Introduction to Data Analytics

Remember all that times you spent in school learning about statistics and numbers, wondering to yourself whether it would ever come to use in the future. While the outreach of mathematics touches every corner and echelon of our lives, it presents a much better opportunity if its language is molded to provide some context. For a generation that generates petabytes of data every moment resulting from transactions, surveys, online interactions and a lot more, it becomes a grueling task to paint a rosy picture from those numbers. Drastic times call for drastic measures which is necessary when considering what the future has in store as more data is produced at larger rates with faster frequencies.

To the average eye, it’s just a cavalcade of numbers but if processed and studied under the tenets of data analysis, the results are highly useful to everyone from corporations to marketing groups to even politicians.

What Does Data Analytics Entail?

The polymath Francis Galton has several inventions and technologies to his name. And perhaps the one that set off the chain for data analysis was regression. The simplistic idea of having a dependent and an independent variable related to each other in terms of a model may not have seemed revolutionary back then but it opened the doors to find other ways to simplify data. Another major subset of the simplistic approach classification that seeks to split or classify data into tiers that better help people understand the causes behind them and their frequencies.

At its crux, there exist supervised and unsupervised data that differ in terms of the input/output scheme and the addition of predictor/predicted variables. Data analytics as a whole can include the numeral approach where analysts use tools such as regression, support vector machines, scatter plots, ANOVA, t-tests and hypothesis modeling. Or it can include the classification approach that deals mainly with contextual and non-numeric data using methods such as treeing, J45 clustering, Bayesian methods, lazy algorithms and much more.

The Tools Of The Trade            

Data analytics requires a fine set of software’s for any enthusiast to master and use. These software’s could differ in terms of the function and process they have in the data analysis chain such as visualization tools, data cleaning software’s, algorithm builders and comparers. Due to the 4Vs of big data (Volume, Veracity, Velocity and Variety) in the world of operations studies and humanity in general, the most commonly used software’s allow for cross platform data sharing and processing capabilities for various file types.

Hadoop, Spark, R, Scala, SPSS, Tableau and Watson are some examples of such software’s that have a popular demand in the market not just among individuals but large scale corporations as well. While data analysis requires a rigorous learning rate and can have steep learning curves depending on the software’s used to study, the methods and algorithms remain the same among all.

The Data Chain

One must understand that data analysis requires crisp data free from errors, incorrect values, outliers, mismatched records, erroneous characters or redundancies. As a result all data first undergoes a filtering and cleaning process in the preprocessing stage. The next step in the chain takes the role of data preparation where the data is uploaded to a platform using a predefined file format. Data is properly inputted in the commonly used tabular or CSV formats which leads to the processing stage where cardinal facts about the dataset is found such as number of entries, sum, average, minimums, maximums and other information. Variables are then studied to find any existing relation between them and their relation to the output(predicted) variable. This can be become a much more complex task if there exists some correlation or if there are multiple inputs and outputs all influencing each other.

Data analysis ultimately boils down to the bias and variance tradeoff routine where the algorithms and models produced must predict the values with little bias or variance. Bias is a measurement of how the model value and average predicted. The variance is how much the predicted values at a given point vary for different forms of the same model. While it is seen to be impossible for data models to be so accurate, finding the most accurate technique that produces little of both becomes a crucial task in data analysis.