Data science major and how to become a professional data scientist

[ad_1]

Back Data science major With the advent of the Internet and the increase in the amount of digital data available in very diverse fields. While the storage and preservation of this data were the main issues in the beginning, it appears that the issues of exploiting and analyzing it are now key issues.

The jobs created by developing these new data sciences require multidisciplinary technical and strategic skills. Data science students can access a wide variety of careers, both in research and in the public and private sectors.

What is the specialty of data science?

Data science major It is a specialized science that uses scientific methods, algorithms, processes, and other systems to exploit large sets of data. Thus, data scientists combine several skills, particularly knowledge of IT, statistics, and commerce to analyze data collected from customers or other sources using sensors, their smartphones, internet browsing habits, etc.

Bachelor’s degree in Data Science

Software development and testing:

  • Algorithms and gifting
  • mobile applications
  • The Internet of things iot
  • Object-Oriented Language (OOP).
  • mathematics
  • Node.JS library
  • Scripting
  • Unified Markup Language UML
  • general project

Information systems infrastructure:

  • active directory
  • CCNA 1
  • CCNA2
  • Linux
  • Protection and a cyber mother
  • Windows software player
  • general project

Web technologies and technology:

  • Work environment
  • Server-side web programming language PHP
  • HTML And CSS and javascript
  • Web graphics integration
  • JavaScript libraries such as Node.JS and others
  • PHP framework (.Net)
  • Optimize broadcast enginesSEO and SEA
  • E-Marketing
  • general project

Database:

  • Database management
  • Merise
  • SQL
  • NoSQL
  • PostgreSQL
  • DB programming
  • general project

Information Systems Management:

  • Taurus English
  • Effective professional communication
  • DevOps development and operations
  • Project management
  • Network security

Optional specialization in the third year:

  • development
  • Networks and connectivity
  • Cyber ​​security

Data science majors in the master’s degree

Software development and testing:

  • big data level
  • The blockchain
  • data and virtual
  • Data science
  • machine learning
  • business malaise

Web design and development technologies:

  • Open Data: Actors and Technologies
  • CNIL, data law, copyright, image rights and intellectual property.

Information Systems Management:

  • rotation
  • Individual training
  • Professional certifications

The roles occupied by the data scientist

Business Analytics Professional

A business analytics professional has the skills to leverage information from data to generate insights about a business. To be a data-focused business analytics professional, you must know the technical components related to managing and processing data.

You can find work in companies such as: Walmart, Conduent, Genpact etc.

Business intelligence professional

A business intelligence specialist analyzes past trends using data visualization tools such as Tableau, Power BI, and others to develop and implement business strategies. They also monitor all company performance metrics and provide insight to the concerned department.

jYou can find work in companies such as: Accenture, ZS Associates, Sun Pharma etc.

data scientist

Data scientists help build complex data models and simulations in the big data environment. With a greater focus on mathematics and statistics, these data scientists have a particular interest in reading statistics and building and deploying machine learning models.

You can find work in companies such as: Bank, Amdocs, Oyo, etc.

Big data analysts

The job responsibilities of a Big Data Analyst include collaborating with data scientists and data engineers to ensure streamlined implementation of services and execution of big data operations.

You can find work in companies such as: Novartis, Allerin Tech, Amazon AWS etc.

HR Analytics Specialists

HR Analytics is the hottest trend in the industry. HR analytics professionals work out how to reduce the rate of employee attrition, discover the best recruitment channels and solve daunting problems with the HR function.

You can find work in companies such as: Lenskart, Maersk, Ericsson etc.

Marketing Analytics Specialists

With an abundance of data in all marketing campaigns, analytics empowers professionals Marketing of assessing the success of their marketing initiatives. This is achieved by benchmarking.

You can find work in companies such as: Microsoft, Deloitte, Reliance, etc.

Duties of a Data Science Specialist

Data mining and structuring:

  • Extract data needed for analysis (web scraping, API, etc).
  • Define database cleaning management rules (formatting, deleting duplicates, etc.).
  • Determine management rules for structuring different databases among themselves.
  • Write and draft specifications to automate administration rules for a department information technology or the contracting authority.
  • Data quality control during processing.
  • Define or construct important variables to be included in the statistical models.

Developing AI algorithms:

  • Analyze the data using standard statistical methods.
  • Create and test automatic learning algorithms (machine learning, deep learning, etc.).
  • Build sample training data.
  • Achieve continuous improvement of models.

Fabrication of Machine Learning Models and Statistical Models:

  • Participate in checks during production launch (acceptance).
  • Determine the rules for model maintenance management (monitoring).
  • Manufacturing AI models into applications

Active participation in projects:

  • Participate in workshops expressing internal needs.
  • Accurate understanding of business issues and translating them analytically.
  • Communicate results and solutions with business teams.

Technology Watch on Data Science Tools:

  • Monitor new data science technologies and software solutions.
  • Research and experiment with new modeling and data science methods.

Machine learning, deep learning: What are the differences in data science?

Term indicatesartificial intelligence Mainly down to a concept, without further technical details on how to achieve it. Moreover, some optimization technologies, for example, were considered artificial intelligence 30 or 40 years ago, but they are no longer so today.

If we look at the technologies used today in artificial intelligence, we often find machine learning and/or deep learning.

