Data Scientists’ Career Roadmap in the AI Ecosystem

AI Art by NUWA NFTs

I’ve created two Data Scientist roadmaps based on the most frequently asked questions I receive. The first one is more entry-level, aimed at those who are just starting out. The second roadmap is a comprehensive guide, detailing the technical competencies expected from a data scientist within the AI ecosystem.

For those new to the field, there’s an entry-level roadmap for aspiring data scientists: you can access it via this link: https://medium.com/p/df0810a1d51b/edit

Of course, acquiring all the skills listed here is a long-term process. Especially with the rise of AI-related work, the industry now expects professionals to have touched upon a few of these steps.

Here is the comprehensive roadmap:

1.Fundamentals

Statistics and Probability:

• Detailed Probability Concepts:
  — Conditional Probability and Bayes’ Theorem: Using Bayes’ theorem with real-world application examples.
  — Probability Distributions: In-depth study of continuous (Normal, Exponential) and discrete (Binomial, Poisson) distributions.
  — Monte Carlo Simulations: Simulating complex systems and modeling stochastic processes.
• Statistical Tests and Power Analysis:
  — Parametric and Non-Parametric Tests: T-test, ANOVA, Mann-Whitney U test, Kruskal-Wallis test.
  — Power Analysis: Conducting power analysis to determine sample size.
  — Bootstrapping and Permutation Tests: Resampling methods on data.

Mathematics:

• Matrices and Vectors:
  — Matrix Factorizations: SVD, LU Decomposition, QR Decomposition.
  — Eigenvalue Problems: Use of eigenvalues in dimensionality reduction methods such as PCA and LDA.
• Integral and Differential Calculus:
  — Multivariate Calculus: Partial derivatives, chain rule, Jacobian and Hessian matrices.
  — Gradient Descent: Derivative and gradient concepts as the foundation of optimization algorithms.

Programming Languages and Libraries:

• Data Science with Python:
  — Data Manipulation and Visualization: Advanced data manipulation with pandas, visualization with matplotlib and seaborn.
  — Scientific Computing: High-performance computations with numpy and scipy.

R Programming:

• Data Analysis in R: Data cleaning with dplyr, tidyr, and visualization with ggplot2.
• Statistical Modeling: Modeling with libraries like caret, glmnet, randomForest.

Resources:

• “The Elements of Statistical Learning” by Hastie, Tibshirani, and Friedman.
• “Introduction to Probability” by Dimitri P. Bertsekas and John N. Tsitsiklis.
• “All of Statistics” by Larry Wasserman.
• “Python for Data Analysis” by Wes McKinney.

Courses:

• Coursera: “Statistics with R” by Duke University.
• Khan Academy: “Probability and Statistics” (Basic and advanced topics).
• Udemy: “Mathematics for Data Science” by Kirill Eremenko.

2. Data Manipulation, Exploration, and Insight Extraction

Data Quality and Cleaning:

• Data Quality Control: Metrics for assessing data quality (completeness, consistency, accuracy, timeliness).
• Data Cleaning Techniques:
  — Handling Missing Data: Simple imputation, multiple imputation, interpolation techniques.
  — Outlier Analysis: Using robust scaler, IQR-based outlier cleaning, z-score based anomaly detection.
  — Time Series Cleaning: Removing seasonal effects and trend analysis in time series data.

Data Visualization Techniques:

• Advanced Graphs:
  — Heatmaps, Pairplots, Violin Plots: Visualizing complex datasets using Seaborn.
  — Interactive Visualization: Creating interactive graphs using Plotly and Bokeh.
• Dashboard Development:
  — Tableau and Power BI: Data visualization and interactive dashboard creation.
  — Jupyter Notebook and Streamlit: Rapid prototyping and interactive report creation using Python.

Exploratory Data Analysis (EDA):

• Techniques for Data Exploration:
  — Pandas Profiling and Sweetviz: Creating automatic EDA reports.
  — Correlation Matrix: Visually examining the relationship between variables in datasets.
• Correlation and Causality:
  — Granger Causality Test: Analyzing causal relationships in time series.
  — Partial Correlation: Correlation analysis controlling for the effect of third variables.

Insight Extraction:

• Advanced Analysis Techniques:
  — Segmentation Analysis: Customer segmentation using algorithms like K-Means, DBSCAN.
  — Time Series Analysis: Forecasting with models like ARIMA, SARIMA, Prophet.
  — A/B Testing: Designing targeted experiments to optimize business decisions.

