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Summary: A comprehensive BigData syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of BigData Understanding the fundamentals of BigData is crucial for anyone entering this field.
Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesistesting, confidence intervals). These concepts help you analyse and interpret data effectively. Here are three critical areas worth exploring: Machine Learning, Data Visualisation, and BigData.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. These techniques span different types of learning and provide powerful tools to solve complex real-world problems.
Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Differentiate between supervised and unsupervised learning algorithms.
MicroMasters Program in Statistics and Data Science MIT – edX 1 year 2 months (INR 1,11,739) This program integrates Data Science, Statistics, and Machine Learning basics. It emphasises probabilistic modeling and Statistical inference for analysing bigdata and extracting information.
Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervisedlearning such as linear regression , logistic regression, decision trees, and support vector machines.
Association Rule Learning: A rule-based Machine Learning method to discover interesting relationships between variables in large databases. B BigData : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis.
Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Explain the difference between supervised and unsupervised learning. In traditional programming, the programmer explicitly defines the rules and logic.
Several technologies bridge the gap between AI and Data Science: Machine Learning (ML): ML algorithms, like regression and classification, enable machines to learn from data, enhancing predictive accuracy. BigData: Large datasets fuel AI and Data Science, providing the raw material for analysis and model training.
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