What you need to know about Data Science

 

Data Science is a field of Science that deals with analyzing data while still extracting any useful knowledge from the data.  The most important activity of any Data Scientist is usually in building the predictive models. The name “Data Science” is not commonly accepted yet because data science is relatively new and also because other names are used to describe what data science is technical.  The technologies of Data Science in comparison to other IT jobs have generated numerous job titles that pay good salaries. If you are interested in knowing more about data science you can enroll in a data science online course that will help guide you in whatever you want to do in your career.

If you are interested in learning data science , you need to know that data extracted in data science can either be structured or unstructured. Just like data mining, predictive analysis and statistics, data science is a continuation of the data analysis.

Various methods are used in data science, some of this methods include:

  •    Signal processing
  •    Machine learning
  •    Data mining
  •    Database
  •    Statistical learning
  •    Data engineering
  •    Visualization
  •    Learning
  •    Pattern recognition
  •    Computer Programming
  •    Uncertainty modeling

Big data is a big field which is more focused on pre-processing and organizing data as opposed to analyzing data and therefore, data science is not restricted to big data. In the last few years, the importance and growth of data science has been enhanced

Origin of Data Science

Before you even begin to learn data science online, it is important first to know and understand the origin of data science. Over the last couple of years, data science has been an integral part in the working of many industries in the market such as marketing optimization, agriculture, fraud detection, risk management, public policy and marketing analytics among others.  Data science tries to resolve most issues in most of these sectors together with the economy at large through the use of machine learning, predictive modeling, data preparation and statistics. An advantage of data science is that it will emphasize on some of the general methods without having to change its application and this is irrespective of the domain.  This approach that is employed by data science is far different from the traditional statistics method that focuses on providing some solutions that are specific to domains or certain particular sectors.

Data science today has high implications in many fields which may both be academic and applied research domains for example speech recognition and machine translation.

Data Science vs. Machine Learning

You can think of machine learning and data science as close cousins. The most common thing about data science and machine learning is the supervised learning methods, i.e. learning from historical data. An advantage of data science to machine learning is that data science is more concerned with the visualization of data together with presenting results of the data in a form that is understandable to people. Data science, unlike machine learning, has some big focus on data engineering and data preparation.

Machine learning, on the other hand, focuses on the learning of algorithms. Unlike data science, machine learning is not concerned with data visualization. Through machine learning, you will not only learn from historical data but also you will learn in real-time. The algorithms of the agents that act in the environment and learning from their actions are some of the major parts of machine learning.  This is referred to as Reinforcement learning (RL).

What Do Data Scientists do?

Most of the data scientists in the market today, have trained in math, statistics and computer science. The experience of the current data scientists in the market is extended to data mining, data visualization and information management. It is common for the data scientists to have some previous experiences in fields such as cloud computing, infrastructure design and data warehousing.  Listed and discussed below are some of the advantages of data science in the field of business:

  •    Delivering relevant products: One great benefit of data science in most organizations is that the organizations can now find when and where their products sell best. By having this knowledge of when and where the products of an organization sell best, the business can now deliver the right products at the right stipulated time. With this delivery of the products in the right specified time, most business companies can now develop newer products that can meet the needs of their customers.
  •    Mitigating fraud and risk: The experts who have specialized in data science are trained to identify any data that stands out is any particular way. These scientists create statistical, path, network, and also big data methodologies for any predictive fraud tendency models. They use these methodologies to generate or create alerts that will ensure timely responses whenever there is any unusual data that is recognized.
  •    Personalized customer experiences: One known benefit of data science is the ability for marketing and sales teams to know and understand their audience on a granular level.  Based on this knowledge an organization is capable of creating the best customer experiences possible.

Ways through which a Data Scientist can add Value to Business

Empowering officers and management to make better decisions

One thing about experienced data scientists is the fact that most likely they become trusted advisors and strategic partners with the business organizations upper management. They do this by ensuring that the organization’s staff maximizes their analytics and capabilities.

Directing action based on trends

One characteristic of a data scientist is the fact that he or she explores and examines the organization’s data. After reviewing the organization’s data, the data scientists are used to recommend and prescribe actions that can help improve the institution’s performance.