Mastering the AI Basics: The Must-Know Data Skills Before Tackling LLMs

LLMs, AI agents, and generative AI are the buzzwords lighting up the data science world. But if you’re serious about working with LLMs or any advanced AI system, you need to start at the foundation. Because no model — no matter how powerful — can perform well on poorly prepared data or without a solid development pipeline based on AI basics.

Before you dive into prompt engineering or fine-tuning, it’s essential to master the AI basics and data science fundamentals. But what are they? Well don’t worry because below we’ll break down the core data skills every aspiring LLM practitioner needs to understand.

1. Data Wrangling: Taming the Raw Data

Why it matters: Real-world data is messy. It’s scattered across sources, filled with inconsistencies, and rarely ready for modeling.

What you’ll do: Data wrangling is about acquiring, consolidating, and reshaping raw data into a usable form. Think of it as the prep work before cooking. You’ll extract from APIs, query databases, and convert formats to make your dataset analysis-ready.

If your wrangling skills are weak, you’ll spend more time fixing issues downstream.

2. Data Cleaning: Eliminate the Noise

Why it matters: Noisy, incomplete, or inconsistent data can sink even the best-trained model.

What you’ll do: Cleaning involves handling missing values, correcting errors, standardizing formats, and filtering outliers. Clean data leads to cleaner insights — especially crucial in NLP tasks, where slight variations in text formatting can dramatically affect results.

3. Data Transformation: Reshaping for Insight

Why it matters: Models require structured, numerical inputs. Your job is to mold raw inputs into model-ready features.

What you’ll do: This includes normalization, aggregation, encoding categorical variables, and converting temporal data into usable formats. When working with LLMs, this can also mean tokenization and sequence padding.

The right transformations directly affect model accuracy.

4. Data Manipulation: Flexibility in Action

Why it matters: Quick exploration. Fast iteration. When deadlines are tight, fluency with data manipulation tools (like Pandas or SQL) keeps you agile.

What you’ll do: You’ll filter, merge, pivot, group, and reshape data constantly. This skill powers rapid experimentation — essential for tasks like fine-tuning LLMs or testing new feature sets.

5. Data Profiling: Know What You’re Working With

Why it matters: Jumping into modeling without understanding your data is like flying blind.

What you’ll do: Profiling gives you visibility into distributions, correlations, missingness, and anomalies. You’ll generate summary statistics and visualize patterns — helping you make smarter decisions during preprocessing and feature engineering.

6. Feature Engineering: Turn Raw Data into Gold

Why it matters: Features drive performance. Poor features = poor results.

What you’ll do: You’ll extract new variables, combine fields, apply domain knowledge, and encode signals hidden in raw data. In language models, this might mean generating TF-IDF scores, word embeddings, or task-specific labels.

Feature engineering can often matter more than model selection.

7. Dataset Splitting: Train, Test, Trust

Why it matters: Without proper data splitting, you risk overfitting — and misleading yourself about your model’s performance.

What you’ll do: This step divides your data into training, validation, and test sets. Techniques like stratified sampling and time-based splits ensure your evaluation remains unbiased.

Good splits enable reproducible results — critical when publishing research or scaling to production.

8. Model Selection: Choose Wisely

Why it matters: Different problems require different algorithms — and sometimes even different modeling philosophies.

What you’ll do: Model selection balances performance, interpretability, training time, and resource constraints. Whether it’s a simple logistic regression or a transformer-based architecture, choosing the right model saves time and boosts outcomes.

It’s not just about the “best” model — it’s about the right one.

9. Model Training: Where the Magic Happens

Why it matters: Training is the process of learning patterns. Do it wrong, and your model learns noise instead.

What you’ll do: You’ll fine-tune hyperparameters, apply regularization, use early stopping, and track loss functions. For LLMs, this may involve transfer learning on domain-specific text.

Strong training practices mean faster convergence and better generalization.

10. Model Evaluation: Measure What Matters

Why it matters: Accuracy isn’t everything. In many cases, it’s not even the right metric.

What you’ll do: You’ll choose and interpret metrics like precision, recall, F1-score, ROC-AUC, or BLEU depending on your use case. Evaluation also includes error analysis and cross-validation to understand what your model doesn’t do well.

It’s not just about performance — it’s about trust.

Final Thoughts: Build the Foundation. Unlock the Future.

Every AI basics skill above is a skill you’ll revisit again and again in your data science career. Whether you’re deploying a chatbot or researching the next iteration of LLMs, these fundamentals are your leverage point.

So, if you’re ready to move beyond buzzwords and build the future, start at the root.

Want a hands-on way to build these AI basics? Now is the time to join the ODSC East Mini-Bootcamp or take the Data Primer Course. With ODSC, you’ll learn the essentials, reinforce your foundation, and accelerate your path to working with advanced models like LLMs.

​Embark on a transformative journey into the world of Artificial Intelligence with ODSC’s 5-week Spring AI Bootcamp, running from April 1st to April 29th, 2025. This comprehensive program is meticulously designed to guide participants from foundational concepts to advanced AI applications.​