A Guide To Data Science And Machine Learning
Technologies tend to increase in complexity as they improve and evolve and many have trouble understanding concepts like data science and machine learning. This is especially true of the leaders and key personnel of organisations who typically do not have knowledge of or interest in tech-heavy digital technologies.
This lack of understanding can delay an organisation’s move to integrate various concepts and technologies to enhance efficiency and improve business processes and the efforts of a data science consulting company may be met with resistance. However, this guide to data science and machine learning will, without doubt, be useful when understanding the concepts.
Data Science
Not being familiar with data in the modern world is almost impossible as it fuels digital technologies and industries. Data plays a key role in efforts to increase efficiency, boost growth, and improve customer experience. It is described as characteristics or information collected through observation and is typically in a numerical form.
Data is essentially quantitative or qualitative variables that are structural or unstructured and organisations invest heavily in mechanisms to measure, collect, report, and analyse data, which can also be visualised with the use of images and graphs.
Given how important data is to organisations today, it is a given that scientific methods, processes, algorithms and systems have been developed to extract insights from data. This is known as data science and it is an inter-disciplinary field.
Now that you have a better understanding of data science, you may wonder what a data science consulting company can offer your organisation. Data science consulting uses various tools and services to improve a client’s analytics skills and develop competencies. Services a consulting firm may offer include model development, strategy building and validation, and employee training.
Considering the role played by data, more and more businesses are turning to industry experts to help them make better use of data and analyse data sets to further improve business functions.
Machine Learning
Data science is used to identify patterns in data and these patterns are important in machine learning. Machine learning is described as the process of having a machine learn to recognise patterns by examples instead of having to be programmed with specific rules.
A machine learning consultancy would essentially create algorithms that learn from complex functions or patterns to make predictions.
There are three main steps involved in this process. Firstly, the data is gathered. A pattern is then found in the data and a new pattern is predicted based on the data.
There are three main types of machine learning and AI service providers will use a type best suited for the purpose.
Supervised machine learning is the most common of the three and can solve regression and classification problems. Unsupervised machine learning can be used to detect patterns in data and group them. This is done by clustering or association.
The third type of machine learning is reinforcement, where a reward/penalty system is used. Reinforcement machine learning may be used in self-driving cards and when training a machine to play a game like chess.
To some, machine learning may seem like it has limited applications and caters primarily to the tech industry. This is far from the truth, however, as several industries use machine learning to provide better services.
In healthcare, for instance, machine learning is used to make diagnostic predictions that can reduce the time healthcare professionals spend trying to determine the possible causes of a condition. In e-commerce, machine learning is used to predict customer information, like purchasing patterns and churn rate.
There is thus a need for data science and machine learning and organisations that integrate the technologies and concepts with their operations and processes will find that they are able to offer clients and customers an improved and efficient service while staying ahead of competition.
If your organization is yet to make use of data in this manner, it is recommended that you contact data science and machine learning professionals to develop strategies and tools that will enable you to make the most of these concepts.
