4 Checklists

Your Tech Stack Checklist §

(use the github)

Languages:

• R
• Python
  • Numpy
  • Pandas
  • PyTorch
  • TensorFlow
• Java
• C++

Technologies:

• AWS
• Hadoop
• Spark

Your Checklist §

1. 📚 Foundational Knowledge: §

• 🧮 Mathematics:
  • 🔢 Linear Algebra
  • ∫ Calculus
  • 🎲 Probability and Statistics
• 💻 Programming:
  • 🐍 Python:
    • 📜 Syntax and Basic Concepts
    • 📚 Data Structures
    • ↪️ Control Structures
    • 🔄 Functions
    • 🏭 Object-Oriented Programming
  • 🅡 R (optional, based on preference)
  • 🔍 SQL

2. 💾 Data Manipulation and Visualisation: §

• 🔄 Data Manipulation:
  • 🧮 Numpy (Python)
  • 🐼 Pandas (Python)
  • 🔄 Dplyr (R)
• 📊 Data Visualisation:
  • 📉 Matplotlib (Python)
  • 📈 Seaborn (Python)
  • 📊 ggplot2 (R)
  • 🔮 Interactive Visualisation Tools

3. 🔍 Exploratory Data Analysis (EDA) and Preprocessing: §

• 🕵️‍♀️ Exploratory Data Analysis Techniques
• ⚙️ Feature Engineering
• 🧼 Data Cleaning
• 🚫 Handling Missing Data
• ⚖️ Data Scaling and Normalisation
• 🕳️ Outlier Detection and Treatment

4. 🤖 Machine Learning: §

• 👨‍🏫 Supervised Learning:
  • 📈 Regression:
    • 📊 Linear Regression
    • 📈 Polynomial Regression
    • 🔒 Regularisation Techniques
  • 📊 Classification:
    • ⚖️ Logistic Regression
    • 📍 k-Nearest Neighbours (k-NN)
    • 🛡️ Support Vector Machines (SVM)
    • 🌳 Decision Trees
    • 🌲 Random Forest
    • ⛰️ Gradient Boosting
• 🧠 Unsupervised Learning:
  • 📍 Clustering:
    • 🎯 K-means
    • 🎈 DBSCAN
    • 🌳 Hierarchical Clustering
  • 📉 Dimensionality Reduction
    • 🔍 Principal Component Analysis (PCA)
    • 🔭 t-Distributed Stochastic Neighbour Embedding (t-SNE)
    • 📊 Linear Discriminant Analysis (LDA)
    • 🔗 Association Rule Learning
• 🏆 Reinforcement Learning
• ✅ Model Evaluation and Validation:
  • 🔁 Cross-validation
  • 🎛️ Hyperparameter Tuning
  • 🏆 Model Selection Techniques
  • 🎯 Evaluation Metrics
• 🚀 Advanced Machine Learning
  • 📚 Ensemble Methods (Bagging, Boosting)
  • 📈 Learning Curves and Bias-Variance Tradeoff
  • 💡 Model Interpretability and Explainability (SHAP, LIME)

5. 🧠 Deep Learning: §

• 🧠 Neural Networks:
  • 💡 Perceptron
  • 📚 Multi-Layer Perceptron (MLP)
• 🖼️ Convolutional Neural Networks (CNNs):
  • 🏷️ Image Classification
  • 🕵️ Object Detection
  • 🎨 Image Segmentation
• 🔄 Recurrent Neural Networks (RNNs):
  • 🔄 Sequence-to-Sequence Models
  • 🏷️ Text Classification
  • 🎭 Sentiment Analysis
• 🕰️ Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU):
  • 📅 Time Series Forecasting
  • 📚 Language Modelling
• 🎨 Generative Adversarial Networks (GANs):
  • 🖼️ Image Synthesis
  • 🎨 Style Transfer
  • 🔄 Data Augmentation
• **🚀 Advanced

Deep Learning:** - [ ] 🔍 Attention Mechanisms - [ ] 🔄 Transfer Learning - [ ] 🎓 Self-Supervised Learning

