How to Embrace Artificial Intelligence as a Business Analyst

As a business analyst with over 25 years of experience, I was unsure where to start in the process of understanding how AI could be used to my advantage in my current position. I curated a list of the web resources I have used to increase my knowledge and understand how I can use AI in my professional settings.

I am not affiliated with any of the sites I recommend, I am sharing these to save you time and effort so that you and get right to the source.

Getting started as a Business Analyst (BA) to understand AI involves a structured learning approach and practical application of AI concepts within your organization. Here’s a step-by-step guide to help you get started:

Getting Started

1. Learn the Basics of AI:
   • Begin by gaining a foundational understanding of what AI is, its history, and its various subfields, including machine learning, deep learning, natural language processing, and computer vision.
2. Online Courses and Tutorials:
   • Enroll in online courses and tutorials. Platforms like Coursera, edX, Udacity, and Khan Academy offer introductory courses on AI and machine learning.
3. AI Books and Resources:
   • Read books and articles on AI and machine learning. Recommended books include “Python Machine Learning” by Sebastian Raschka and “Deep Learning” by Ian Goodfellow.
4. Programming Skills:
   • Learn programming languages commonly used in AI, such as Python. Familiarize yourself with libraries like TensorFlow and PyTorch for deep learning, and scikit-learn for machine learning.
5. Data Analysis and Statistics:
   • Develop your data analysis skills, as a solid foundation in statistics and data manipulation is crucial for working with AI algorithms.

Find Communities and Mentors

1. Online AI Communities:
   • Join AI-focused online communities and forums like Stack Overflow, Reddit’s r/MachineLearning, and LinkedIn groups to ask questions and learn from experienced practitioners.
2. Hands-On Projects:
   • Apply your learning through practical projects. Start with simple tasks like data analysis, then progress to more complex projects involving machine learning models.
3. Online AI Platforms:
   • Explore AI platforms like Google Cloud AI, Microsoft Azure Machine Learning, and AWS Machine Learning to get hands-on experience with AI tools and services.
4. Data Collection and Preparation:
   • Understand the importance of data in AI. Learn how to collect, clean, and preprocess data, as high-quality data is essential for model training.
5. Machine Learning Algorithms:
   • Study common machine learning algorithms, such as linear regression, decision trees, and clustering techniques. Understand when and how to apply them.
6. Deep Learning:
   • Dive into deep learning by studying neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) for more advanced AI applications.
7. AI Ethics and Bias:
   • Familiarize yourself with ethical considerations and potential biases in AI systems. Understanding fairness, transparency, and accountability is crucial.
8. Collaborate with Data Scientists:
   • Collaborate with data scientists or AI experts within your organization. Learn from their expertise and work together on AI projects.
9. Business Domain Knowledge:
   • Combine your BA skills with AI knowledge to identify business problems that AI can solve. Develop domain-specific expertise to provide valuable insights.

Keep Learning and Get Certified

1. Continuous Learning:
   • AI is a rapidly evolving field. Stay updated by reading research papers, attending conferences, and participating in online courses or webinars.
2. Certifications:
   • Consider obtaining relevant certifications, such as Google’s TensorFlow Developer Certificate or AWS Certified Machine Learning Specialty, to validate your AI skills.
3. Experiment and Iterate:
   • Don’t be afraid to experiment and learn from failures. AI projects often involve trial and error, so be patient and persistent.
4. Document Your Learning:
   • Keep a learning journal or portfolio to track your progress and showcase your AI-related accomplishments to potential employers or within your organization.

In Summary

Remember that mastering AI as a BA is an ongoing journey, and practical experience is invaluable. As you gain more experience, you’ll become better equipped to identify AI opportunities, work on AI-driven projects, and contribute to data-driven decision-making within your organization.