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Data Science Course Eligibility

This course made for anyone who wants to learn it, whether they are new or professional. One can go for a bacheloru2019s degree in Data Science after class 12. They should have a background in science, and it can be an additional benefit to having computer programmes in high school.

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Data Science Course Eligibility

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  1. Data Science Course Eligibility

  2. Introduction • This course made for anyone who wants to learn it, whether they are new or professional. One can go for a bachelor’s degree in Data Science after class 12. They should have a background in science, and it can be an additional benefit to having computer programmes in high school. The percentage needed to accept admission, however, depends on the institution. A new graduate from a recognised university may also opt for either a master’s degree in Data Science in the relevant discipline. A PG Diploma in Data Science may be carried out by working professionals with a similar background. Data Science also has several online certification programmes.

  3. Data Science Course Eligibility • Data scientists are highly educational. 88% have at least a Master’s degree and 46% have PhDs. And although there are notable exceptions, it requires a very strong educational background to develop the level of knowledge necessary to be a data scientist. You could obtain a Bachelor’s degree in Computer Science, Social Sciences, Physical Sciences, and Statistics to become a data scientist. Mathematics and Statistics (32 per cent), followed by computer science (19 per cent) and engineering (16 per cent), are the most common fields of study. A degree in each of these courses will provide you with the skills to process and analyse big data that you need. The fact is that most data scientists have a Master’s or Ph. D degree and often undergo online training to learn a special ability such as how to use Hadoop or Big Data querying. You may then apply for a master’s degree in Data Science, Mathematics, Astrophysics or any other related area. During your degree programme, the skills you have gained will help you to easily transition to data science. You can practise what you learned in the classroom, apart from classroom learning, by creating an app, starting a blog or exploring data analysis to enable you to learn more.

  4. Course Pre-requisites • Data Analytics stakeholders should have some previous experience in (or be prepared to work with): Mathematics Basic • Statistics (working with statistical methods and numbers) • For anyone with a degree in social and natural sciences, engineering, mathematics, art and others, this course is well adapted.

  5. Skills required • For data analytics, as a programming language, as an environment for statistical analysis, data visualisation, in-depth information in R: R is used. Other skills that are required are: • Python coding  mathematical models and concepts are primarily preferred to Python since Python has rich libraries/packages to construct and deploy models. • MS Excel For all data entry work, Microsoft Excel is considered a basic requirement. In data processing, applying formulae, equations, diagrams from a messy tonne of data is of great benefit.

  6. Hadoop Platform • It is a distributed computing system that is open source. It is used for the management of big data applications for processing and storage. • SQL database/coding It is primarily used for dataset preparation and extraction. It can also be used for issues such as graphics and network analysis, search activity, detection of fraud, etc. • Technology Because there is so much unstructured knowledge out there, one should know how to access the information as well. This can be done through AP in a variety of ways

  7. Techniques required • Along with the skills, students require some techniques as well. Mathematical Expertise Data scientists often work on machine learning algorithms that require a very large amount of mathematical knowledge, such as regression, clustering, time series, etc., since they themselves are based on mathematical algorithms. • Working with unstructured data Since most of the data created each day is unstructured in the form of photos, comments, tweets, search history, etc., knowing how to turn this unstructured into a structured form and then work with them is a very useful ability in today’s market.

  8. About Data Science Course • This course intends for learners who do not have prior data analytics experience. It targets to gain these skills in a short period of time. These students will learn how to evaluate broad data sets and find trends that will enhance the decision-making process of any business or organisation. After completing the course, they will be able to: Capturing, classifying, simplifying, normalising and preparing data for processing. • Working with and evaluating huge sets of data. • Reflect the findings of the study visually to professional and non-technical audiences. • Use the most common algorithms to make sense of vast quantities of information. They are important to most business and management issues. • You will have a professional portfolio of projects at the end of the programme. Alongside you will have real experience with data analytics. This will give you the confidence required to be successful as a data analyst.

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