• What is Data Science? Career Pathways
• Data Science Workflow and Lifecycle
• Python Basics: Syntax, Variables, Data Types
• Operators, Control Flow (If-Else, Loops)
• Functions and Modules
• File Handling (Text, CSV, Excel)
• Python Data Structures: Lists, Tuples, Dictionaries, Sets
• Introduction to Jupyter Notebooks
• Version Control with Git & GitHub (Basics)

• NumPy: Arrays, Indexing, Broadcasting, Operations
• Pandas: Series, DataFrames, Data Selection/Filtering
• Importing/Exporting Data
• Handling Missing Data
• Data Aggregation and GroupBy
• Merging, Joining, Concatenation
• Type Conversion & Parsing Dates
• String Operations & Regex
• Data Cleaning Pipelines

• Exploratory Data Analysis (EDA)
• Matplotlib & Seaborn: Histograms, Boxplots, Heatmaps
• Plotly Basics (Optional)
• Storytelling with Data
• Tableau: Connecting Data, Filters, Calculated Fields
• Tableau Charts: Bar, Line, Pie, Maps, Scatter
• Dashboard Design Principles
• SQL: SELECT, WHERE, ORDER BY, LIMIT
• Aggregations: COUNT, AVG, SUM, GROUP BY & HAVING
• JOINs (INNER, LEFT, RIGHT, FULL)
• Subqueries, CTEs, Window Functions

• What is Machine Learning?
• ML Pipeline & Model Evaluation
• Supervised Learning Overview
• Linear, Multiple, Polynomial Regression
• Evaluation Metrics: MAE, MSE, RMSE, R²
• Classification: Logistic Regression, KNN, Decision Trees, SVM
• Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1
• Train/Test Split, Cross-Validation
• Feature Scaling, Encoding
• Hyperparameter Tuning (GridSearch, RandomizedSearch)

• Ensemble Models: Random Forests, XGBoost, LightGBM
• Clustering: K-Means, Hierarchical, DBSCAN (Optional)
• Dimensionality Reduction: PCA, t-SNE (Optional)
• Intro to Time Series: Trend, Seasonality, ARIMA Concepts
• Introduction to NLP (Optional): Tokenization, Stopwords, Vectorization, Text Classification

• Neural Networks Basics: Perceptron, Activation Functions
• Forward & Backward Propagation
• TensorFlow/Keras Introduction
• Building ANN, CNN (Images), RNN (Optional)
• Dropout, Batch Normalization
• Model Evaluation in Deep Learning
• Introduction to Model Deployment (Flask Basics, Docker Optional)
• Capstone Project: Problem to Deployment
• Career Guidance & Portfolio Development

This bootcamp is designed for anyone looking to start or transition into a data science career. It's suitable for fresh graduates, working professionals from analytical or non-analytical backgrounds, and career changers who want to gain expertise in this high-demand field.

Basic understanding of mathematics, especially algebra and statistics basics, is helpful but not strictly required as we cover foundational concepts. No prior programming experience is necessary, but comfort with computers is essential.

You'll receive support from expert instructors and teaching assistants. We provide live doubt-clearing sessions, dedicated online forums, and access to mentors for project guidance and career advice.

Yes, upon successful completion of the course, including projects and assignments, you will receive a certificate of completion from CodeParallels.

We keep our class sizes optimized to ensure personalized attention and interaction. The exact size may vary by batch but is typically kept under 4-5 students.