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In the digital age we live in, collecting, analyzing, and storing data is detrimental to business success. Companies understand that their success depends on their ability to extract meaningful insights from user data and apply it in their strategy. This is where data scientists come in. To help you better understand what data science is and everything it is related to, we have created this how-to article Data Science Program in BSc and MSc .
At the crossroads of mathematics, business, and computer science, the Data science programme It trains students to be able to exploit and understand large amounts of raw data, also known as big data. You will have technical knowledge and skills, which will allow you to know how to use and develop artificial intelligence in decision making. Machine learning, deep learning, software engineering: all these concepts will be acquired at the end of the four semesters of training.
It is complex to define precisely what data science is, as this field is interdisciplinary. However, it could be said that it is a matter of extracting usable information from the unprocessed data, in order to plot trends or connections.
Warning: a data scientist should not be confused with a data analyst. The first is a scientist who intervenes in the initial stages in the data sets to sort and store them and design modeling tools to facilitate their analysis. This is where the data analyst comes into play.
Data science is a scientific discipline at the intersection of computer science and applied mathematics (in particular statistics) that has emerged over the past ten years with the proliferation of acquisition devices, connected objects, and the Internet. Huge blocks of data (big data). This growing amount of data constitutes a wealth that must be explored, selected, filtered and interpreted in order to generate knowledge.
This ability to extract knowledge from data is relevant to many sectors of activity ranging from health to digital humanities, including logistics, home automation, e-commerce, and finance. Thus, in the very short term, and for the long term, this field will be a vector of innovation and wealth.
The aim of the standard course “Data Science” for computer licensing is to train data professionals by inculcating: – the necessary theoretical foundations in general mathematics and statistics, to analyze, exploit and extract primary knowledge from data, – the necessary theoretical foundations in computer science, with mastery of at least three Programming languages – the mathematical and computational rules needed to process signals and images. Knowledge needed to develop data analysis and processing infrastructure.
The first year of this course is combined with the other IT course. In the second year, there are two training modules dedicated to data science, and the third year allows you to get a real “data science” coloring.
Major and recent developments in data science have redefined the classic framework for modeling industrial and natural phenomena, with a two-way interaction, continuous models and all (usually huge) data primarily from the advent of sensors and sensors of all kinds.
The BSc Data Science and Modeling aims to create this unique link and provide the foundations for a new generation of high-profile students. The proposed training will enable future laureates to be able to model, acquire and study data, and then interpret it in order to extract useful information for decision-making or to improve and enrich existing ongoing models.
The Data Science program in the Bachelor’s degree is focused on:
First session:
Second period:
Third session:
Fourth session:
Fifth session:
Sixth session:
End-of-school training:
At the end of the course, the student also completes a 3-month end-of-school internship, in Morocco or abroad, that allows him to discover career in a company or in a research laboratory.
The purpose of this training is to apply the knowledge and experience gained, both practical and theoretical, and to facilitate professional integration.
The MSc in Data Science is a relatively new graduate program that combines key concepts from mathematics, computer science, statistics, and information science to harness insights and help data scientists improve operational and business processes. The Master of Data Science is best positioned for someone interested in furthering their data science career, interested in building or extending skills in machine learning, cluster analysis, databases, data visualization, statistics, data mining and more.
First session:
:Second period
Third session:
Professional course:
Fourth session:
End of study project: At the end of the course, the student completes an internship at the end of the study of 6 months, which allows him to discover a career in a company or in a research laboratory. The purpose of this training is to apply the knowledge and experience gained, both practical and theoretical, and to facilitate professional integration.
With the accumulation of the volume of available data in a large number of areas, new collection and analysis techniques exist in almost all sectors of economic activity and research.
Data science will improve the exploitation, protection and enhancement of data. The objective for companies is to anticipate market developments, develop their sector of activity and implement a trend-based development strategy.
In local authorities and government agencies, data science is used to produce and analyze data and statistics to improve public policies.
Data science will make it possible to produce, enhance and preserve knowledge.
Data science is used in a variety of business sectors. The main ones are: aviation, automobiles, communications, finance, search engines, e-commerce, media, medicine, etc.
A data scientist should have knowledge of programming in languages like R or Python as well as knowledge of computer architecture and databases. He must be able to adapt to different computer environments and have an intellectual agility that allows him to constantly learn and adopt new approaches. Must be proficient in data manipulation and be comfortable with many different data structures.
The Data Scientist role requires you to be an expert solver of complex problems. In this category of skills are skills in Statistics Advanced, machine learning, advanced mathematics, modeling, simulation, andartificial intelligence , etc. In general, STEM fields of study allow you to develop the analytical skills you are looking for and allow you to practice Problem Solving Scientific.
A data scientist must fully understand the company environment in which the data is evolving. In the field of data science, successful projects are those that are based on a specific situation and end with concrete solutions that can be integrated into the work environment.
Data scientists are in demand now more than ever. However, we should not forget that their success requires, first of all, reliable data in sufficient quantities. Real-time production monitoring or data analytics solutions are definitely ways to consider!
Data science is a field of study that uses data for various research and reporting purposes to derive insights and meaning from that data.
entertainment recommendation systems, and more.
Data scientists create and use algorithms to analyze data. This process generally involves using and building custom machine learning tools and data products to help businesses and customers interpret data in a meaningful way.
Yes, but in order to become an expert, you must enroll in a course that will provide you with the appropriate training, guidance, and mentorship.
To become a data scientist, you must have a college education. Even if there are few undergraduate programs in data science, a bachelor’s degree in mathematics, statistics, or physics can be an alternative.
A master’s degree is recommended as it is required by many employers to hire a data scientist.
Some data scientists choose to study for a PhD so they can teach and take a position of responsibility within a company.
It is interesting to note that the majority of algorithms, even by private companies, are open source. On the other hand, data is often jealously guarded!
However, there are more and more initiatives advocating “open data”, which is the equivalent of open source data. This consists of making data available to the public, particularly those of public institutions.
The majority of available data is scattered, uncoordinated, sometimes incomplete or erroneous.
Data mining is a system whose goal is to collect, clean and coordinate all this data … and thus make it as accessible and standard as possible in order to be able to exploit it.
This modular track provides students with the fundamental knowledge, technical skills, and concrete applied methodologies to exploit and make sense of large real-world datasets, which are usually very large and may consist of several heterogeneous knowledge bases. In particular, students will gain experience using and developing intelligent, data-driven services and tools for decision-making.
Students will also be trained to master big data processing and knowledge. This course will also provide the basics of learning technologies (machine learning, deep learning). This course focuses, as mentioned above, on data science but also on software engineering.
The areas of application of data science are countless. However, it is mainly used in the following sectors: aviation, econometrics, telecommunications, e-commerce, media, and public health.
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