Amazon QuickSight

Predictive Analysis in Business

Decision-making in business is often based on assumptions about the future. Many companies aspire to develop and deploy an effective process for understanding trends and relationships in their activity in order to gain forward-looking insight to drive business decisions and actions. This is called predictive analytics. We can define predictive analytics as a process that uses data and a set of sophisticated analytic tools to develop models and estimations of an environment's behavior in the future. In predictive analysis, the first step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), and geographical data, among other options. It is important to have accurate and up to date information. Most of the time, you will have information from multiple sources and, quite often, it will be in a raw state. Some of it will be structured in tables, while the rest will be semi-structured or even unstructured, like social media comments. The next important step is to clean and organize the data - this is called data preprocessing. Preprocessing usually takes up 80% of the time and effort involved in all analysis. After this stage, we produce a model using already existing tools for predictive analytics. It is important to note that we use collected data to validate the model. Such an approach is based on the main assumption of predictive analytics, which claims that patterns in the future will be similar to the ones in the past. You must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can only be valid for a certain time period, since reality is not static and an environment can change significantly. For example, the preferences of customers may change so fast that previous expectations become outdated. So, it is important to monitor a model periodically. There are plenty of applications for business based on predictive analytics. To conclude this article, we will briefly consider some of them.

A Brief History of Data Science

Data science, AI, and Big Data have been the biggest buzzwords of the technological world over recent years. But even though there’s a lot of marketing fluff involved, these technologies do make a real difference in highly complex industries like healthcare, financial trading, travel, energy management, social media, fraud detection, image and speech recognition, etc. With the digitalization of the world economy and virtually every aspect of life, data has become the new oil (a term coined by Clive Humby). Subsequently, data science has become the sexiest job of the 21st century. But that’s really cutting a long story too short. Let’s look at the development of data science in more detail.

Introduction to Data Science: Resources Available Online

Data Science is a highly developing field, with a steady upslope of demand for data scientists. Job openings for data scientists have increased by 56% over the past year, according to LinkedIn. There are more and more people who want to start their career in Data Science, or plan to use some Data Science techniques in their work. An important question emerges for the people following this route: “Where can I start learning Data Science?” There is no simple answer to this question. Data Science is a complex multi-disciplinary field. It employs techniques and theories from statistics, multivariable calculus, linear algebra, and Machine Learning. Data scientists need good knowledge in the fields mentioned above, as well as strong programming and data visualization skills. There are many offline and online university programs for those who want to gain a degree in Data Science. In this article, we will consider the case of a person who already has enough background in math, statistics, and programming, and focus on online resources specifically for Data Science. The basic concepts and techniques of Data Science can be learned in different ways, but, in general, it is better to use a resource that gives a complete picture of the subject, such as MOOCS. E-books are also very useful in understanding the basic concepts of Data Science. Usually, books open the subject deeper, but less widely than MOOCS. So, in my opinion, the best way to start is to find a MOOC or e-book that corresponds to your skill level (according to the requirement skills for Data Science mentioned above). For your reference, we have listed below some MOOC platforms, courses and e-books that can be helpful for beginners. MOOCS: