Data analytics

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University of California, Davis *

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Information Systems

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Dec 6, 2023

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What is Data analytics? Data analytics is the science of analyzing raw data to make conclusions about that information. Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions. Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).

What is Data ecosystem? The term data ecosystem refers to the programming languages, packages, algorithms, cloud- computing services, and general infrastructure an organization uses to collect, store, analyze, and leverage data. Data ecosystems provide companies with data that they rely on to understand their customers and to make better pricing, operations, and marketing decisions. The term ecosystem is used rather than ‘environment’ because, like real ecosystems, data ecosystems are intended to evolve over time.

Data Analysis Lifecycle The process of going from data to decision

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Ask In this phase, two things are done-The problem to be solved is defined and we make sure that we fully understand stakeholder expectations. The first step in understanding stakeholder expectations is to determine who the stakeholders are. That may include managers, an executive sponsor, or sales partners. What they all have in common is that they help make decisions, influence actions and strategies, and have specific goals they want to meet. So, as a data analyst, developing strong communication strategies is very important.

Prepare Once there is an understanding of the problem, one can think about how to solve this. It is time to decide what data needs to be collected in order to answer the questions and how to organize it so that it is useful. One should think about the following aspects: · What metrics to measure? In answering this question, there might be a need to answer also sub- questions · What factors should be taken into account? · Where is the data located (files, database, external system, internal system)? · If the data will be moved, how it will be stored and what are the needed security measures to protect that data.

Process When we start using the data, it might be a combination from different sources or it might not be of the highest quality. Here, data analysts find and eliminate any errors and inaccuracies that can get in the way of results. This usually means cleaning data, transforming it into a more useful format, combining two or more datasets to make information more complete, and removing outliers, which are any data points that could skew the information. It also involves how to check the data you prepare to make sure it's complete and correct. This phase is all about getting the details right.

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Analyze Analyzing the data you've collected involves using tools to transform and organize that information so that you can draw useful conclusions, make predictions, and drive informed decision-making. There are lots of powerful tools data analysts use in their work like spreadsheets and structured query language. Further processing might include: · Performing different calculations to get additional metrics. · Combining additional data attributes from a variety of sources to get a more comprehensive story. · Create different views for the data.

Share Data analysts interpret results and share them with others to help stakeholders make effective data-driven decisions. In the share phase, visualization is a data analyst's best friend. Sharing will certainly help with: · Making better decisions. The feedback will help to answer the questions that initially were not thought of. · Making more informed decisions. Feedback will not be merely critic, but also suggestions and additional information on the matter. · Improve the general outcome. From one angle, the decision will most likely be more informed and better, but also the transparency will grant that there is more support to the findings.

Act Taken the results and depending on the problem statement, recommendations for further actions can be made at this step. And once the recommendations are ready, the actual decision can be made. Not necessarily is the conductor of the analysis, the one to make a decision, it could also mean providing the decision-makers(stakeholders) with recommendations based on the findings so they can make data- driven decisions. But the key here is data-driven decisions .

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Data Analysis Tools

Spreadsheets A spreadsheet is a computer program that can capture, display and manipulate data arranged in rows and columns. The following are just a few of the features available in most spreadsheet programs: ● Cell formatting: Within the spreadsheet, selected cells can be formatted to represent various numeric values. ● Formulas: Under the formula bar, users can perform calculations on the contents of a cell against the contents of another cell. ● Pivot tables: Using a pivot table, users can organize, group, total, average or sort data via the toolbar.

SQL Structured Query Language is a standard Database language which is used to create, maintain and retrieve a relational database. It is particularly useful in handling structured data. SQL consists of five basic commands to control structure, perform manipulation for transactions, and Data Analytics. It is an Open-source tool facilitating an integrated development environment and is widely used for data warehousing, logging, and inventory management. It stores data in a table format, consisting of rows representing a number of records and columns corresponding to various features. All back-end data storage and analysis processes use SQL queries comprising three phases — parsing, binding, and optimization.

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Visualization Data visualization is the process of graphical representation of data in the form of geographic maps, charts, sparklines, infographics, heat maps, or statistical graphs. Data presented through visual elements is easy to understand and analyze, enabling the effective extraction of actionable insights from the data. Relevant stakeholders can then use the findings to make more efficient real-time decisions. Few of the tools used are: ● Tableau ● Google Charts ● JupyteR ● Visual.ly

Few famous data analysts ● Yann LeCun- Director of AI Research at Facebook ● Dr. DJ Patil- first Chief Data Scientist at White House for Obama ● Leslie Kaelbling- leading professor and researcher at MIT ● Caitlin Smallwood- VP of Data Science and Analytics, Netflix

“Without big data analytics , companies are blind and deaf, wandering out onto the web like deer on a freeway” - Geoffrey Moore, author and consultant.

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Thanks!

Data analytics

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