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They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesistesting and deep learning to the team. The most common data science languages are Python and R — SQL is also a must have skill for acquiring and manipulating data.
Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.
Familiarity with cloudcomputing tools supports scalable model deployment. Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. It’s often used in customer segmentation and anomaly detection.
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. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.
In Inferential Statistics, you can learn P-Value , T-Value , HypothesisTesting , and A/B Testing , which will help you to understand your data in the form of mathematics. Three of the most popular cloud platforms are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
Hypothesistesting and regression analysis are crucial for making predictions and understanding data relationships. Unsupervised Learning techniques such as clustering and dimensionality reduction to discover patterns in data. Industry-Relevant Topics: Covers advanced subjects like AI ethics, blockchain, and cloudcomputing.
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