Resources:

• “Python Data Science Handbook” by Jake VanderPlas.
• “Storytelling with Data” by Cole Nussbaumer Knaflic.
• “Data Science for Business” by Foster Provost and Tom Fawcett.
• “Interactive Data Visualization for the Web” by Scott Murray.

Courses:

• Coursera: “Data Visualization with Python” by IBM.
• Udemy: “Python for Data Analysis and Visualization” by Jose Portilla.
• DataCamp: “Interactive Data Visualization with Bokeh”.
• LinkedIn Learning: “Tableau Essential Training”.

3. Machine Learning

Fundamentals of Machine Learning:

• Types of Algorithms: Supervised, unsupervised learning, semi-supervised learning, reinforcement learning.
• Model Evaluation:
  — Performance Metrics: Accuracy, precision, recall, F1 score, ROC-AUC, PR curves.
  — Model Reliability: Calibration curves, measuring model uncertainty.
  — Model Robustness: Adversarial testing, out-of-sample performance evaluation.
• Model Complexity and Regularization:
  — Regularization Techniques: Lasso (L1), Ridge (L2), ElasticNet.
  — Cross-Validation Strategies: Stratified K-Fold, Time Series Split, Group K-Fold.
  — Overfitting and Underfitting: Bias-variance trade-off, model selection, and tuning.

Supervised Learning:

• Regression Techniques:
  — Simple and Multiple Regression: Feature engineering, diagnostic tools for regression.
  — Polynomial Regression: Modeling curvilinear relationships, model selection, and validation.
  — Regression Trees: Decision trees, ensemble methods (Random Forests, Gradient Boosting).
• Classification Algorithms:
  — KNN and Logistic Regression: Basic algorithms for classification problems.
  — Support Vector Machines (SVM): Defining linear and non-linear decision boundaries.
  — Naive Bayes: Text classification and spam filtering.

Unsupervised Learning:

• Clustering Techniques:
  — K-Means and GMM: Clustering and density-based modeling.
  — Hierarchical Clustering: Defining cluster hierarchies with dendrograms.
  — DBSCAN and HDBSCAN: Density-based clustering, outlier detection.
• Dimensionality Reduction:
  — PCA (Principal Component Analysis): Identifying key components in high-dimensional data.
  — LDA (Linear Discriminant Analysis): Dimensionality reduction based on class separation.
  — t-SNE and UMAP: Visualization and exploration of high-dimensional data.

Advanced Algorithms and Techniques:

• Ensemble Learning:
  — Bagging and Boosting: Random Forest, AdaBoost, Gradient Boosting (GBM), XGBoost, LightGBM, and CatBoost.
  — Stacking and Blending: Combining different models to create a stronger predictor.
  — Voting Classifier: Combining different algorithms using majority or weighted voting.
• Anomaly Detection:
  — Isolation Forest: Tree-based anomaly detection.
  — One-Class SVM: Anomaly detection in high-dimensional data.
  — Elliptic Envelope: Anomaly detection based on Gaussian distribution.
• Time Series Analysis:
  — ARIMA and SARIMA: Classical methods for time series analysis and forecasting.
  — Prophet: Time series modeling with seasonality and trend developed by Facebook.
  — LSTM (Long Short-Term Memory): Deep learning applications in time series data.

Feature Engineering and Feature Selection:

• Feature Engineering:
  — Interaction Features: Creating interaction terms between variables.
  — Polynomial Features: Using polynomial terms to enhance model accuracy.
  — Datetime Features: Extracting and utilizing time-based features in models.
• Feature Selection:
  — Embedded Methods: Feature importance metrics embedded in regularization methods (Lasso, Ridge), decision trees, Random Forest.
  — Filter Methods: ANOVA, Pearson Correlation, Chi-Square tests.
  — Wrapper Methods: Recursive Feature Elimination (RFE), Forward/Backward Selection.
• Model Tuning and Optimization:
  — Grid Search and Random Search: Basic search techniques for hyperparameter optimization.
  — Bayesian Optimization: More efficient methods for hyperparameter optimization (Hyperopt, Optuna).
  — AutoML: Rapid model development using automated machine learning tools (H2O AutoML, Auto-sklearn, TPOT).

Resources:

• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron.
• “Ensemble Methods: Foundations and Algorithms” by Zhi-Hua Zhou.
• “Pattern Recognition and Machine Learning” by Christopher M. Bishop.
• “Machine Learning Engineering” by Andriy Burkov.
• “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson.

Courses:

• Coursera: “Advanced Machine Learning Specialization” by National Research University Higher School of Economics.
• Udacity: “Machine Learning Engineer Nanodegree”.
• DataCamp: “Ensemble Learning”.
• Fast.ai: “Practical Deep Learning for Coders”.

