The growing interconnection between data science and cryptocurrencies results from increased cryptocurrency popularity. The rise of blockchain technology used to conceptualise some elements of cryptocurrencies in particular.

Social-media analysts have seized the opportunity to infuse a data-driven approach to understanding the cryptocurrency market. As a result, the cryptocurrency markets face high volatility and price fluctuations.

Here are some data-driven approaches to crypto investments


1. Bitcoin Price Prediction

Python, a coding script used to develop algorithms, can study the price of Bitcoin and predict the future price of the cryptocurrency. Bitcoin is the most valuable cryptocurrency in the market and is, therefore, a focus of crypto investment data analysts.


2. Altcoin Price Correlation

Altcoins are alternatives to Bitcoin. They are cryptocurrencies that use blockchain technology that allows for secure peer-to-peer transactions.

By studying how one asset's price behaves concerning another, an analyst can determine the relationship between the two assets.

3. Using Social Data to Predict Consumer Behaviour

The standard tracking of supply and demand to predict the market's direction is not the case for cryptocurrencies.

With cryptocurrencies, the trade relies on individuals more than on large companies. Therefore, extensive data gathered from social media profiles, especially Twitter, can gauge the market sentiment by reflecting a clear picture of people's feelings towards the cryptocurrency market's current state. Including the latest events that concern cryptocurrency.


4. Theft Prevention

Even though cryptocurrencies, especially Bitcoin, are very secure and provide limited public data, information is still susceptible to a hack attack.

Although Cryptocurrencies, especially Bitcoin, are very secure and provide a limited amount of public data.

It's also worth considering.

There are many data-driven approaches to crypto-currency and understanding how the cryptocurrency market functions:

  1. The data-driven approaches to crypto investments that have successfully understood the market are still premature. Many of the concepts are theoretical and still require substantial testing and fine-tuning.

  2. Analysts are still becoming aware of specific nuances unique to the crypto market and creating contingencies for their programming models.

  3. The centralised exchange platform poses challenges for programming, and the majority of the daily trade takes place on centralised exchanges. If successful, these scientific approaches to crypto investment could help investors become more successful in the market.

  4. Using these technical Indicators becomes all the more critical as Bitcoin is a currency and not a company with a balance sheet and other financials to reflect on future performance.


Summary:


  • In sum, there is an increasing correlation between cryptocurrency and data science and numerous ways to approach it in theory. Businesses can use data Science and bitcoin to predict the price of cryptocurrencies, altcoin price correlation, and use social data to Predict Consumer Behaviour and theft prevention. However, this will need fine-tuning and testing and centralised in addition to numerous implications.



References:

CNBC, 2021. What is cryptocurrency? Here’s what you need to know about blockchain, coins and more. [online] CNBC. Available at: <https://www.cnbc.com/select/what-is-cryptocurrency/> [Accessed 16 February 2022].

Medium, 2022. Data Science meets Cryptocurrency Trading — more than Just Friends. [online] Medium. Available at: <https://medium.com/the-capital/data-science-meets-cryptocurrency-trading-more-than-just-friends-bf8d6967e141> [Accessed 16 February 2022].

The Balance, 2022. What Are Altcoins?. [online] The Balance. Available at: <https://www.thebalance.com/altcoins-a-basic-guide-391206> [Accessed 16 February 2022].

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Since Covid-19, a human virus has forced virtually the majority of workforces online, making companies exceptionally reliant on digital systems and vulnerable to malicious cyber attacks as a consequence. Since then cyber attacks have increased.


As the paradigm of working-from-home is in full swing here are three ways companies can ramp up their cybersecurity strategies today:


1. Review incident response plans

Ensuring companies have braced themselves for attack with a crystal clear written response plan. A high-quality plan will state people’s tasks, who is to be called for assistance, and what lines and protocols on communication will be within the company (such as board notification).


2. Stay up-to-date on current scams

No one wants to click on an email link only to find that the entire company drive has been compromised by a ubiquitous ‘business email interruption’. Ensure all employees are aware of the most current threats so that it can be prevented.


3. Password protect video conferences

“Zoom-bombing” has become so rampant that GCHQ, the UK’s cyber intelligence agency has issued a warning. Recent cases involve unwanted guests shouting profanity and displaying other inappropriate content. Zoom conferences can be protected by not posting the link publicly, making sure a meeting password and only share the meets with those authorised, and locking the meeting once all guests have arrived.



In the global era of business digitalisation, the work from home paradigm is not likely to end anytime soon. Businesses must review their cybersecurity strategies to stay one step ahead of the wrath of cybercriminals.




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

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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

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