The 2025 Executive Guide To Data Science and AI
Your Essential Handbook for Data Science & AI Strategies.
Your 2025 Executive Guide to Data Science and AI
This series of white papers lays out the essential steps you need to take to ensure your business stays at the forefront of an increasingly data-driven and automated world. We cover everything you need to know about growing a data science team, the current trends in machine learning, data visualisation, essential data science tools and technologies, automation, deployment, how to prove value with data science and also give our thoughts on the next ‘big ideas’ that are set to take the world of AI in business by storm, including a dedicated chapter on Generative AI.
Practical AI and Data Science
We reveal the best approach to implementing an AI strategy across each of these areas, providing expert advice and industry best practices throughout the guide. We hope you enjoy it!
This whitepaper is a practical guide to developing your AI and data science strategy into 2025 and beyond. We cover all the key technologies, tools and techniques that you need to build a data science team that delivers real value for your business.
"If you read one data strategy guide this year, make it this one - the complete guide to data science and AI in 2025."
-- Head of Data Science
Chapters Overview:
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1. Building a Modern Data Science Team
A look at the essential roles and structures for building effective data science teams, including data scientists, data engineers, and data analysts.
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2. Machine Learning (ML)
Explore the latest trends and techniques in machine learning, including models like XGBoost, NGBoost, and self-supervised learning.
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3. Data Visualization
Discover the best practices for creating compelling data visualizations and choosing the right platform such as Tableau or PowerBI.
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4. Tools and Technologies
An overview of the key tools and technologies utilized by data science teams, from programming languages to cloud storage solutions.
5. Automation
Understand the workflows and tools, like Apache Airflow, that can help automate data validation, model training, and more.
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6. Deployment
Learn about different deployment strategies, including batch processing, API integration, and streaming, with practical steps for implementation.
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7. Proving Value
Methods for measuring the tangible value created by data science projects, focusing on customer acquisition, lifetime value, cost savings, and time savings.
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8. Big Ideas
Insights into future trends in AI, including reinforcement learning, creative AI, language modeling with GPT-3, nowcasting, and human-AI interaction.
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9. Generative AI
An exploration of generative AI capabilities, large language models (LLMs), and how businesses can leverage these advancements for growth and efficiency.
