Data Scientist

Topics Covered:

• Python programming fundamentals
• R basics (optional but recommended)
• Jupyter Notebooks & Google Colab
• Git & version control basics
• Working with files, error handling

Topics Covered:

• Statistics (mean, median, mode, std dev, distributions)
• Probability theory basics
• Linear algebra essentials (vectors, matrices)
• Calculus (derivatives for optimization in ML)
• Descriptive vs inferential statistics

Topics Covered:

• Using Pandas for data manipulation
• NumPy for numerical operations
• Handling missing data and outliers
• Data preprocessing & feature engineering
• Working with large datasets (memory efficiency)

Topics Covered:

• Matplotlib and Seaborn for static plots
• Plotly for interactive visualization
• Dashboards with Tableau or Power BI
• Storytelling with data

Topics Covered:

• SQL basics (SELECT, WHERE, GROUP BY)
• Joins, subqueries, and window functions
• Working with PostgreSQL / MySQL
• Connecting Python with databases (sqlite3, SQLAlchemy)

Topics Covered:

• Supervised vs unsupervised learning
• Scikit-learn API and pipelines
• Classification (logistic regression, decision trees, SVM)
• Regression (linear, ridge, lasso)
• Clustering (KMeans, DBSCAN)
• Model evaluation metrics

Topics Covered:

• Neural networks with TensorFlow/Keras
• Image classification with CNNs
• Text classification with RNNs / LSTMs
• Transfer learning
• PyTorch basics

Topics Covered:

• Big data processing with Hadoop and Spark
• Data lakes & warehouses overview
• ETL pipelines overview
• Working with AWS/GCP/Azure (S3, BigQuery, etc.)

Topics Covered:

• NLP: Tokenization, TF-IDF, Word2Vec, Transformers
• Text classification and sentiment analysis
• Computer Vision: OpenCV basics, CNN applications
• Image augmentation and preprocessing

Topics Covered: