Data Science & Business Intelligence (Bi) Weiterbildung

Published Dec 20, 20
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It is designed for managing data in relational database management systems. - Hadoop is a suite of technologies for managing data and executing programs in a cluster (a collection of networked computers running in a data center). This includes a file system designed for the needs of large data, the MapReduce system for running programs in parallel, the SQL-like Hive database for querying data in a cluster, and many other components.

As a competitor to MapReduce, it has gained popularity for its higher efficiency on many problems. It also has a powerful machine learning library, mllib, and can be used with R, which makes it especially popular among data scientists. - Python and R are both standard languages that are used by data scientists.

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A complete data scientist will know both languages and leverage their different strengths. - Machine learning refers to a growing set of algorithms that are able to analyze large sets of data. Its popularity is due to the fact that these algorithms are able to make predictions about future events that exceed what traditional statistics is designed to do.

Thus the machine “learns” how to improve its predictive powers. While having math aptitude is important, Data Scientists come from a variety of educational and professional backgrounds. Common background skills include: problem-solving, logical reasoning, communication, and being detail-oriented. For a better idea of the types of students who are successful, check out these Q&As: As with most fields, Data Science job titles don’t always give you the nuts and bolts of what the job entails.

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Data Analysts are responsible for analyzing large datasets whether for customer research, business intelligence or internal studies. Data Analysts start with a large data set and are tasked with drawing actionable conclusions from this data. Data Analysts may work with engineers, UX Researchers and Sales staff to develop growth solutions.

Data Scientists are responsible for determining the data necessary to answer a question, from designing a method for capturing data to gathering data, analyzing data and finally presenting the solution. Similar to the Data Analyst, the Data Scientist’s role is much larger in scope and requires careful planning and design of research from beginning to end.

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Database Administrators work with technologies such as MySQL, MongoDB, and Postgres to manage large datasets. Depending on the company and role, their duties may include investigating and solving database problems, repairing glitches and designing elements that improve the storage and maintenance of data. Data Engineers are half software developer, half data scientist.

Data Engineers then analyze the data and make program or product recommendations based on their analyses. Alex is an educator turned programmer in training. Find out what she's up to at alexandriawilliams. com (big data ).

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Platform — CourseraLevel — BeginnerDuration — 6 months (3 hr/week)Image Source: CourseraThe Business Analytics Specialization is hosted on Coursera developed with Wharton School of the University of Pennsylvania. It provides a good foundational introduction to big data analytics across business professions such as marketing, human resources, operations and finance. The courses require NO prior analytics experience.

Having audited the course and completing it, I can say it surely develops a sense of how we as data analysts should and can describe, predict, and inform business decisions in specific business areas. I am sure, after completing the specialization, any learner will develop an analytic mindset to help make better strategic decisions based on data, than having done before.

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Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years. Businesses in all industries are beginning to capitalize on the vast increase in data and the new big data technologies becoming available for analyzing and gaining value from it.

But it isn’t just those following a traditional academic path – such by studying for one of the best US data science masters degree courses I covered in a recent article – who can benefit. There are also a large number of free online courses and tutorials which a motivated individual could use as a springboard into a rewarding and lucrative career.

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A lot of this is because of the proliferation of self-service infrastructure and tools designed to automate many of the technical but repetitive tasks involved with data cleaning, preparation and analytics. eduvision training . This means workers are increasingly able to carry out complex data-driven operations such as predictive modelling and automation without getting their hands dirty coding complex algorithms from scratch.

It’s worth noting however that while you can educate yourself with these courses without spending a penny, some of them charge for certification when you’ve finished. Coursera provides one of the longest-established online data science educations, through John Hopkins University. It isn’t completely free – if you can afford it, you are expected to pay a course and certification fee – but this is waived for students who don’t have the financial resources available.

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To complete the program, students create a data product which can be used to solve a real-world problem. eduvision. Also from Coursera, this course is provided by PwC so unsurprisingly focuses more on business applications than theory. It covers the spectrum of tools and techniques which are being adopted by businesses today to tackle data challenges, and the different roles that data specialists can fill in modern organizations.

The four-week course concludes with a task involving deploying a data solution in a simulated business environment, This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment.