Data science is the complete range of activities that encompasses artificial intelligence, machine learning, and deep learning. It applies mathematics, statistics and linear algebra to create algorithms that solve diverse business and operation issues in multinational organizations and start-ups alike.
Artificial intelligence is the ultimate goal to be achieved. The very basic purpose of it is to make machines think and act like humans. Artificial intelligence is achieved through machine learning and deep learning techniques. In present times, a career in data analytics and machine learning is seeing an upward trend.
The Concept of Data Analytics
The foundation of data science is plenty of historical data. Data may be gathered from various sources. Sometimes the organizations provide their own past data along with the data of their competition, if available. Sometimes, the analyst has to gather the data from several resources such as websites and relevant social media or e-commerce platforms. These collected data are raw and need to be cleaned, filtered and segregated. The job of the analyst covers all these activities and then applying proper algorithms to the same.
The knowledge of a programming language like Python or R is essential at this stage. While Python has its own set of algorithms that may be directly applied and thus recommended for beginners, R is an advanced language which will enable the analyst to create his or her own algorithms to extract meaningful insights from the data. When all these activities are complete, the analyst then applies visualization tools like Power BI or Tableau to transform these data into easily understandable pie and bar charts. The sole purpose of all these activities is to enable the management to take important business decisions regarding its products, services and much more.
The Concept of Machine Learning
When we read a machine to respond to situations in a way that a human would have done under similar circumstances, we achieve the purpose of machine learning. Machine learning is generally of three main types – supervised learning, unsupervised learning and semi-supervised learning.
- Supervised learning is the process of feeding labelled data as inputs so that the machine may respond to similar situations as per the input conditions. The inputs may be text, images, videos etc.
- Unsupervised learning is the case where there will not be any labelled data, but the machine will be programmed to read and draw useful insights from the data they get. This technique is used in clustering group data.
- Semi-supervised learning is a mixture of the above two. Deep learning is an advanced form of machine learning where the machine is made to mimic a human brain.
It is universally true that humans learn from the pages of history. History consists of past data. In earlier days, the quantity of this data was small, and it could be easily managed over manual accounting or, at a later stage, over a simple Excel sheet. Business and Operation Managers made the best use of these historical data to make future decisions. However, with the passage of time, the volume of data has changed, and so has the method of record keeping and analysing. Start-ups and big companies alike need data to predict their next business moves. They would like to know which products and services would remain relevant in business and which ones will fade out. They would also like to know the potential a particular business will have in the next financial year or further ahead. This demand has evolved analytics as a key career subject with the present-day young job searchers.
Similarly, machine learning also has its own application domain. We are privileged to the benefits of robotics. Machine learning has other applications in different services. For instance, a reputed spectacles merchant often uses this technique to enable its customers to understand which frame would best fit their face contour. A user of a social site is often recommended as per his or her earlier choices.
Course Details of Machine Learning And Data Analytics
The contents of the Data Analyst training course are very similar to those that are covered during Machine Learning as well. The courses are available in both online and offline modes. However, it is important for an aspiring candidate to join a reputed institute with credibility among employers. Furthermore, students should choose those courses which give them ample opportunity to enhance their practical experience with projects. The following topics are generally covered in data analytics certification courses -
- Advanced Microsoft Excel, basic mathematics, statistics, and linear algebra.
- Data analysis and project cycle life.
- Techniques of evaluation, exploration, and experimentation.
- Segment analysis using clustering and method of prediction.
- Data visualization with Tableau or Power BI.
- Analytics and recommender systems.
Both of these subjects have evolved as very demanding careers amongst the present job-seekers. A prospective candidate can learn data analytics from the postgraduate program in data science and analytics course taught at Imarticus. The duration of the course is 6 months. This course will help you achieve your dream and establish a career in sync with present requirements.