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This discipline takes raw data, deciphers it, and turns it into a digestible format using various tools and algorithms. Tools such as Python, R, and SQL help to manipulate and analyze data. Statistics helps data scientists to estimate, predict and test hypotheses.
Machine learning Machine learning is a key part of data science. It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decisiontrees, clustering, neural networks, and other data-driven problem-solving methods is vital.
Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and dataengineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.
Scala is worth knowing if youre looking to branch into dataengineering and working with big data more as its helpful for scaling applications. Knowing all three frameworks covers the most ground for aspiring data science professionals, so you cover plenty of ground knowing thisgroup.
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