Day: August 24, 2024

Are you an aspiring data analyst looking to take up a data analytics course in Pune? Then, learning about the data analytics lifecycle is fundamental to your knowledge and expertise. So, let’s look at the data analytics lifecycle and the six phases, including discovery, data preparation, model planning, model building, communication results, and operationalization that make up for it. Data Analytics Lifecycle and its Significance The data analytics lifecycle defines the roadmap of the way data is generated, collected, processed, used, and analyzed to accomplish business goals. These processes refer to an organized way of converting data into useful information to help businesses achieve project or organizational goals. The lifecycle guides and provides strategies for extracting information and moving in the right direction to achieve business objectives. Analysts use the circular representation of the lifecycle to analyze data in a forward or backward direction. The insights they receive help them decide whether to proceed with the existing research and stop or rework the analysis. Why should you learn about the data analytics lifecycle? The lifecycle aims to address big data problems and data science projects. The systematic and step-by-step methodology helps analysts plan tasks concerning data acquisition, processing, analysis, and recycling. These phases or stages help data analysts address specific big data analysis needs. 6 Stages of Data Analytics Process Before we see the phases of data analytics, let’s look at the various steps involved in data analysis with an example. Let’s say, an eCommerce portal is struggling with a massive number of cart abandonments. The decision-makers have taken cognizance of this concern and want to know what’s driving people away from the brand after creating a cart. As a data analyst, this is what you would do. 1. Define the Problem The process begins with understanding the task and the stakeholder’s expectations for the solution. It would involve asking the managers and other stakeholders questions about cart abandonments to find a solution to their problem. It would also involve finding the problem’s root cause to understand the concern. A couple of key questions that you must ask yourself include; Which problems have the stakeholders mentioned What are their expectations from the solution 2. Data Collection The next step is collecting data from multiple sources, including external and internal. Internal data is available in the company, whereas external information will have to be collected from outside the organization. Data generated from own resources is first-party data, while that collected and sold is called second-party data. On the other hand, data collected from external sources is termed third-party data. Common sources of data are feedback, questionnaires, surveys, etc. Accordingly, as a data analyst, you will have to collect cart abandonment data from the system and conduct online surveys to ask users why did they abandon the cart. 3. Data Cleanup The next process is cleaning the data collected. It might comprise redundancies, duplication, and irrelevant information. You must remove such data to ensure you have relevant and only the data you need to analyze. While helping you analyze the data effectively, it would also enable you to identify trends and patterns. Another significant part of this process is determining if the data is biased toward something. Such data wouldn’t let you drive the right inferences. 4. Data Analysis This is where the actual analysis begins. It involves analyzing the data, identifying trends, making calculations (using tools like Excel or SQL (Structured Query Language), and combining data for better outcomes. Additionally, programming languages like R and Python also help you analyze data. In the context of the eCommerce company, it would involve understanding, analyzing, and grouping the various reasons for cart abandonments. Check Out Full Blog – Data Preprocessing in Machine Learning: 7 Easy Steps to Follow 5. Data Visualization Visualizing helps non-technical people or the consumers of the data understand complex data. The transformed data has to be made into a visual, including a chart or a graph for a simpler comprehension of the data. You can leverage various tools to do that. A couple of them include Tableau and Looker. Tableau includes a simple drag-and-drop tool that helps create effective visualizations. Whereas, Looker is a data viz tool directly connecting to the database and creating visualizations.  6. Data Presentation Presentation is the last step in the data analysis process. It involves transforming raw information into an easily comprehensible and meaningful format. You can present the data in various forms, including graphs, charts, tables, etc., to make it easier for decision-makers to draw conclusions and make informed decisions. For example, after analyzing the data, you’ve categorized various reasons for cart abandonments, including slowly loading web pages, external distractions, network issues, unspecified, etc. If you decide to show it through a pie chart, you will be able to show the reasons and their pie share depending on the cart cancellations they result in. If slowly loading the webpage is the most common reason, the company can make efforts to enhance the website’s speed and gradually reduce the number of canceled carts. Data Analytics Lifecycle Phases Here are the six phases that form the data analytics lifecycle. Phase 1: Discovery The data science team explores the issue and investigates it. It builds context and understanding. Learn about the required and available data sources. The team builds an initial hypothesis that can later be tested with data. Phase 2: Data Preparation Methods or steps to discover, preprocess, and condition data before modeling and analysis. An analytic sandbox is required. The team executes, loads, and transforms to get data into the sandbox. The team may perform data preparation tasks several times and not in a predefined order. Some tools used for this phase are Alpine Miner, Hadoop, and Open Refine. Phase 3: Model Planning The data science team studies the data to identify connections between variables. Next, it selects crucial variables and the most useful models. Datasets used for testing, production, and training goals are created. The team builds and executes models depending on … Read more