Big Data Science
- Acquired core statistical concepts and techniques essential for practical data analysis.
- Developed skills in modeling data sources and analyzing their statistical properties.
- Proficient in visualizing data in various formats and fitting samples to known probabilistic models.
- Proficient in utilizing data mining techniques to extract meaningful insights from large and complex datasets.
- Skilled in exploring and implementing algorithms, considering their strengths and limitations.
- Experienced in working with diverse data sources and applying data mining principles to assist in decision-making.
- Proficient in fundamental concepts, methodologies, and practical tools of machine learning.
- Skilled in building and evaluating data-driven models to describe real-world systems and predict their behavior.
- Experienced in applying supervised and unsupervised learning techniques to solve a wide range of problems.
- Knowledgeable about identifying and avoiding common pitfalls in machine learning projects.
- Stay updated with state-of-the-art models and advancements in model deployment.
- Proficient in both the theoretical foundations and practical applications of Neural Networks and Deep Learning.
- Skilled in utilizing automatic differentiation for modern AI and implementing Neural Networks using Deep -Learning Pytorch frameworks.
- Experienced in solving real-world Machine Learning problems using Neural Networks and staying updated with industry trends.
- Knowledgeable in key topics such as Stochastic Gradient Descent, Regression, Softmax Regression, Multi-Layer -Perceptrons, Convolutional Neural Networks, and Recurrent Neural Networks.
- Familiar with applying Neural Networks to diverse domains, including computer vision and natural language processing.
- Proficient in understanding and addressing challenges related to risk assessment, prediction, and decision-making in various domains.
- Skilled in applying methods and tools for improved risk assessment in personal, group, and strategic decision-making processes.
- Experienced in critically analyzing and navigating through the complexities of risk discourse in public discussions.
- Knowledgeable about the limitations of big data and machine learning in solving decision and risk problems.
- Familiar with applying risk and decision-making principles in fields such as public health, medicine, law, government strategy, transport safety, and consumer protection.
- Proficient in the field of Natural Language Processing (NLP) or Computational Linguistics.
- Skilled in applying core techniques for language processing to various applications, such as machine translation, question answering, text mining, and spoken dialogue systems.
- Experienced in utilizing both statistical and rule-based approaches in NLP tasks.
- Knowledgeable about the advancements and trends in NLP and its importance in modern computing.
- Familiar with the practical application of NLP techniques to enhance language understanding and processing in real-world scenarios.
- Proficient in large-scale programming models and algorithms for processing massive amounts of data.
- Skilled in utilizing MapReduce programming model and platforms like Apache Hadoop and Apache Spark for big data processing.
- Experienced in handling complex parallel computations and coordinating them across a cluster of computer nodes.
- Knowledgeable about big stream and big graph processing solutions.
- Familiar with related topics such as NoSQL data stores and cloud computing execution infrastructure.