To prepare for a beginner-level data scientist role, you’ll need a strong foundation in Machine Learning (ML). Here’s a structured list of key topics to cover:
1. Foundations of Machine Learning
- Supervised Learning:
- Regression (Linear Regression, Logistic Regression)
- Classification (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees)
- Unsupervised Learning:
- Clustering (K-Means, Hierarchical Clustering, DBSCAN)
- Dimensionality Reduction (Principal Component Analysis (PCA), t-SNE)
- Semi-supervised Learning (Introduction)
2. Feature Engineering
- Data Preprocessing: Handling missing data, encoding categorical data.
- Feature Scaling: Normalization and standardization.
- Feature Selection: Techniques like Recursive Feature Elimination (RFE), SelectKBest.
- Feature Extraction: Creating new features from raw data.
3. Model Evaluation & Validation
- Train-Test Split: Understanding overfitting and underfitting.
- Cross-Validation: K-Fold Cross-Validation, Leave-One-Out Cross-Validation (LOO).
- Metrics for Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared.
- Metrics for Classification: Accuracy, Precision, Recall, F1-score, ROC, AUC.
4. Ensemble Methods
- Bagging: Random Forest.
- Boosting: Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost.
- Stacking: Combining models for improved accuracy.
5. Hyperparameter Tuning
- Grid Search.
- Random Search.
- Bayesian Optimization.
6. Advanced Algorithms (Must-Know for Beginners)
- Naive Bayes.
- Support Vector Machines (SVM).
- Neural Networks (Introductory level).
- Time Series Forecasting (ARIMA, Exponential Smoothing).
7. Natural Language Processing (NLP)
- Text Preprocessing: Tokenization, Lemmatization, Stopword Removal.
- Bag of Words, TF-IDF.
- Word Embeddings: Word2Vec, GloVe.
- Intro to Transformers (optional, depending on job requirements).
8. Deep Learning (Basics)
- Artificial Neural Networks (ANNs): Introduction to deep learning.
- Convolutional Neural Networks (CNNs): For image data.
- Recurrent Neural Networks (RNNs): For sequence data.
9. Deployment of Models
- Flask / FastAPI for model deployment.
- Model Serving: Using tools like Docker and Heroku for deploying ML models.
10. Python Libraries & Tools
- NumPy, Pandas: For data manipulation.
- Matplotlib, Seaborn: For data visualization.
- Scikit-Learn: For implementing ML algorithms.
- TensorFlow / PyTorch: Basics of deep learning frameworks.
- Jupyter Notebooks: For experimentation.
11. Real-world Applications & Projects
- Work on projects like:
- Predictive modeling (e.g., predicting house prices).
- Classification tasks (e.g., spam detection, customer churn).
- Clustering (e.g., customer segmentation).
- NLP projects (e.g., sentiment analysis, text classification).
- Image classification (if deep learning interests you).
12. Tools for Data Scientists
- SQL: For data extraction and manipulation.
- Excel: Advanced features like pivot tables, VLOOKUP.
- Tableau / Power BI: For data visualization and dashboards.
- Git/GitHub: Version control.
- Cloud Platforms (optional but helpful): AWS, Google Cloud, Azure.
Focus Areas for Job Readiness:
- Practical Experience: Build projects, participate in Kaggle competitions.
- Soft Skills: Ability to explain ML concepts and communicate findings clearly.
- Portfolio: Showcase your projects on GitHub or a portfolio website.
- Interview Prep: Prepare for coding interviews and ML theory questions.
By mastering these areas, you’ll be well-prepared for a beginner-level data scientist role.