What I will learn?

• Foundational Knowledge: The program starts with foundational courses in mathematics, statistics, and programming to ensure that students have a solid understanding of the fundamental concepts and tools used in data science. Topics may include linear algebra, calculus, probability theory, statistical inference, and programming languages such as Python and R.
• Data Wrangling and Preprocessing: Students will learn how to acquire, clean, and preprocess data from various sources, including databases, APIs, web scraping, and sensor networks. They will gain hands-on experience with data manipulation techniques, data cleaning, missing data imputation, and data transformation.
• Exploratory Data Analysis: Students will learn how to explore and visualize data to gain insights and identify patterns, trends, and outliers. They will use descriptive statistics, data visualization libraries, and exploratory data analysis techniques to uncover hidden patterns and relationships in the data.
• Machine Learning Algorithms: The program covers a wide range of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and reinforcement learning. Students will learn how to apply machine learning algorithms to solve classification, regression, clustering, and reinforcement learning problems.
• Deep Learning and Neural Networks: Students will explore deep learning techniques and neural network architectures for tasks such as image recognition, natural language processing, and time series analysis. They will learn how to design, train, and evaluate deep learning models using frameworks such as TensorFlow and PyTorch.
• Big Data Technologies: Students will learn about big data technologies and tools for processing and analyzing large volumes of data efficiently. Topics may include distributed computing, Hadoop, Spark, MapReduce, and cloud computing platforms such as Amazon Web Services (AWS) and Microsoft Azure.
• Data Mining and Knowledge Discovery: Students will learn data mining techniques for discovering patterns, associations, and trends in large datasets. They will explore data mining algorithms such as association rule mining, cluster analysis, and anomaly detection, and learn how to apply these techniques to real-world problems.
• Predictive Modeling and Forecasting: Students will learn how to build predictive models and perform time series forecasting using statistical and machine learning techniques. They will learn how to evaluate model performance, tune model parameters, and interpret model results.