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If you have short hair that you’re looking to real hair extensions, thin hair that you want to bulk up or just love the idea of a completely new look, then adding real hair extensions might be the perfect solution for you. However, before you make any commitment to this service, it’s important that you do your homework. According to hairstylist Marc Mena, there are many different types of extensions and that you’re going to need to know what type works best for your specific needs. He recommends researching the various options such as clip-ins, tape, sew-ins or micro-tip and bonded extensions.

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You’ll also need to figure out which length is right for you, as there are a variety of lengths available from 7 inches (think: bob) to 18 inches. And if you have natural textured hair, there are even options that blend effortlessly with curl patterns and coils.

One thing to keep in mind is that while there are many claims about hair extensions damaging your own natural hair, it really depends on the type of extensions and how they’re applied. ‘Providing that they are properly applied by a trained professional, there shouldn’t be any damage caused to your own hair,’ says Bailey.

That being said, it is crucial to find a salon that you trust and have a good relationship with your stylist. ‘You’re putting a lot of faith in them, and they should be someone that you can talk to and get any questions that you may have answered,’ explains Bailey.

Data science is a domain that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. It uses complex machine learning algorithms to build predictive models and requires a deep understanding of the functioning of databases, as well as of programming languages like Python.

While a common misconception is that you need a science or math Ph.D to become a data scientist, that’s not entirely true. There are plenty of multidisciplinary data scientists out there who are able to combine a strong foundation in statistics, programming, and business, along with advanced skills in data visualization, machine learning, and other specialized disciplines.

The first step in data science is to gather structured and unstructured data from multiple disparate sources—enterprise systems, public domain data, and more. It’s important to recognize that raw data can be messy and chaotic, with mismatched or missing records, inconsistent formats, and more. This process is known as “data munging,” and it requires a good pattern-recognition sense and clever hacking skills to transform masses of database-level information into a form that’s ready for analysis.

From here, data scientists use a variety of methods to find new insights and opportunities in the data. Predictive analytics forecasts what will happen based on existing patterns in the data, while descriptive analytics examines the underlying factors that influence those outcomes. Advanced methodologies include ML algorithms, recommendation engines, neural networks, and simulation.

Lastly, data scientists often need to communicate their findings to non-technical audiences. This includes creating narratives about the problem and its solution, supporting them with data insights. The Oakland Athletics’ general manager in the movie Moneyball, for example, used data on player performance that other teams overlooked to assemble a successful team despite a limited budget.

Data science is a rapidly growing field that has applications in nearly every industry. Companies from Amazon to Google use it to predict customer behavior and identify new products or services they could offer. It’s also been used to improve customer service by identifying patterns in customer complaints and satisfaction surveys.

If you’re interested in becoming a data scientist, a comprehensive program like Great Learning’s Postgraduate Diploma in Data Science can give you the skills and knowledge you need to succeed. The program is developed in partnership with top-ranked universities, including MIT and Northwestern University. It teaches you the latest tools and techniques for working with big data, helping you build real-world applications through hands-on projects and case studies. Learn more about the program today.