4. Deep Learning

Neural Networks and Basic Structures:

• Feedforward Neural Networks: Structure of deep neural networks, activation functions, forward and backward propagation algorithms.
• Deep Learning Regularization Techniques:
• Dropout: Randomly disabling nodes in neural networks to prevent overfitting.
• Batch Normalization: Speeding up the learning process by normalizing data distribution during training.
• Weight Decay: Penalizing weights to prevent overfitting.

Convolutional Neural Networks (CNN):

• Convolutional Layers: Extracting image features, kernel sizes, and padding strategies.
• Pooling Layers: Downsampling, Max Pooling, Average Pooling.
• Advanced CNN Architectures: Deep CNN architectures like ResNet, VGG, Inception, EfficientNet.
• Application of CNNs:
• Image Classification: Image classification.
• Object Detection: Object detection algorithms like R-CNN, Fast R-CNN, YOLO, SSD.
• Image Segmentation: Image segmentation with U-Net, Mask R-CNN.

Recurrent Neural Networks (RNN):

• Basic Structure: Modeling time series data and sequential data.
• LSTM and GRU: Advanced RNN structures for capturing long-term dependencies.
• Attention Mechanism: Mechanism underpinning Transformer architectures, seq2seq models.

Generative Adversarial Networks (GAN):

• GAN Structure: Game theory-based battle between Generator and Discriminator.
• GAN Applications: Synthetic data generation, style transfer, super-resolution.
• Advanced GAN Techniques: Wasserstein GAN, Conditional GAN, CycleGAN.

Transformer Models and NLP:

• Self-Attention: Fundamental mechanism of Transformer architectures for modeling long-term dependencies.
• BERT and GPT: Masked Language Modeling, Next Sentence Prediction, Text Generation.
• Natural Language Understanding (NLU): Sentiment analysis, named entity recognition (NER), machine translation.

Libraries and Tools:

• TensorFlow and Keras: Popular frameworks for developing deep learning models.
• PyTorch: Flexible framework for model development and rapid prototyping.
• Hugging Face Transformers: Transformer-based NLP models.
• ONNX: Model format compatible across different platforms.

Resources:

• “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron.
• “Neural Networks and Deep Learning” by Michael Nielsen.
• “GANs in Action” by Jakub Langr and Vladimir Bok.
• “Transformers for Natural Language Processing” by Denis Rothman.

Courses:

• Coursera: “Deep Learning Specialization” by Andrew Ng.
• Udacity: “Deep Learning Nanodegree”.
• edX: “Deep Learning for Self-Driving Cars” by MIT.
• DataCamp: “Convolutional Neural Networks in Python”.

5. Big Data and Distributed Computing

Big Data Ecosystems:

• Hadoop Ecosystem: HDFS (Hadoop Distributed File System), MapReduce, YARN, Hive, Pig.
• Apache Spark: In-memory computation, RDDs, DataFrames, Datasets, Spark SQL, Spark Streaming, GraphX.
• Flink and Storm: Real-time data processing, event stream processing.
• Data Lake vs. Data Warehouse: Amazon S3, Azure Data Lake vs. Amazon Redshift, Google BigQuery.

Databases and Storage Solutions:

• SQL Databases: PostgreSQL, MySQL, Oracle Database.
• NoSQL Databases: MongoDB, Cassandra, HBase, Neo4j.
• NewSQL: Google Spanner, CockroachDB, VoltDB.
• Data Warehouses and Data Lakes: Amazon Redshift, Google BigQuery, Azure Synapse Analytics.

Distributed Computing and Data Processing:

• MapReduce: Basic concepts and operation of distributed data processing.
• Apache Spark: Spark RDDs, DataFrames, Spark SQL, GraphX, MLlib, Spark Streaming.
• Apache Kafka: Real-time data streaming and event-driven architectures.

Big Data Analysis and Machine Learning:

• Spark MLlib: Distributed machine learning library, ML pipelines, model tuning.
• H2O.ai: Distributed machine learning platform, AutoML, Spark integration.
• Dask: Flexible Python library for parallel processing and working with large datasets.

