Data science is an interdisciplinary field of scientific methods, processes, algorithms, and systems for extracting knowledge or insights from data in various forms, structured or unstructured, similar to data mining.
Big Data Analytics or Data Science is a very common term in the IT industry because everyone knows it is a fancy term that is going to help us manage the huge amount of data that we are generating these days -this.
The science of data:
Today, data is one of the most important values in the world. Huge amounts of data are produced and compiled every day of our lives.
This sea of data and information must be used appropriately to optimize the factors that affect our daily lives.
To take advantage of data or to consider it a value, we must first collect, analyse and adapt it to specific expectations or requirements. In the past, the concept of data mining was seen as meeting this need.
Data mining is the process of examining big databases to generate new information, which is then used to increase efficiency or solve complex problems.
Over time, this concept has evolved and been renamed data science.
Data science is defined in several ways. In general, it is multidisciplinary knowledge of data, mathematics (statistics) and algorithms, and technology aimed at proposing solutions to complex situations and problems.
Data Science explains the concept as follows:
"Data science encompasses almost all things that are directly relevant to the data: so that this science makes a difference: its collection, its analysis, the modeling process.... In addition, the larger section that we attach great importance to is the practical part, or rather the application part and all its types."
The importance of data science:
Data science can meet different types of demands in our daily lives. For example, here are some areas using data science:
Genomic data provide a better understanding of genetic issues. Logistics companies like DHL or FedEx can determine the best routes and shipping times. Human resources managers can predict employee attrition and understand the variables that affect staff turnover. Airlines can easily predict flight delays and inform passengers. You may ask yourself: why become a data scientist? In fact, according to recent research, data science is considered the best field of employment in the United States in three years (2016, 2017 and 2018), according to the 2018 Glassdoor ranking. In addition, as the amount of data increases every day, the demand for this type of position will increase, offering incredible opportunities for people in the field!
Scientific Python language:
Python has become a language of choice for scientists, because of its simplicity of implementation and the richness of its ecosystem, especially thanks to its numerous and efficient libraries of numerical calculations often developed by scientists themselves.
Python is probably the only language to offer computer scientists a comprehensive open-source environment dedicated to scientific research, engineering and mathematics.
Educational objectives:
Navigating the Python Science Ecosystem Create and use a Jupyter notebook Perform operations on digital boards Manipulate data from tabular files Calculate statistics from time series Predict a value by interpolation Create Static 2D Charts Create interactive 2D graphics Create maps from geographic data Manipulate images Automatically classify a dataset Implement a supervised learning method Parallelize a for loop Measuring the performance of a programme:
Introduction:
Over the years Python has become a daily tool for engineers and researchers of all scientific disciplines. Thanks to its many high-quality bookstores, it can now match or even surpass the best-performing proprietary solutions on the market. It has become one of the essential tools of Data Scientists! In this article, we invite you to discover the vast extent of this ecosystem…
I discovered Python’s scientific ecosystem almost 4 years ago now, with the «Scipy stack» in a statistical simulation project with strong parallelism needs. The handling of these libraries was not very simple, the environment being very rich and varied, it was difficult to see clearly and to find the right tutorials presenting the basic concepts to the novice that I was. And I’m still here.
With practice I started to be more comfortable, but the more I dug deeper into the topics the more I discovered new features and libraries, rich, varied, often competing, usually of high quality. It was hard to know which ones to choose based on the issues I had to deal with.
This set of scientific libraries resembled a galaxy that was meant to be endless and whose contours could never be defined.
Today I am just beginning to emerge, and I would like to offer you a sample of this «big picture» in order to help you see more clearly to start your projects on the right foot. Why use Python for scientific calculation?
Python has become a viable alternative to leading proprietary solutions like MatLab, Maple, Mathematica, Statistica, SAS...
It offers several advantages over these tools:
He calculates just as fast if not much more Most of Python’s scientific libraries can be compiled to take advantage of the vector and multicore/multithread architectures of modern processors. They are generally implemented in C to provide the most advanced performance. Finally, Python has many distributed compute libraries, allowing you to spread the load of your applications on many machines. It also works very well on the top 500 super computers
It probably covers all scientific fields: Python is not limited to mathematics and statistics, it has many libraries to address multiple domains such as:
Signal processing
The mechanics of fluids
Chemistry The atom
Genetics
Machine learning
Natural language
Mapping
... Python distributions for data scientists:
Installing Python is simple. But setting up a homogeneous and efficient scientific environment becomes laborious:
Start by isolating your work environment from the system
Indeed, libraries provided in general by Linux systems are difficult to exploit as such:
They are generally too old
Little or no optimization
They only offer part of the available bookstores of this huge community
There are many libraries and managing their dependencies can be difficult
You need to compile them specifically for your system if you want to harness the computing power of modern processors
It is therefore recommended to use Python distributions that offer consistent packaging of all these libraries and offer a minimum of optimizations for your machines.
Among the most common are:
Anaconda
WinPython
Python Anywhere
Python (x,y)
Conclusion:
This presentation is far from exhaustive.
We could say a lot more or provide examples of demonstrative codes. We keep that for other clauses.
We especially wanted, through these few pages, to give you an overview of what Python can offer to «Data Scientists» and engineers of all disciplines.
Its intrinsic qualities are certainly the reason for this success: simplicity, ease, conciseness, and, contrary to its reputation, performance!
He seems to have great years ahead of him.
Science continues to progress at a giant pace, the rise of artificial intelligence and BigData are no strangers.
"Google’s autonomous cars and robots get a lot of press, but the real future of the company is in machine learning, the technology that allows computers to become smarter and more personal." - Eric Schmidt (President of Google)
Python was able to settle in this very technical and sharp universe. Today, he holds a place of first choice.
keywords: machine learning, machine learning is, python machine learning,machine learning modeling, andrew ng machine learning , ai learning , aws machine learning, supervised learning ,unsupervised learning, ai ml, deep learning ai, tensorflow, data analytics, master's in data science, online master's data science, data analytics degrees, data science degrees, certified data scientist, master's in data analytics online , ms in data science, datascience berkeley ,uc berkeley data science, data science for managers, data science for beginners, certified data scientist, data science for all, big data analyst, r for data science, pandas, keras,tensorflowjs,hands on machine learning.
crossorigin="anonymous">