TSSFL TECHNOLOGY STACK - Data Science

Data Science

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Data Science Fundamentals Concepts and Principles: Definitions of Data Science, the Data Science lifecycle, different roles in Data Science, ethical considerations, data governance, introduction to various tools and technologies used in Data Science.

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Statistics and Mathematics for Data Science Probability theory, statistical inference, hypothesis testing, linear algebra, calculus, optimization techniques relevant to Data Science.

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Machine Learning Algorithms and Techniques: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation, feature engineering, algorithm-specific discussions (e.g., decision trees, support vector machines, Bayesian methods).

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Deep Learning Neural Networks and Architectures: Fundamentals of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, autoencoders, generative adversarial networks (GANs), model training and optimization in deep learning.

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Mathematical Modeling in Data Science Frameworks and Applications: Building mathematical models for various data science problems, simulation techniques, model validation, using mathematical tools to understand data patterns.

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Data Analysis and Visualization Exploration and Communication: Data cleaning and preprocessing, exploratory data analysis (EDA), different visualization techniques (charts, graphs, dashboards), storytelling with data, communicating insights effectively.

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Applied Data Science Real-World Problems and Solutions: Demonstrations of applying Data Science techniques to solve real-world problems using actual datasets, case studies from various industries, discussions on deployment and scaling of Data Science solutions.

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