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Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Statistics : Fundamental statistical concepts and methods, including hypothesistesting, probability, and descriptive statistics.
With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc.
Here are some of the most common backgrounds that prepare you well: Mathematics and Statistics These disciplines provide a rock-solid understanding of data analysis, probability theory, statistical modelling, and hypothesistesting – all essential tools for extracting meaning from data.
By the end of this blog, you will feel empowered to explore the exciting world of Data Science and achieve your career goals. This knowledge allows the design of experiments, hypothesistesting, and the derivation of conclusions from data. It forms the basis of predictive modelling and risk assessment.
Eligibility Criteria To qualify for a Master’s in Data Science, candidates typically need a bachelor’s degree in a related field, such as computerscience, statistics, mathematics, or engineering. Frequently Asked Questions What are the Eligibility Criteria for a Master’s in Data Science in India?
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