Libraries and Tools:

• PySpark: Big data processing and analysis with Apache Spark in Python.
  — RDD (Resilient Distributed Datasets): Core data structure of Spark, flexible and distributed data processing.
  — DataFrames: SQL-like data structure, performance optimization, and data analysis.
  — MLlib: Running machine learning algorithms on Spark.
• Dask: Parallel processing with large datasets in Python.
  — Dask Arrays and DataFrames: Similar to NumPy and Pandas, but distributed.
  — Dask Delayed: Usage of computation graphs and lazy evaluation.
  — Dask-ML: Running machine learning algorithms on large datasets.
• Apache Kafka: Real-time data streaming and messaging system.
  — Stream Processing: Kafka integration with Apache Flink, Apache Storm.
  — Kafka Streams API: API for developing Kafka-based streaming applications.
• Hadoop Ecosystem:
  — HDFS: Distributed file system for storing and managing large datasets.
  — MapReduce: Basic framework for large-scale data processing.
  — Hive and Pig: SQL-like query languages and data processing tools, data warehouse solutions.
  — HBase: NoSQL database for fast read/write operations on large datasets.
• H2O.ai: Distributed and parallel machine learning platform.
  — H2O Flow: Web-based interface for data analysis and modeling.
  — H2O AutoML: Automated model selection and hyperparameter optimization.
  — Sparkling Water: Integration of H2O with Apache Spark.

Resources:

• “Hadoop: The Definitive Guide” by Tom White (For Hadoop ecosystem and big data).
• “Learning Spark: Lightning-Fast Big Data Analysis” by Jules Damji, Brooke Wenig, Tathagata Das, and Denny Lee (Big data analysis with Apache Spark).
• “Kafka: The Definitive Guide” by Neha Narkhede, Gwen Shapira, and Todd Palino (Comprehensive information on Apache Kafka).
• “Designing Data-Intensive Applications” by Martin Kleppmann (For designing data-intensive systems).
• “Data Science on the Google Cloud Platform” by Valliappa Lakshmanan (Big data and machine learning applications on the cloud).

Courses:

• Coursera: “Big Data Specialization” by UC San Diego (Big data technologies and analysis).
• edX: “Introduction to Big Data with Apache Spark” by UC Berkeley (Big data analysis with Spark).
• Udacity: “Data Engineering Nanodegree” (Big data engineering and distributed systems).
• DataCamp: “Big Data with PySpark” (Big data analysis with PySpark).

6. Natural Language Processing (NLP)

NLP Fundamentals:

• Text Cleaning:
  — Tokenization: Breaking text into sentences or words, word tokenization, sentence tokenization.
  — Stemming and Lemmatization: Porter and Snowball stemmers, WordNet lemmatizer.
  — Stop Words: Filtering out frequently used but non-informative words, stop word lists in nltk and spacy.
  — Regular Expressions (RegEx): Searching for specific patterns in text, filtering text using Regex.
• N-gram Models:
  — N-gram Language Models: Modeling probability distributions of texts.
  — Skip-Gram and CBOW: Techniques used in Word2Vec model.
• Language Models:
  — Bag of Words (BoW): A simple and effective language model based on word frequencies.
  — TF-IDF: Term Frequency — Inverse Document Frequency, a statistical weighting for identifying important terms.
  — Word Embeddings: Numerical representations of words with Word2Vec, GloVe, FastText.

Advanced NLP Techniques:

• Transformer Models:
  — Self-Attention: Fundamental mechanism of Transformer architectures for modeling long-term dependencies.
  — BERT (Bidirectional Encoder Representations from Transformers): Masked Language Modeling, Next Sentence Prediction.
  — GPT (Generative Pre-trained Transformer): Language modeling and text generation, large language models like GPT-3.
  — RoBERTa, ALBERT, T5: Various Transformer architecture variants and their performance in NLP applications.
• Sequence Models:
  — Recurrent Neural Networks (RNNs): Modeling sequential data, time series analysis, text sequences.
  — Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU): Modeling long-term dependencies.
  — Seq2Seq Models: Encoder-Decoder structures for machine translation and text summarization.
  — Attention Mechanism: Contextual information usage with the attention mechanism, the foundation of Transformer models.
• Natural Language Understanding (NLU):
  — Sentiment Analysis: Classifying texts as positive, negative, or neutral through sentiment analysis.
  — Named Entity Recognition (NER): Identifying proper nouns like people, places, organizations in text.
  — Topic Modeling: Identifying hidden themes in texts with Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF).
  — Text Classification: Text classification models based on CNN and RNN.

Practical Applications:

• Chatbot Development: Developing chatbots with tools like Rasa, Dialogflow, Microsoft Bot Framework.
• Machine Translation: Translation systems based on Seq2Seq models, Transformer-based translation systems.
• Text Summarization: Extractive and abstractive summarization methods.
• Automatic Speech Recognition (ASR): Recognizing and converting speech to text, using models like DeepSpeech.

