Starter Guide: AI basics for Analytics Engineers

A practical guide to help analytics engineers get started, and get ahead, with AI.

June 19, 2024
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 min read
Starter Guide: AI basics for Analytics Engineers

You have heard it a million times before: as AI grows, so does the need for Analytics Engineers to get creative and up-skill. Enough with high-level statements and “inspirational” LinkedIn posts; here’s an ACTUAL practical guide to help you get started and get ahead.

Start Here: The Basics of AI and ML

To effectively use AI in analytics, you need a solid understanding of its fundamentals. Start with the basics:

Online Courses:

  • "Machine Learning" by Andrew Ng: This course is one of the most popular and highly recommended introductions to machine learning. It covers a wide range of topics from linear regression to neural networks, with practical exercises.
  • "Introduction to Artificial Intelligence (AI)" by IBM on Coursera: This course provides a broad overview of AI concepts, including machine learning, neural networks, and AI applications. It is well-reviewed for its clarity and accessibility.
  • "Elements of AI" by the University of Helsinki: This course aims to demystify AI by providing a basic understanding of what AI is, how it works, and what it can do. It's highly accessible and has received excellent reviews for its engaging content.


Get to know key concepts such as neural networks, supervised and unsupervised learning, and deep learning. Remember, you don't need to become a data scientist overnight, but a robust understanding of these concepts is crucial.

Get Familiar with AI Frameworks and Tools

Theoretical knowledge is vital, but practical experience is indispensable. Start experimenting with popular AI tools and frameworks to solidify your understanding and skills.


  • Key Features:
    • TensorFlow Serving: For deploying ML models in production environments.
    • TensorFlow Lite: For deploying models on mobile and IoT devices.
    • TensorFlow.js: For training and deploying models in the browser.
  • Practical Knowledge:
    • Utilize for efficient data input pipelines.
    • Leverage tf.function to optimize performance with graph execution.
    • Explore pre-trained models and fine-tune them using TensorFlow Hub.


  • Key Features:
    • TorchScript: For transitioning from eager execution to graph execution for deployment.
    • Distributed Training: Native support for distributed training across multiple GPUs and nodes.
    • Integration with Python Ecosystem: Seamless integration with Python libraries like NumPy and SciPy.
  • Practical Knowledge:
    • Use torch.nn for defining neural networks.
    • Leverage torch.optim for optimization algorithms.
    • Employ for data loading and preprocessing.


  • Key Features:
    • Modularity: Simple building blocks for creating neural network layers, loss functions, optimizers, and more.
    • Extensibility: Easily add custom layers and models.
    • Pre-trained Models: Access a variety of pre-trained models for transfer learning through Keras Applications.
  • Practical Knowledge:
    • Start with Sequential API for building simple models and progress to the Functional API for complex architectures.
    • Utilize built-in callbacks like EarlyStopping and ModelCheckpoint to enhance training processes.
    • Leverage the Keras Tuner for hyperparameter optimization.


  • Key Features:
    • Wide Range of Algorithms: Support for regression, classification, clustering, and dimensionality reduction.
    • Pipeline Utilities: Tools for building complex machine learning workflows.
    • Model Selection: Functions for cross-validation, hyperparameter tuning, and model evaluation.
  • Practical Knowledge:
    • Use Pipeline and ColumnTransformer for streamlined preprocessing and modeling pipelines.
    • Leverage GridSearchCV and RandomizedSearchCV for hyperparameter tuning.
    • Explore metrics module for robust model evaluation metrics.

Start Building Your Projects

Start Simple:

  • Linear Regression: Predicting housing prices based on features like square footage, location, etc.
  • Classification: Building a classifier to identify spam emails.

Gradually Increase Complexity:

  • Deep Learning: Implement convolutional neural networks (CNNs) for image recognition.
  • Time Series Analysis: Use recurrent neural networks (RNNs) or long short-term memory (LSTM) networks for stock price prediction.

Experiment: Integrate AI into Your Analytics Projects

Integrating AI into your analytics projects can significantly enhance their impact. Here’s how to incorporate advanced AI techniques:

Predictive Analytics:

Time Series Forecasting:

  • Use models like ARIMA, Prophet, or LSTM networks for time series prediction.
  • Apply feature engineering to extract meaningful features such as lagged variables, moving averages, and seasonal indicators.

