Good insight Billy. Visualizing or storytelling with data definitely requires a set of skills for precise interpretation of the data. Like Google’s chief economist Dr. Half R. Varian said understanding, processing, and extracting value from data would be done important in the coming decades. There are several data types and their ideal representation. For example, candlestick patterns are ideal for stocks. For us to be able to do deliver and story tell using data, you need to develop a strategic skillset. Deliver a statement that arouses the interest of an audience. Pose a question that requires solving and offer the solution. Highlight the benefits related to your given solution and call for a call of action. This is mainly for data analysts.
There are several data types and their ideal representation. For example, candlestick patterns are ideal for stocks. For us to be able to do deliver and story tell using data, you need to develop a strategic skillset. Deliver a statement that arouses the interest of an audience. Pose a question that requires solving and offer the solution. Highlight the benefits related to your given solution and call for a call of action. This is mainly for data analysts.
Thanks for the good explanation, Kiana. A sparse matrix is commonly used to avoid wasting memory. In most cases, zeros in the matrix are of no importance and therefore represented a sparse matrix using a 2D array leads to wastage. It becomes necessary then to only store the non-zero elements using the triples of a row, column and value. The two common representations are linked list representation and array representation. Linked list representation has for fields in each node; row, column, value, and next node.
This is a good source of the overview of the challenges Aaron. When the areas that may have issues are addressed, the data is said to be ready for use and we can make a presentation of the same. Preparing this data means cleaning, structuring, and enriching raw data. Many teams dealing with big data analytics face the challenge of overreliance on IT departments where they source their data for analysis. They also face the challenge of lack of context of the use case. Missing data can also be a problem that we face and sourcing of data is necessary to address this shortcoming. After dealing with the challenges, we need to reevaluate the data and ensure it is ready for presentation.
Thanks for the post, Dominic. There is a long history that is associated with digging through data to unravel some hidden details and connections that can help with making future predictions. This concept is enclosed in scientific disciplines of machine learning, statistics, and artificial intelligence. Data analysis has been made easier in the current age with advances made in technology; speed and processing power. Data mining can be defined as the process of obtaining usable data from a big chance of raw data. It involves the analysis of data with the help of softwares in large batches. Businesses are able to learn more about their customers and come up with elaborate strategies to help leverage their resources in an insightful way.
Thanks, Ori for your post. Data wrangling is very important and crucial since it is the only way to convert raw data to usable information. Customer information comes from different departments within an organization and is oftentimes stored in different pieces. It is therefore important to create a picture that is whole of what is happening within a given firm. Data automation tools help in the process of data wrangling. These tools are as well important because they reduce errors and human dependency. Piecing together raw data is termed good data-wrangling which also involves understanding the context of the data business-wise. There are activities that are involved in the process of data wrangling. They include; discovering, structuring, cleaning, enriching, validating, and publishing.
Good piece Aaron. Rescaling of data is necessary to ensure that the comparison makes sense. There are problems however that are associated with scaling two different variables. It is difficult to compare variables and the usual way is calculating the mean for each and comparing them. In multi-item scales, those with different endpoints contribute differently to the scale score finally if used in their raw. Take for example a test that is marked out of 100 and another one out of 50. To solve these problems therefore we need to convert the scales to a common one to make them comparable. This can be done by the conversion of each scale such that they have similar lower and upper levels. The other method to achieve this is making sure the variables are standardized to express the values at standard deviation units.
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