Libraries and Tools:

• nltk: A fundamental Python library for natural language processing.
• spacy: An efficient and modern NLP library.
• gensim: For topic modeling and similar text processing tasks.
• transformers (Hugging Face): Transformer-based NLP models.
• textblob: A simple and easy-to-use NLP library.
• Flair: An advanced NLP library for rich word embeddings and sequence tagging.

Resources:

• “Speech and Language Processing” by Daniel Jurafsky and James H. Martin (A foundational resource for NLP theory and applications).
• “Natural Language Processing with Python” by Steven Bird, Ewan Klein, and Edward Loper (For NLP applications with Python).
• “Deep Learning for NLP and Speech Recognition” by Uday Kamath, John Liu, and James Whitaker (Deep learning and NLP).
• “Transformers for Natural Language Processing” by Denis Rothman (For Transformer-based NLP).
• “Introduction to Information Retrieval” by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze (Information retrieval and NLP).

Courses:

• Coursera: “Natural Language Processing” by deeplearning.ai (Basics and advanced techniques of NLP).
• Udemy: “Natural Language Processing with Python and NLTK” by Jose Portilla (NLP with Python).
• Fast.ai: “Practical Deep Learning for Coders” (Includes an NLP module).
• edX: “Text Mining and Analytics” by University of Illinois (Text mining and analytics).
• DataCamp: “Natural Language Processing in Python (V2)” (NLP with Python).
• Stanford Online: “CS224n: Natural Language Processing with Deep Learning” (An advanced NLP course offered at Stanford University, focusing on deep learning and NLP).
• Udacity: “Artificial Intelligence for Trading” (NLP applications in financial data analysis).

7. Model Deployment and Production

Model Deployment:

• Web Services and APIs:
  — Flask and FastAPI: Developing lightweight web applications and APIs with Python.
  — Django: A full-featured web development framework, creating REST APIs.
  — GraphQL: Developing APIs for data querying and manipulation.
• Containerization:
  — Docker: Isolating and deploying applications in containers.
  — Kubernetes: A platform for deploying, managing, and scaling applications.
  — Docker Compose: Managing and deploying multiple services simultaneously.
• Continuous Integration/Continuous Deployment (CI/CD):
  — Jenkins: An automation platform for CI/CD processes.
  — GitLab CI/CD: CI/CD integrated with GitLab.
  — CircleCI: A fast and flexible CI/CD solution.
  — Travis CI: CI/CD automation for GitHub projects.

Model Monitoring and Management:

• MLOps: Model management, monitoring, and retraining processes.
  — Model Drifting: Monitoring the performance of the model over time and retraining when necessary.
  — Model Monitoring: Continuously monitoring model performance and metrics (Prometheus, Grafana).
• A/B Testing:
  — Split Testing: Comparing the performance of different model versions in a real-world environment.
  — Metric Analysis: Analyzing performance metrics to determine which model performs best.

Model Versioning and Reproduction:

• Model Versioning:
  — MLflow: Tracking machine learning experiments, model monitoring, and version control.
  — DVC (Data Version Control): Version control for datasets and models.
  — Git: Version control system for managing code and models.
• Reproducible Pipelines:
  — Prefect and Airflow: Managing data processing and model training processes and creating reproducible workflows.
  — Kubeflow Pipelines: Automating machine learning workflows to run on Kubernetes.
  — Docker: Managing environmental dependencies and portability of workflows.

Model Management Platforms:

• MLflow: Model management, monitoring, and experiment tracking.
• Kubeflow: Creating and deploying machine learning workflows on Kubernetes.
• Seldon: An open-source MLOps platform for model deployment and monitoring.
• Airflow: A powerful tool for scheduled workflows and data pipelines.
• Neptune.ai: A platform for model and experiment management.

Resources:

• “Building Machine Learning Powered Applications” by Emmanuel Ameisen (For MLOps and model deployment).
• “Designing Data-Intensive Applications” by Martin Kleppmann (For designing data-intensive applications).
• “Flask Web Development” by Miguel Grinberg (Developing web applications with Flask).
• “Effective DevOps” by Jennifer Davis and Katherine Daniels (DevOps principles and practices).
• “Continuous Delivery” by Jez Humble and David Farley (Principles and practices of CI/CD).

Courses:

• Coursera: “Deploying Machine Learning Models in Production” by deeplearning.ai (Model deployment and MLOps).
• Udemy: “Flask Framework: Build Python-based Web Applications” by Jose Salvatierra (Model deployment with Flask).
• Pluralsight: “Docker for Data Scientists” by Andrew Baker (Docker usage and deployment).
• edX: “MLOps with Python” by Microsoft (MLOps applications and model management).
• DataCamp: “Introduction to Docker for Data Science” (Using Docker and containerization for data science projects).

Best of luck.