Data Science as a Career – What it Means, How to Get Into it and Key Trends

Data science is a booming field with plenty of opportunities. It’s important to know what to expect when entering this field. However, many people interested in the field may not be aware of the things they can do or the skills they need to build while preparing for a successful career in data science. So, how can professionals and students enter this field and become effective data scientists?

A hot topic in today’s job market, data science It is an emerging field that combines statistics, computer programming, and other disciplines to solve problems using data. Data science has many subfields within it, but it can be defined as the practice of using data to solve problems. The role of data scientist is to take raw information from various sources (such as sensors or satellites) and turn them into something useful for decision making by finding patterns within the collected information. The type of problem solving varies depending on what industry you work in, for example:

• A weather forecaster analyzes satellite images from around the world to predict whether any rainstorms will occur during their forecast period (a few days).
• A retail company uses customer information gathered over time through purchases at their store locations along with demographic data about their customers’ locations so they can offer coupons based on their place of residence, for example: “We know you live near our store so here are coupons!”

In general, data scientists need to be able to work with different technologies and tools, build knowledge about their domain and have effective communication with stakeholders. They must be able to perform on-the-spot analyses and make sense of large amounts of structured and unstructured data. For data scientists, the ability to solve problems is vital because they will be working on real-life business challenges that need immediate solutions. Data scientists should also be able to communicate effectively with peers who don’t have a technical background, since they’ll likely have stakeholders who don’t understand how data science works and they will need to provide them with useful feedback or critical insights into problems at hand.

Data science is a field that requires the application of multiple skills and possessing certain knowledge. A good data scientist will have:

• Strong analytical skills – data analysis, statistical, and data mining skills are essential to the field
• Machine learning expertise – machine learning, artificial intelligence (AI), deep learning are key associated sub-domains of data science
• Programming experience – understanding algorithms, programming languages such as Python or R are necessary for creating machine learning and statistical analysis models
• Business knowledge – business acumen is an important part of being able to apply your skillset to real-world problems in a company or organization
• Strong communication skills – the ability to take data, analyze and model it, and then effectively communicate it to a non-technical audience is essential for data science projects to succeed

Data science education has opened new horizons for traditional careers. It is a dynamic field, which can be applied to any industry. The data scientist uses their skill-set to solve problems in an efficient way and make better decisions by analyzing data.

Data science is not just about programming; it encompasses many other areas such as math, statistics, and business intelligence. These areas are essential for being a good data scientist because they help you understand how people think and what they want from the product or service that you offer them.

With more demand for Data Scientist roles across industries, it’s becoming clear that this is among the most sought-after jobs today! It’s also important to note that there’s no single course or degree program to specialize in Data Science, professionals seeking to enter the field need exposure through multiple courses in addition to a portfolio of data science projects that can demonstrate the value added by applying the data science methodology. Data science is a multi-disciplinary field and hence many data scientists typically combine knowledge across two or more domains.

Educational institutions can work towards building data science skills in students by offering courses that are practically relevant to the field and by providing a platform to learn from industry experts. Students should also be encouraged to participate in industry projects and data science competitions where possible. Academic institutions can also work towards building an ecosystem where students are encouraged to innovate and create new products that can be used by the industry. This partnership between academia and industry is key as data science is a practical field that solves key business challenges.

On the other hand, the industry needs to provide the right kind of opportunities for students to learn, grow, and demonstrate value. The curriculum should be designed based on the current trends in the market so that students can build a strong foundation. They also need to be given an opportunity to work with senior industry professionals to gain practical experience and hone their skills.

Major universities across the globe have started offering graduate and undergraduate courses in data science. In addition, there are a number of local universities and institutions offering these courses in Bahrain. For example, University of Bahrain offers a master’s degree in Big Data Science and Analytics, whereas the Bahrain Institute of Banking and Finance (BIBF) offers a bachelor’s degree in Data Science and Business Analytics in collaboration with University of London.

Industry giants such as LinkedIn and Facebook provide plenty of opportunities for aspiring data scientists to get their feet wet by working on real-world problems at scale. Even Google has launched its own university course which covers topics such as machine learning and artificial intelligence (AI). This means students will be able to get hands-on experience by participating directly in these exciting projects from day one!

For students to equip themselves as global-ready talent, they need to have skills which can be benchmarked against the global data science workforce. This will help them understand their strengths and weaknesses in practice and how they compare against local and global talent, enabling them to make informed career choices.

A global-ready data scientist needs to have a portfolio of data science projects that can be showcased to potential employers. This may include a Github repository, where a student uploads their projects work/ data science code for employers to verify. It is a way of showing off their skills and expertise, and it will help them stand out from the crowd.

Data science is a hybrid of computer science and statistics. While some people may think that being able to write code is enough to get hired as a data scientist, there is more than just developing algorithms at play here. A data scientist also needs to understand statistics well enough so as not only can test own findings but also explain them clearly in layman’s terms if necessary—something most programmers aren’t used to!

When it comes to future trends, there are various future trends that aspiring data scientists should be aware of and prepare for if they want to succeed in this rapidly changing field which requires keeping up-to-date, reskilling and upskilling as needed. For instance, there is a growing demand for automated machine learning (AML) solutions. AML has the potential to revolutionize the world of analytics by reducing cost and time, enabling real-time decision making, and empowering people with limited technical skills to take advantage of sophisticated algorithms.

In addition, augmented analytics refers to the process of creating new value from existing data using advanced visualization techniques and automating the work that would typically be carried out by a data scientist. End-to-end artificial intelligence solutions are also emerging as one of the most sought-after skills in this field because they enable organizations to gain insights from their businesses at scale without heavy IT infrastructure investments or complex programming and integrations.

Moreover, data democratization: It means giving everyone access to data they need so that anyone can visualize and get insights from their own data without needing technical expertise at all! Data democratization will be a major game changer as it will allow non-technical users to access powerful analytics tools previously only accessible by experts who could write code/algorithms from scratch themselves — which should eventually lead towards more democratization within companies too where everyone gets treated equally regardless of whether they’re engineers or programmers, but this remains an open question in the future.

Data science is an emerging field with a promising future. It is a combination of art and science and requires skills in both areas to be successful. For those who have the right set of skills and the passion to learn, there are plenty opportunities whether they are still students or already professionals.