GHEBTECH

  Data Science - Big Data - Data Analytics These three terms are often heard frequently in the industry, and although their meanings share some similarities, they have profound differences.  Unstructured, structured, and semi-structured data are all dealt with in data science. Data cleansing, data preparation, data analysis, and other procedures are included. Statistics, mathematics, programming, and problem-solving are all part of data science, as is creative data capture, the capacity to view things in new ways, and data cleansing, preparation, and alignment. This umbrella phrase refers to a variety of strategies for collecting information and insights from data. Big Data is a term that refers to massive amounts of data that can't be processed efficiently with today's apps. Big Data processing begins with raw, unaggregated data that is often too large to fit in a single computer's memory. Big data is a buzzword for massive amounts of unstructured and organized data that c...

  The world we live in is data-driven. In fact, the amount of digital data available is rapidly increasing, and transforming how we live. After Hadoop and other frameworks solved the storage challenge, the focus on data has switched to processing this massive volume of data. When it comes to data processing, the terms Data Science, Big Data, and Data Analytics come to mind, and there has always been a misunderstanding between them. When it comes to data processing, the terms Data Science, Big Data, and Data Analytics come to mind, and there has always been confusion between them. Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics: Data science VS BigData VS Data Analytics Data Science is a collection of tools, algorithms, and machine learning techniques aimed at uncovering hidden patterns in raw data. It also entails solving an issue in a variety of approaches to arrive at a solution, as well as designing and constructing new data modeling and produc...

Big data - Data science - Data mining - Machine learning - Deep Learning - Artificial neural networks. What are the various terminologies used in the world of data science?  Many terminologies in data science are interchangeable, let's take a look at the most prevalent ones. The term " Big Data " refers to datasets that are so large, fast-growing, and diverse that they defy typical analytical methods like those used with relational databases. Organizations now have the ability to examine these massive data sets because of the simultaneous development of immense processing capacity in dispersed networks and new data analysis tools and techniques. The five V's are frequently used to define big data: velocity, volume, variety, veracity, and value. Volume : Big Data refers to massive and ever-increasing amounts of data to store and process. Velocity : Big Data that has been optimized must deliver the right answer at the right moment and through the right channel. Variet...

Social Big Data -  Big Data social media strategy -  Social media mining -  social media Companies today acquire massive amounts of data at breakneck speeds. This onslaught of large data can be overwhelming, especially if you have no idea how to examine it.  Organizations may better understand their consumers' demands and how to continue to build their business when this data is used and evaluated wisely. Businesses trying to promote their brands, attract new users, and retain consumers continue to find social media networks to be effective platforms.  Social networking platforms are generating enormous revenue for many firms thanks to upsell chances, incentives, and new features. What, though, is more valuable than the money generated by these networks? Big data is the answer. By utilizing Big Data, marketers may provide a more tailored experience for their users while also increasing business profitability. That's a lot of data that marketers and the advertisi...

Business Analytics - Predictive Analytics  Business analytics (BA) encompasses all of an organization's performance measurement tools and approaches. Business analytics is a set of statistical techniques that can be used to analyze a project, process, or product. Business analytics can also be used to assess a corporation as a whole. Business analytics is used to find flaws in existing processes and to highlight important data that can help a company prepare for future development and issues. Predictive analytics refers to a set of analytical and statistical methods for creating models that can predict future events or actions. Predictive models come in a variety of shapes and sizes, depending on the event or behavior that needs to be predicted. Almost all predictive models generate a score, with a higher score indicating that a specific event or behavior is more likely to occur. The demand for strong business analytics has prompted the development of business analytics software an...

  Business intelligence software (BI software) is a type of software that aids in the measurement and improvement of productivity in all types of enterprises. The major goal of BI software in businesses is to reduce costs and speed up the production cycle. In the beginning, BI software was mostly targeted at large manufacturing organizations rather than small businesses. Traditionally, corporate automation was handled by giant mainframe computers, which were designed to execute many of the same duties that office personnel did. Data processing, text mining, online benchmarking and analytical processing, and predictive analysis are all examples of BI software tools that started as a computer shift that gradually supplanted white-collar occupations, particularly in factory accounting. Key performance indicators are commonly used in digital dashboards. By gathering information from massive amounts of data and applying it to business developments, data mining techniques aid in the iden...

Social media analytics - Social media analytics tools - Business intelligence Social media analytics is the process of extracting business insights from social media platforms such as Facebook, Twitter, and Instagram. Likes and shares aren't the only metrics used in social media analytics. Even counting the number of answers, comments, and link hits are insufficient. This approach also helps organizations to measure client sentiment and discover trends as a subfield of social media marketing. In a nutshell, it entails using social media to track the effectiveness of activities taken as a result of these decisions. The concept of social listening is also included in Social Media Analytics. Listening entails keeping an eye on social media for issues and possibilities. Listening is generally integrated into more comprehensive reports that include listening and performance analysis in social media analytics solutions. It uses software tools to convert modulated and non-modulated data i...

  Python in Data Science - Python Libraries Python's popularity in the data science industry has exploded in recent years, and it's now the programming language of choice for data scientists and machine learning professionals trying to improve the functionality of their apps. Python also includes a huge number of libraries that help data scientists execute complex jobs without having to deal with a lot of code. Python is one of the world's third most popular programming languages. We'll go through 7 Python libraries that can assist you in creating your first data science application in the sections below. Numpy In many data science initiatives, Arrays are the most significant data type. NumPy is a software library that provides a wide range of multidimensional array and matrix operations and is us...