Foundation Course for Data Science

 

1. Introduction Data Science

• Overview, real-world applications, and scope of data science.

2. Python Programming and Libraries

• Expanded coverage of NumPy, Pandas, Matplotlib, and introduction to Seaborn.
• Advanced data manipulation and visualization techniques.

3. Math Foundation for Data Science

• Comprehensive coverage of matrices, vectors, and gradients.
• Introduction eigenvalues and eigenvectors for PCA.

4. Data isualization and Analysis

• Advanced visualizations using Plotly and Tableau.
• Real-world examples of interactive dashboards.

5. Basic and Advanced Statistics

• Descriptive statistics: Measures of central tendency and variability.
• Inferential statistics: Hypothesis testing, confidence intervals, ANOVA.
• Correlation and covariance.

6. Exploratory Data Analysis (EDA)

• Feature engineering, handling imbalanced datasets.
• Outlier detection using statistical methods.

7. Probability

• Discrete and continuous probability distributions.
• Bayesian probability basics.

 

Machine Learning

 

1. Linear Regression

• Deep dive into assumptions, diagnostics, and regularization techniques like Ridge and Lasso.

2. Cost Function and Gradient Descent

• Optimization algorithms: Stochastic Gradient Descent (SGD), mini-batch gradient descent.

3. Logistic Regression

• Multiclass classification and regularization techniques.

4. Support Vector Machines (SVM)

• Advanced kernels (polynomial, RBF) and hyperparameter tuning.

5. Tree Models, Clustering, and Classification

• Ensemble methods: Random Forest, Gradient Boosting, and XGBoost.
• Unsupervised learning: k-means, hierarchical clustering, and DBSCAN.

6. Principal Component Analysis (PCA)

• Dimensionality reduction for high-dimensional datasets.

7. Model Optimization

• Cross-validation, grid search, and randomized search for hyperparameter Tuning.

 

Deep Learning

 

The advanced course significantly expands deep learning coverage:

 

1. Introduction to Neural Networks

• Architecture, activation functions, and feedforward/backpropagation.
• Lab: Building a simple neural network using TensorFlow or PyTorch.

2. Advanced Neural Networks

• Convolutional Neural Networks (CNNs): Basics and applications in image data.
• Recurrent Neural Networks (RNNs): Basics and applications in sequential Data.

3. Deployment of Deep Learning Models

• Using Flask, FastAPI, or Docker for model deployment.
• Introduction tAWS SageMaker and cloud deployment.

 

Hands-On Labs

 

1. Data Manipulation and EDA

• Cleaning, preprocessing, feature engineering, and visualizations.

2. Model Implementation

• Advanced machine learning models using Scikit-Learn and TensorFlow.

3. Neural Networks

• Building and evaluating CNN and RNN models.

4. Model Deployment

• Hands-on deployment using Flask/Docker.

 

Capstone Project

 

The project duration is extended t2 months and includes:

•  Dataset collection and preparation.
•  Comprehensive EDA, feature engineering, and model selection.
•  Implementation of supervised or unsupervised models.
•  Deployment of the final model and preparation of a project report.
•  Tools: Jupyter Notebooks, TensorFlow, Flask, and cloud platforms like AWS.

Placement Assistance Package Cost 35,000 INR and Inauguration Discount 5,000 INR actual

cost 30,000 INR

 

(Optional Add-On for Extra Cost)

 

1. Interview Training

• Soft skills and technical interview preparation.
• Common interview questions for data science and ML roles.

2. CV Preparation

• Professional CV building and customization.

3. Mock Tests

• Simulated tests for technical and aptitude skills.

4. CV Forwarding

• Connecting students with relevant recruiters and job portals.

5. Interview Conducting

• Scheduling interviews with hiring partners.