Data is getting bigger and more diverse every day. Therefore, analyzing and processing data to advance human knowledge or to create value is a big challenge. To tackle these challenges, you will need domain knowledge and a variety of skills, drawing from areas such as computer science, artificial intelligence (AI) and machine learning (ML), statistics and mathematics, and knowledge domain, as shown in the following figure:
Let's go through data analysis and its domain knowledge:
Computer science: We need this knowledge to provide abstractions for efficient data processing. Basic Python programming experience is required to follow the next chapters. We will introduce Python libraries used in data analysis.
Artificial intelligence and machine learning: If computer science knowledge helps us to program data analysis tools, artificial intelligence and machine learning help us to model the data and learn from it in order to build smart products.
Statistics and mathematics: We cannot extract useful information from raw data if we do not use statistical techniques or mathematical functions.
Knowledge domain: Besides technology and general techniques, it is important to have an insight into the specific domain. What do the data fields mean? What data do we need to collect? Based on the expertise, we explore and analyze raw data by applying the above techniques, step by step.
Data analysis is a process composed of the following steps:
Data requirements: We have to define what kind of data will be collected based on the requirements or problem analysis. For example, if we want to detect a user's behavior while reading news on the internet, we should be aware of visited article links, dates and times, article categories, and the time the user spends on different pages.
Data collection: Data can be collected from a variety of sources: mobile, personal computer, camera, or recording devices. It may also be obtained in different ways: communication, events, and interactions between person and person, person and device, or device and device. Data appears whenever and wherever in the world. The problem is how we can find and gather it to solve our problem? This is the mission of this step.
Data processing: Data that is initially obtained must be processed or organized for analysis. This process is performance-sensitive. How fast can we create, insert, update, or query data? When building a real product that has to process big data, we should consider this step carefully. What kind of database should we use to store data? What kind of data structure, such as analysis, statistics, or visualization, is suitable for our purposes?
Data cleaning: After being processed and organized, the data may still contain duplicates or errors. Therefore, we need a cleaning step to reduce those situations and increase the quality of the results in the following steps. Common tasks include record matching, deduplication, and column segmentation. Depending on the type of data, we can apply several types of data cleaning. For example, a user's history of visits to a news website might contain a lot of duplicate rows, because the user might have refreshed certain pages many times. For our specific issue, these rows might not carry any meaning when we explore the user's behavior so we should remove them before saving it to our database. Another situation we may encounter is click fraud on news—someone just wants to improve their website ranking or sabotage a website. In this case, the data will not help us to explore a user's behavior. We can use thresholds to check whether a visit page event comes from a real person or from malicious software.
Exploratory data analysis: Now, we can start to analyze data through a variety of techniques referred to as exploratory data analysis. We may detect additional problems in data cleaning or discover requests for further data. Therefore, these steps may be iterative and repeated throughout the whole data analysis process. Data visualization techniques are also used to examine the data in graphs or charts. Visualization often facilitates understanding of data sets, especially if they are large or high-dimensional.
Modelling and algorithms: A lot of mathematical formulas and algorithms may be applied to detect or predict useful knowledge from the raw data. For example, we can use similarity measures to cluster users who have exhibited similar news-reading behavior and recommend articles of interest to them next time. Alternatively, we can detect users' genders based on their news reading behavior by applying classification models such as the Support Vector Machine (SVM) or linear regression. Depending on the problem, we may use different algorithms to get an acceptable result. It can take a lot of time to evaluate the accuracy of the algorithms and choose the best one to implement for a certain product.
Data product: The goal of this step is to build data products that receive data input and generate output according to the problem requirements. We will apply computer science knowledge to implement our selected algorithms as well as manage the data storage.