6. 🔬 Advanced Topics: §

• 💬 Natural Language Processing (NLP):
  • 📚 Text Preprocessing
  • 📚 Word Embeddings (e.g., Word2Vec, GloVe)
  • 🔄 Recurrent Neural Networks for NLP
  • 📚 Transformer Models (e.g., BERT, GPT)
• 🕰️ Time Series Analysis:
  • 📈 Time Series Decomposition
  • 🔄 Autoregressive Integrated Moving Average (ARIMA)
  • 🕰️ Seasonal ARIMA (SARIMA)
  • 📈 Exponential Smoothing Methods
  • 📚 Prophet
• 🎯 Recommender Systems:
  • 🔄 Collaborative Filtering
  • 🎯 Content-Based Filtering
  • 🧮 Matrix Factorization
  • 🔄 Hybrid Methods
• 📚 Causal Inference:
  • 📈 Experimental Design
  • 📚 Observational Studies
  • 📊 Propensity Score Matching
  • 📚 Instrumental Variable Analysis
• 🚀 Advanced Deep Learning:
  • 🏗️ Advanced Architectures (e.g., Transformers, GPT models)
  • 🎨 Generative Models (e.g., VAEs, flow-based models)
  • 🚀 Advanced Techniques for NLP and Computer Vision
• 📊 Bayesian Statistics and Probabilistic Programming:
  • 📚 Bayesian Inference
  • 🔄 Markov Chain Monte Carlo (MCMC)
  • 📊 Probabilistic Graphical Models
  • 📚 Stan, PyMC3, or Edward for Probabilistic Programming
• 🤖 Automated Machine Learning (AutoML)
• 🏭 Data Engineering:
  • 🔄 ETL (Extract, Transform, Load) processes
  • 🏭 Data Warehousing
• 🚀 Advanced Deep Learning (continued from Deep Learning section):
  • 🔍 Attention Mechanisms
    • 🔄 Transfer Learning
  • 🎓 Self-Supervised Learning

7. 📊 Big Data Technologies: §

• 🐘 Hadoop
• 📂 HDFS
• 🔄 MapReduce
• 💥 Spark:
  • 📚 RDDs
  • 📊 DataFrames
  • 📚 MLlib
• 📂 NoSQL Databases:
  • 🐵 MongoDB
  • 🚀 Cassandra
  • 🐘 HBase
  • 🛋️ Couchbase
• 📡 Stream Processing Frameworks
  • 📡 Apache Kafka
  • 📡 Apache Flink
  • 🌪️ Apache Storm

8. 💠 Algorithms: §

• Hill Climb
• Genetic Algorithm; Beam Search

9. 📈 Data Visualisation and Reporting: §

• 🎛️ Dashboarding Tools:
  • 📊 Tableau
  • 💥 PowerBI
  • 🐍 Dash (Python)
  • 🅡 Shiny (R)
• 📖 Storytelling with Data
• 🗣️ Effective Communication

10. 🎯 Domain Knowledge and Soft Skills: §

• 🏭 Industry-specific Knowledge
• 💡 Problem-solving
• 🗣️ Communication Skills
• ⏱️ Time Management
• 👥 Teamwork
• 💼 Business Acumen:
  • 📈 Understanding of business metrics and Key Performance Indicators (KPIs)
  • 🔀 Ability to translate business problems into data problems and vice versa

11. ⚖️ Ethical Considerations and Bias in Data Science §

• ⚖️ Fairness in Machine Learning
• ⚖️ Bias Detection and Mitigation
• 🔐 Privacy and Data Security
• 🧲 Zook’s 5
• 🔏 Data Privacy and Governance:
  • 📚 Understanding regulations like GDPR, CCPA
  • 🔏 Data anonymisation and pseudonymisation techniques

12. 🚀 Deployment and Productionisation §

• 🏭 Model Deployment Techniques
• 📦 Containerisation (e.g., Docker)
• 🌐 Model Serving and APIs
• 📈 Scalability and Performance Optimisation
• 🧑‍💻 Project Management and Collaboration Tools:
  • 📚 Knowledge of version control systems like Git
  • 🗂️ Familiarity with project management tools (JIRA, Asana, etc.)
  • 👥 Experience with collaborative coding platforms (GitHub, GitLab, etc.)

13. 🎓 Continuous Learning and Staying Updated §

• 🌐 Online Courses and Tutorials
• 📖 Books and Research Papers
• 🎙️ Great Podcasts
• 📚 Conferences and Workshops
• 👥 Networking and Community Engagement

14. 👨‍💻 Software Engineering Best Practices §

• ✏️ Writing clean, efficient, and reusable code
• 🐜 Code testing and debugging
• 🏗️ Understanding of design principles and architectural patterns
• 💻 Familiarity with Integrated Development Environments (IDEs)