1. Python(Numpy, Pandas, Matplotlib) for Data Science
2. Exploratory Data Analysis

3. Implementing Neural Network from Scratch
4. Clasification & Regression using Deep Learning with Tensorflow and Keras
5. Image Classification using Pre-Trained Deep Learning Models and Transfer Learning

6. Univariate Time Series Forecasting using Statistical Models (ARIMA, Exponential Smoothing, etc.)
7. Univariate Time Series Forecasting using Deep Learning and Hybrid Models
8. Fuzzy Time Series Forecasting using Deep Learning Models

9. Collaborative Filtering-based Recommender System
10. Sentiment Analysis using Deep Learning

Learn Python for data science and exploratory data analysis.

Gain proficiency in neural networks and deep learning for regression and classification problems.

Learn crisp and time series forecasting using statistical, deep learning and hybrid models.

Learn to develop recommendation systems and perform sentiment analysis.

Develop an in-depth understanding of the key technologies in data science and business analytics: data mining, deep learning, visualization techniques, predictive modeling, and statistics.

Practice problem analysis and decision-making.

Gain practical, hands-on experience with statistics, programming languages, and tools through applied research experiences.

Apply data science concepts and methods to solve problems in real-world contexts and communicate these solutions effectively.

Pang-Ning Tan, Michael Steinbach, Vipin Kumar,, Introduction to Data Mining, Springer

Laura Igual and Santi Seguí, Introduction to Data Science, Springer

Davy Cielin, Arno Meysman, Mohamed Ali, Introducing Data Science, Manning

Andreas, Practical Data Science, Apress

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