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SQL, Python, and essential libraries (numpy, pandas, and more).
Focuses on data collection, cleansing, preprocessing, and storage, including data acquisition and working with SQL and NoSQL databases.
This topic covers probability, distributions, hypothesis testing, regression analysis, and time series analysis.
Includes data extraction and visualization, featuring Exploratory Data Analysis (EDA) techniques, data visualization using tools/libraries such as Tableau, Power BI, matplotlib, and use of descriptive statistics.
Encompasses supervised learning (linear regression, logistic regression, decision trees, etc.), unsupervised learning, model evaluation and validation, and feature engineering.
Deep understanding of neural networks, deep learning frameworks, convolutional neural networks (CNNS), recurrent neural networks (RNN), and natural language processing (NLP).
This topic covers tools and techniques to handle and process vast volumes of data. It also includes in-depth learning of distributed storage, parallel processing, and scalable computing frameworks.
It involves ethical considerations and privacy regulations that guide responsible and secure data handling and analysis.
Focuses on integrating machine learning models into practical, real-world applications for decision-making and automation.
This project within the data science syllabus culminates in applying acquired skills to solve real-world data challenges.
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