What is deep learning and how does it work?

Deep learning is a class of brain-inspired machine learning algorithms (hence they are also called “neural networks”) that have shown amazing results on complex tasks like image analysis or the aforementioned NLP.

More precisely, the term deep refers to the fact that a neural network consists of many layers connected in series, hence the idea of ​​depth.

Machine learning is based on general algorithms, whose specificity must be adjusted according to the application situation. In a way, it’s like giving a computer a blank sheet of data and some data to make notes about the correlations it notices within that data (without human intervention).

So in fact, deep learning is a subclass of machine learning algorithms.

MLOps, Machine Learning in Production

Data science is definitely a business-oriented field. However, integrating all of these new technologies, all of this complexity within the company is a real challenge.

Therefore, an additional aspect of data science is the set of IT methods and practices developed with the aim of integrating data projects into companies.

MLOps, as well as DataOps, are micro-approaches (combining methods, processes, tools, and team organization) aimed at facilitating each phase of a data project.

Data science uses

Data science helps us achieve some major goals that were not possible or required a great deal of time and energy just a few years ago, such as:

Examples of data science and applications:

  • Anomaly detection (fraud, disease, crime)
  • Classification (background checks; an email server that rates emails as “important”)
  • Forecasting (sales, revenue and customer retention)
  • Pattern spotting (weather patterns and financial market patterns)
  • Recognition (face, voice, text)
  • Recommendation (based on earned preferences, recommendation engines can refer you to movies, restaurants, and books)
  • Regression (predicting food delivery times, predicting home prices based on amenities)
  • Optimization (scheduling shared rides and package delivery)

Here are some in-depth examples of how companies are using data science to innovate and disrupt their sectors, create new products, and make the world around them more efficient:

Data science in healthcare

Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available across everything from electronic medical record records to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases faster, and explore new treatment options. The sensitivity of patient data makes data security a greater focus in healthcare.

Data science in self-driving cars

Data science is on the way, too. executed Tesla, Ford and Volkswagen Predictive analytics in their autonomous vehicles. These cars use thousands of cameras and tiny sensors to transmit information in real time. Using machine learning, predictive analytics, and data science, self-driving cars can adapt to speed limits, avoid dangerous lane changes, and even pick up passengers on the fastest route.

Data science and logistics

UPS uses data science to maximize efficiency, both internally and along its delivery routes. The company’s Integrated On-Route Navigation and Optimization (ORION) tool uses statistical models and algorithms backed by data science that create optimal routes for delivery drivers based on weather, traffic, and construction. It is estimated that data science saves a logistics company millions of gallons of fuel and delivery miles each year.

Data Science in Entertainment

Ever wonder how Spotify seems to recommend that perfect song for you? Or how does Netflix know just what shows to love? Using data science, these streaming media giants learn your preferences to carefully curate content from their vast libraries that they think will precisely appeal to your interests.

Data science in product and sales andMarketing

Many companies rely on data scientists to build time series forecasting models that help with inventory management and supply chain optimization. Data scientists are sometimes tasked with making proactive recommendations based on budget projections made through financial models. Some even use data mining to segment customers by behavior, and tailor future marketing messages to attract specific groups based on past brand interactions.

Data Science in Finance

Machine learning and data science have saved the financial industry millions of dollars, and an immeasurable amount of time. For example, you use a company’s contract information platform J.P. Morgan Natural language processing to process and extract vital data from thousands of trade credit agreements annually.

Thanks to data science, what would have taken around hundreds of thousands of hours of manual labor to complete is now finished in just a few hours. In addition, financial technology companies such as Stripe and Paypal are investing in data science to create machine learning tools that quickly detect and prevent fraudulent activity.

Data Science in Cyber ​​Security

Data science is useful in every industry, but it may be most important in cybersecurity. For example, the international cybersecurity company Kaspersky uses science and machine learning to discover hundreds of thousands of new malware samples every day. The ability to instantly discover and learn new avenues of cybercrime through data science is essential to our safety and security in the future.

common questions

What does a big data specialist do?

You just typed in your search engine Data science major : This information will be stored and collected using powerful algorithms designed by a data specialist. Then, depending on the identified needs, it will be intersected with other indicators (the question is asked from which region, with what kind of device), and then observed every week / month / year, to conduct studies, develop behavior scenarios or identify market opportunities.

Where does a data scientist work?

Its activity will vary according to the context of the exercise: Develop predictive algorithms and prevent risks in finance or insurance, query databases and prepare reports for the company’s marketing department, or rather methodology and statistics as a consultant.

What does a data scientist do?

A data scientist deals with the analysis and interpretation of data related to an industry or business. He undertakes the following tasks:

Develops a data science program to exploit data and provide the right solutions
Recommend new data exploitation projects and find solutions if necessary
Maintain documentation on data science practices and standards
Communicate with the rest of your team to communicate results clearly

What is natural language processing (NLP)?

A very popular field in AI is Neuro Linguistic Programming (NLP) for Natural Language Processing or Language Analysis.
There are a lot of applications such as machine translation, text classification, voice recognition… This is the basis of voice / virtual assistants such as Siri, Alexa or even automated robots.



[ad_2]

Source link

Leave Your Comment