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Researching » Designing » Editing » Branding » Advertising » Crypto» Incubating» Auditing» Consulting » | .__|Data Analytics|__. A great start would be trying out PMMINCUBATOR's free, 3-day introductory data course to see if working in data could be the career for you. Let's give it a shot. What is data analytics? Most companies are collecting loads of data all the time-but, in its raw form, this data doesn't really mean anything. This is where data analytics comes in. Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions. A data analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company's next steps should be. You can think of data analytics as a form of business intelligence, used to solve specific problems and challenges within an organization. It's all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business-how certain customer groups behave, for example, or how employees engage with a particular tool. Data analytics helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you're making informed choices based on what the data is telling you. How businesses use data analytics Armed with the insights drawn from the data, businesses and organizations are able to develop a much deeper understanding of their audience, their industry, and their company as a whole-and ,as a result, are much better equipped to make decisions and plan ahead. What's the difference between data analytics and data science? You'll find that the terms 'data science' and 'data analytics' tend to be used interchangeably. However, they are two different fields and denote two distinct career paths. What's more, they each have a very different impact on the business or organization. Despite their differences, it's important to recognize that data science and data analytics work together, and both make extremely valuable contributions to business. As we progresses, you would be learning deeper about deeper the differences between a data scientist and a data analyst in our education, but for now let's cover two key differences. 1: What they do with the data One key difference between data scientists and data analysts lies in what they do with the data and the outcomes they achieve. A data analyst will seek to answer specific questions or address particular challenges that have already been identified and are known to the business. To do this, they examine large datasets with the goal of identifying trends and patterns. They then 'visualize' their findings in the form of charts, graphs, and dashboards. These visualizations are shared with key stakeholders and used to make informed, data-driven strategic decisions. A data scientist, on the other hand, considers what questions the business should or could be asking. They design new processes for data modeling, write algorithms, devise predictive models, and run custom analyses. For example: They might build a machine to leverage a dataset and automate certain actions based on that data-and, with continuous monitoring and testing, and as new patterns and trends emerge, improve and optimize that machine wherever possible. In short:data analysts tackle and solve discrete questions about data, often on request, revealing insights that can be acted upon by other stakeholders, while data scientists build systems to automate and optimize the overall functioning of the business. 2: Tools and skills Another main difference lies in the tools and skills required for each role. Data analysts are typically expected to be proficient in software like Excel and, in some cases, querying and programming languages like SQL,R,SAS and Python.Analysts need to be comfortable using such tools and languages to carry out data mining, statistical analysis, database management and reporting. Data scientists, on the other hand, might be expected to be proficient in Hadoop, Java, Python, machine learning, and object-oriented programming, together with software development, data mining, and data analysis. PROFFYMAXIMEDIA EXPERTS: OFFERS THE FOLLOWING DATA ANALYSIS SERVICES AND MORE TO COMPANIES; Manage the delivery of user satisfaction surveys and report on results using data visualization software Work with business line owners to develop requirements, define success metrics, manage and execute analytical projects, and evaluate results Monitor practices, processes, and systems to identify opportunities for improvement Proactively communicate and collaborate with stakeholders, business units, technical teams and support teams to define concepts and analyze needs and functional requirements Translate important questions into concrete analytical tasks Gather new data to answer client questions, collating and organizing data from multiple sources Apply analytical techniques and tools to extract and present new insights to clients using reports and/or interactive dashboards Relay complex concepts and data into visualizations Collaborate with data scientists and other team members to find the best product solutions Design, build, test and maintain backend code Establish data processes, define data quality criteria, and implement data quality processes Take ownership of the codebase, including suggestions for improvements and refactoring Build data validation models and tools to ensure data being recorded is accurate Work as part of a team to evaluate and analyze key data that will be used to shape future business strategies |
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