Computer Science
- Explored various data mining techniques, including clustering, classification, and association rule mining.
- Applied data preprocessing methods to clean and transform raw data for analysis.
- Utilized popular data mining tools and algorithms to extract meaningful insights from large datasets.
- Learned advanced techniques for analyzing and interpreting big data sets.
- Developed skills in predictive modeling and machine learning algorithms.
- Employed tools and technologies for processing and analyzing big data, such as Hadoop and Apache Spark.
- Acquired a strong understanding of the fundamental concepts and principles of database management systems.
- Designed and implemented efficient relational database schemas.
- Gained proficiency in SQL for querying and manipulating data within a database.
- Studied techniques for processing and analyzing human language data.
- Explored methods for sentiment analysis, text classification, and named entity recognition.
- Developed practical applications using NLP tools and libraries, such as NLTK.
- Explored the foundations of artificial intelligence, including search algorithms and knowledge representation.
- Studied machine learning techniques, such as decision trees, neural networks, and reinforcement learning.
- Developed AI applications and algorithms to solve complex problems.
- Gained proficiency in the Python programming language.
- Learned core concepts, data structures, and control flow in Python.
- Applied Python for data manipulation, analysis, and scripting tasks.