Artificial Intelligence (AI) has a tremendous attention in recent years. John McCarthy who coined this term in 1956, defines it as “ The science and engineering of making intelligent machines”. AI is the intelligence demonstrated by machines, by performing smart tasks that typically require human intelligence.

The main core of AI is Machine learning (ML), Arthur Samuel said that ML is the “Field of study that gives computers the ability to learn without being explicitly programmed”, witch means that ML is the field of learning from experience, without the need of so much programming.

In this article, we will talk…


Every data science project can be seen as a sequence of steps, each step depends on the previous ones.

Before learning more about data science life cycle, I suggest you take a look in advance at what data science is and what its goals are, by reading this article : Data Science : The Buzzword.

Data science is a multidisciplinary field that uses methods, algorithms, mathematics, statistics, programing and logic in order to extract insights and relevant ideas from structured and unstructured data.

For a better understanding of What is Data Science ?, …


  1. Introduction
  2. Data vs Insight
  3. Data Science
  4. Traditional Programing vs Data Science
  5. NLP
  6. Computer Vision

We hear more and more about data science, it is the buzzword in companies, universities and even social networks …that’s why you have to read about it.

Data Science is simply a multi-disciplinary field where the goal is to use data to solve a real problem or provide value.

The main goal of data science is to extract insights from data

So it is first necessary to know the difference between these terms “data” and “insight” :

Bendriss Maryam

data science enthusiast

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