set the foundation for more advanced techniques.

In this first chapter, you’ll see how these moving pieces fit together. Therefore, we suggest the following two tips to making the most out of this primer:

Tip #1 - Don’t sweat the details (for now).

We’ve seen students master this subject 2X faster by first understanding how all the pieces fit together… and then diving deeper. Our trainings all follow this “top-down” approach.

Tip #2 - Don’t worry about coding (yet).

Again, it’s easy to get lost in the weeds at the beginning… so our goal is to see the forest instead of the trees. Don’t worry - We’ll get to the code later.