Companies can overcome potential cybersecurity threats by using machine learning to identify and stop them from doing damage.


Machine learning is used in data science and uses algorithms of previous datasets to improve a system automatically through experience.


Machine Learning allows data scientists to:


  1. Predict risks based on past exploits and behaviour patterns.

  2. Identify outliers in data automatically.

  3. Productive use of large amounts of data

  4. Find cybersecurity threats using devious data


Implications of machine learning:


This ensures businesses and the wider community are protected from a breach in their systems.

  1. Human intervention is still crucial

  2. Over-reliance on artificial intelligence in cybersecurity can create a false sense of safety

  3. Not everything can be solved with artificial intelligence & data


congentBI can ensure that AI learning supplements and enhances human efforts, rather than replacing them. Machine Learning is is a tool in the toolkit - It’s more essential than ever for businesses bulletproof against cybersecurity attacks.


Is machine learning the most effective way to overcome cybersecurity challenges?





#datascience #cybersecurity #technology data #datastrategy #cyberattack #internet #security

Malware attacks have become sophisticated than ever and more difficult to detect and fight. Luckily data science is also speeding up.

Data Science is a promising response to the increased risk of cyber attacks in 2020:

  1. Data Acquisition

  2. Periodically revisit and update the enterprise security plan as new knowledge and tools become available.

  3. Awareness, communication and coordination are key to a cybersecurity culture

  4. Security analytics enables data-driven decision making throughout the cycle

Solution:

Over 85% of data science using business reported a decent decline in security breaches after introducing Big Data analytics into their operations.

Applying the power of Big Data can improve your data-management techniques and cyberthreat-detection mechanisms. Monitoring and improving your approach can bulletproof your business against intruders.



#datascience #cybersecurity #technology #data #datastrategy #cyberattack #internet #security #cybercrime #hacker


Closing the achievement gap between higher and lower-achieving student groups is a great focus of many education policies. Why use data?


  1. To understand skill gaps of low achieving students.

  2. Administer frequent assessments of students.

  3. Receive professional development about linking low performing student data to instructional strategies.

  4. Recommend using data and teacher collaboration when asked: “what should schools do to close the gap?”.


Assumptions:

Data is broadly assumed in education as the objective, efficient, and less discriminatory method to evaluative processes that has traditionally been heavily influenced by subjectivity.


Solution:

Schools need frequent and reliable data, either in the form of diagnostic assessments, quantitative or qualitative data. Teachers and school leaders need frequent feedback to identify strengths and weaknesses. These considerable factors contribute to lessening the equity gap.



#datascience #school #education #backtoschool #businessintelligence #ukschools #schoolperformance #alevels #gcse #teaching #disadvantaged


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