Regression Analysis:

  • Implement linear and non-linear regression models using libraries like Scikit-Learn or TensorFlow.
  • Utilize techniques such as polynomial regression, ridge regression, and lasso regression to handle complex relationships and prevent overfitting.

Model Evaluation:

  • Use cross-validation techniques to evaluate model performance.
  • Analyze metrics such as RMSE, MAE, and R² to select the best model.

Natural Language Processing (NLP):

Text Preprocessing:

  • Perform text cleaning with tokenization, stop-word removal, and stemming/lemmatization.
  • Use libraries like NLTK, SpaCy, and Transformers for advanced text processing.

Feature Extraction:

  • Employ techniques like TF-IDF, word embeddings (Word2Vec, GloVe), and contextual embeddings (BERT) for converting text into numerical features.
  • Implement topic modeling using LDA or Non-Negative Matrix Factorization (NMF).

NLP Applications:

  • Sentiment Analysis: Build classifiers to detect sentiment from text using models like logistic regression, SVM, or BERT.
  • Named Entity Recognition (NER): Extract entities such as names, dates, and organizations using SpaCy or Hugging Face’s Transformers.
  • Text Classification: Develop models for categorizing text data into predefined classes using deep learning frameworks like TensorFlow or PyTorch.

Anomaly Detection:

Modeling Techniques:

  • Statistical Methods: Implement statistical methods such as Z-score, moving average, and seasonal decomposition.
  • Machine Learning Models: Use clustering algorithms like DBSCAN and isolation forests for unsupervised anomaly detection. Deploy supervised learning models such as one-class SVMs and autoencoders for identifying anomalies.


  • Fraud Detection: Detect fraudulent transactions or behaviors using anomaly detection techniques.
  • Quality Control: Monitor production processes and identify deviations from normal operation to maintain quality standards.

Evaluation and Deployment:

  • Assess model performance using precision, recall, F1-score, and ROC-AUC.
  • Implement real-time anomaly detection systems using streaming data platforms like Apache Kafka and Spark Streaming.

Experimentation and Optimization:

Hyperparameter Tuning:

  • Use grid search or randomized search for hyperparameter optimization.
  • Implement advanced techniques like Bayesian optimization with libraries such as Hyperopt or Optuna.

Model Selection:

  • Experiment with different algorithms to identify the best fit for your data and use cases.
  • Perform model stacking and ensembling to improve predictive performance.

Continuous Learning:

  • Keep models updated with new data through techniques like online learning or periodic retraining.
  • Monitor model performance over time and implement drift detection mechanisms to ensure sustained accuracy.

By integrating these AI techniques into your analytics projects, you can derive more insightful and actionable outcomes, significantly enhancing the value and impact of your analytics efforts.

Get Involved with the Community and Stay Updated

AI and analytics are rapidly evolving fields. Staying updated and networking with professionals is crucial to staying ahead of the game.

Examples of Online Forums and Communities:

Blogs and newsletters:

  • Towards Data Science: Articles, tutorials, and guides on a wide range of topics in data science, machine learning, and AI.
  • The Gradient: A platform for in-depth discussions on AI research, ethics, and applications, featuring articles by experts and researchers.
  • TLDR AI: Be an AI expert in 5 minutes, with daily updates on AI, ML, and data science.


  • Data Skeptic: Explores machine learning, AI, and data science topics through interviews with industry experts and researchers.
  • Lex Fridman Podcast: Features in-depth conversations on AI, technology, and science with a variety of guests from different fields.
  • The TWIML AI: Hosted by Sam Charrington, featuring discussions with AI and machine learning practitioners on current trends and research.

Renowned Publications & Hubs:

  • MIT Technology Review: Stay informed about AI advancements, industry trends, and emerging technologies.
  • IEEE Xplore: Access a vast library of research papers on AI, machine learning, and related fields.
  • ACM Digital Library: Explore papers from conferences like SIGKDD, ICML, and NeurIPS.


Stay curious, stay competitive, and never stand still.

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