8 ways that AI has changed data science forever

ai and data science

In an era where data is worth more than gold, data scientists are on the cutting edge of business development. But now, a new player has joined the data science game, and its name is Artificial Intelligence (AI). Many in this field wonder how AI will influence their industries, roles, and day-to-day operations.

So, for today’s article, let’s explore eight ways AI has already been incorporated into data science. Some you might know, while others you might not expect! Will your job as a data scientist be affected? Dive in to find out.

8 unexpected ways that AI is influencing data science

  1. Automated data preparation (ADP)

Data science has never been an easy job — you try gathering, cleaning, and processing enormous datasets every day! In fact, 76% of data scientists dread this part of their job. Thanks to the wonders of AI, this time-consuming task has been automated.

Once fed the raw dataset, your AI of choice will return your data in a usable format. This way, data scientists can focus on their most important tasks. ADP also reduces the chances of human error.

  1. Find patterns in huge datasets

AI-powered neural networks are a powerhouse when it comes to anomaly and pattern detection in complex datasets. These algorithms can spot relationships between data that otherwise would have been missed.

Data scientists often know exactly what they’re looking for and might not realize the more subtle connections between data. These patterns, after all, are the insights data scientists are searching for.

  1. The new age of predictive analytics

With this powerful ability to find even the faintest relationships between data, predictive analytics have also evolved. Arguably the most important role of data scientists, predictions are what give businesses a leg-up on their competition.

  • Anomaly and pattern detection help to forecast future trends.
  • Datasets can be fed to AI chatbots for automated predictions.
  • AI can assess historical data before making predictions.
  • Customer intent, market trends, and financial forecasts can be automated.
  1. Chatbots can assist in data exploration

In the end, it’s rarely data scientists that make the big business decisions based on their findings. Shareholders and executives will likely want to understand your findings, too — here’s where AI can be a tour guide of sorts:

  • Chatbots can answer Q&As regarding the dataset;
  • Automatically simplify or advance technical language used;
  • Any staff member can be walked through the dataset and findings, regardless of technical knowledge.
  1. Fraud prevention safeguards data scientists

AI has allowed the cybersecurity industry to advance data protection in various ways. One of the greatest improvements caused by AI is the strides taken in fraud prevention technology.

Data scientists are no strangers to cyber attacks, being the gatekeepers of confidential business data. All data scientists should have next-gen firewalls, high-end encryption (e.g.: Surfshark VPN), and flawless cyber hygiene.

  1. Recruitment is precise and automated

Depending on the industry, data scientists have varied roles within their companies. However, an uncommon role for a data scientist is in the recruitment process. Yet, with AI at their side, data scientists are being tasked with streamlining this process.

  • AI can filter through thousands of CVs and resumes;
  • All suitable candidates are compiled according to the desired skills;
  • Everything from work history to personality can be automatically assessed.
  1. Menials tasks are turbocharged with AI

Forming the bulk of a data scientist’s job are various menial tasks that often consume entire work days. AI can help data scientists to instantaneously produce:

  • Boiler-plate or unique code;
  • Email responses;
  • Data summaries and reports;
  • Visual components like graphs, diagrams, etc.

AI not only performs these menial tasks as accurately as humans but also in a fraction of the time.

  1. Transparency has never been easier

Many open-source AI models allow companies, their customers, and data scientists to gain deep insights into the AI clockwork. This enables businesses to comprehend the data science process and share this transparency with their shareholders. This added transparency enables companies to:

  • Explore and understand the technical side of their AI helpers;
  • Ensure a high ethical standard with no bias detected in the AI;
  • Understand complex relationships and patterns in datasets.

Conclusion

AI’s impact on the world of data science is inevitable. Data scientists have welcomed the advances of AI as a tool to reach deeper insights and make precise market predictions. From preventing cyber attacks to offering transparency, the effects of AI on data science have only begun. So, what impact do you think AI will have on data science next?

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