Advanced methods in data science and big data analytics
Data science and big data analytics is a buzz word these days. Small and big companies are investing in data science and big data analytics because they are realising the value it can add to their business operations. They are willing to hire candidates who specialise in these fields at an attractive salary. Since there is so much demand in this industry, a lot of young professionals are interested in joining this field. These professionals are gaining knowledge of data science and big data analytics by taking up data science certification and big data analytics certification courses available online or through the traditional college route.
Data Science is the interdisciplinary field that uses advanced methods, processes and algorithms in order to get data and insights from large data sets. These data sets could be structured and unstructured. Data science is similar to data mining however it combines various methodologies of data analysis such as statistics, machine learning and others. Through all this they analyse the data at a deeper level using computer science, maths, information science and statistics. As data science is reshaping business analytics, data scientists are in great demand these days.
Big data analytics is the measurement and analytics of large, structured or unstructured data sets. Big data allows organizations to study complex data covering various aspects of customer, market and business operations. Traditional analytical tools are unable to work on such large and complex data sets and specialised big data tools are to be used to work on such huge data sets and derive insights out of it. Some of the main big data tools are Hadoop, Hive etc.
There are many advance methods within the two disciplines of data science and big data analytics. These methods are used to effectively work on a variety of data sets and make sense of them. Some of these methods are as follows –
- Natural Language Processing
Natural language processing (NLP) is an area that mixes computer science and artificial intelligence (AI) to analyse the interaction between humans and machines. It deals with programming the computers so that they can analyse large volumes of natural human languages using computer software. They deal with fields like speech recognition, natural language understanding, natural language generation.
- Social Network Analysis
Social network analysis is a very interesting field of that deals with analysis of social networks. It uses graph theories and network characteristics in terms of nodes. A node could be a popular actor or a popular trend or anything else in the network around which interactions or events may happen. Some examples of social structures which can be studied using this method are common social media networks, friend networks, memes spread, disease transmission etc. This type of analysis is recent and gained a significant following these days. It has uses in the fields of communication studies, political science, developmental studies and also as a consumer tool.
- Data Visualization
Data visualization is the area dealing in the creation and study of visual representation of data. It uses statistical graphics, information graphics, plots and other tools. It converts numerical data to visual graphics in order to analyse it effectively. It makes complex data be more accessible and easy to understand. It can show data patterns and relationships visually. It is a descriptive form of analytics. Increased amounts of data on the internet and the increasing number of sensors in the environment are expanding the volume of data around us. Such large data sets can be seen better visually. Data scientists help address this challenge.
- Simulation
Simulation deals with the imitation of real life processes. It requires for data scientists to build models imitating the real-world scenarios with their key characteristics. While the model represents the system, the simulation represents the operations of that system. It helps in gaining insights into problems through modelling methods.
- Random Decision Forests
Random decision forests are a statistical learning method that creates many decision trees. It was created by Tin Kam Ho.
Multinomial Logistic Regression
Using tools and methods of value as such as above, businesses are trying to extract the maximum value out of data analysis. These advanced quantitative methods require experts to study them and develop the information in them to business friendly insights which can then be used by companies to grow and stay ahead of competition.
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