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As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.
With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills i...
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The Transformative Power of Data Science in Career Development" suggests a focus on how data science can profoundly impact an individual's professional trajectory and advancement. This theme can be explored in various ways, including:
Skill Development: Emphasizing how acquiring data science skills can open up new career opportunities and enhance employability. This might include learning programming languages like Python or R, mastering statistical analysis techniques, and gaining proficienc...
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Data science is increasingly becoming necessary for several reasons:
Data-driven Decision Making: In today's digital age, organizations collect vast amounts of data from various sources. Data science enables these organizations to analyze and extract valuable insights from this data, which can inform strategic decision-making and drive business growth.
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Can you tell us about your journey and what motivated you to co-found ForwardLane, particularly focusing on AI’s role in financial services?
My journey into fintech came to me when I worked at the multi-asset alternative asset manager, CQS. There, we could find insights and act on them far ahead of financial institutions. When I saw how difficult it was for advisors to get to insights, I came up with the vision of an AI co-pilot for every financial advisor. With EMERGE, that vision is now a r...
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Many businesses have learned the hard way that not every AI project leads to glory and success. In fact, a 2023 CIO.com survey found that more than half of AI projects fail to produce actionable results at all. There are many reasons for this, but one of the biggest causes we frequently see is a disconnect between the data scientists who are actually building the models and the end users who would consume or use the models.
Most data scientists would agree that deep data exploration of all t...
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