Introduction Data science: An art of interpretation of the data and bringing insights from the data. It is also a study of observations and interpretation in any form. Data visualization: Representation of the data. Data scientists need tools to deal with the data. What best value can be brought out of it? How can it be broken down? How one parameter is correlated to other? All these questions are answered with one of the solutions - Data Visualization. Best example on our day today basis is amazon's recommendation for a user while shopping. Machine is learning about a user's web activity and interprets and manipulate it thus by giving best recommendation based on your interests and choice of shopping. To provide this recommendation, the …show more content…
They are bound to each other. Data visualization is a subset of data science. Data science is not a single process or a method or any workflow. It is a combined effect of small miniatures dealing with the data. Be it a process of data mining, the EDA, modeling, representation. Example: To portray any incident/story in our daily basis, it could be conveyed as a speech but when it is represented visually, the real value of it will be established and understood. Also, it is not only about representing the final outcome, but also applicable in understanding the raw data. It is always better to represent the data in order to get better insights and how to solve the problem or get a meaningful information out of it which influences the system. To get better understanding of data science and data visualization, Let's say we want to predict what will be iPhone sales for the year 2018 vs google pixel 2018: How exactly one can predict the sales in the future? What are the prerequisites, how confidence is your prediction, what’s the error rate? All these are answered and justified using data science. Prerequisites, 1. Historical data - iPhone sales from the year 2010 - 2017 2. Trend of each year's and the …show more content…
The initial phase of analytics (i.e., Represent the available data and conclude what attributes and parameters to be used in order to build a predictive machine). So here in our example, it is historical data representation, opinion analysis on each iPhone by the users. Two - Outcome phase. The prediction results for the year 2018 has to be represented in a way that it reaches the world. Comparison between phone and google pixel sales for the upcoming years. Back to the iPhone analysis, the historical data has to be analyzed and pick the best attributes that cause significant impact towards the prediction rate (like sales on location wise, season-wise, age ). Followed by picking up the best model (Algorithms like Linear regression, logistic regression, support vector machine – to mention few). Train the model using the historical data and get the prediction for the upcoming year. This is a high level picture on the processes involved in the data science. Once the prediction results for the upcoming year is settled, it can be represented and get some insights that influence other analytics based on the sales and marketing techniques for a
By collating, calculating and analyzing historical trends, a pattern can be inferred. Consequently, assumptions can be made using these trends for the future.
Society is increasingly subjected to predictions on subjects as diverse as economic development, finance, fashion and even relationships. For instance, Economists forecast the gross domestic product of countries; Financial Analysts model the likely increase in earnings per share of a company based on potential sales of future products; Fashion forecasters predict how the mood of consumers determine the styles for next season’s haute couture collections; and websites encourage a person to input data about them self and an algorithm tries to predict their most suitable partner.
Secondly, data visualization is a way of squeezing an enormous amount of information and understanding into a small space.
Data integration consists of three processes that integrate data from multiple sources into a data warehouse: accessing the data, combining different views of the data and capturing changes to the data. It makes data available to ETL tools and, through the three processes of ETL, to the analysis tools of the data warehousing environment.
Google analytics can be applied in big as well as small businesses to support decision-making processes. In sense, each kind of business has its own
A picture is worth a thousand words. The ability to graphically represent your business data gives you the power to make informed business decisions quickly. (Microsoft.com, 2002) This representation must be visually appealing and easy to understand. By keeping it simple, it allows the broadest number of users to interpret the data, gain insights as to its meaning and facilitate communication on the data ultimately to solve the company¡¦s problem. Data visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition. (Wikipedia.org, 2005) That in itself is a method or technique of decision-making. To further break it down, the most popular and widely used tool of data visualization is the Pie Chart.
first day of sales for the new smartphone model (Dobby, 2012). To add to the idea of
When you consider predicting demand you also need to think about whether the demand will be the same all through the year. If you launch a new ice-cream you could probably predict that demand would be highest when the weather was hottest. How would this affect your forecast?
For the Samsung Galaxy Note 8, we expect the sales to grow in the first quarter of year one. With people still suspect about the phone there won’t be a fixed demand on a per month basis. After a couple months pending no problems with the smartphone people will have their faith in Samsung again and purchase the Galaxy Note 8. After the first year, we expect the Galaxy Note 8 to reach a profit of 1 billion dollars. During this section, I will explain the sales forecast for each month and for the next 3 years. Also, I will discuss the breakeven analysis of the Samsung Galaxy Note 8, the cost to build the smartphone, how much we are willing to pay for expenses and what expense we focus on the most.
Forecasting is related with predicting the future with the help of past and historical data this data is then put into a mathematical function or model. Forecasting is all depe...
Different predictive models and analysis are used to predict future which can be applied to different business to analyze something about current data and historical facts in order for getting better understanding about customers, products and partners and to identify possible risks and opportunities. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.
Business forecasting can be used in a wide variety of contexts, and by a wide variety of businesses. For example, effective forecasting can determine sales based on attendance at a trade show, or the customer demand for products and services (Business and Economic Forecasting, p.1). One of the most important assumptions of business forecasters is that the past acts as an important guide for the future. It is important to note that forecasters must consider a number of new information, including rapidly changing economic conditions and globalization, when creating business forecasts based on past sales.
Historical Analysis forecasts demand for a new product. It bases the forecast for one product on the demand for a similar product. An example would be forecasting demand for a new type of camera film based on sales of the company's latest camera in the market. This is an accurate way to predict the sales of products that share market share with similar products. (Chase 2005 pg514)
After collecting and analysing the data, data interpretation is carried out and is followed by report writing. Data interpretation has to be done very carefully otherwise wrong inferences may be drawn which leads to inaccurate conclusion and the whole objective of doing research may get invalid.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.