Data Analytics and Data Science

A modern data ecosystem includes a network of interconnected and continually evolving entities that include:  Data that is available in a host of different formats, structure, and sources. Enterprise Data Environment in which raw data is staged so it can be organized, cleaned, and optimized for use by end-users. End-users such as business stakeholders, analysts, and programmers who consume data for various purposes. Emerging technologies such as Cloud Computing, Machine Learning, and Big Data, are continually reshaping the data ecosystem and the possibilities it offers. Data Engineers, Data Analysts, Data Scientists, Business Analysts, and Business Intelligence Analysts, all play a vital role in the ecosystem for deriving insights and business results from data.  Based on the goals and outcomes that need to be achieved, there are four primary types of Data Analysis:  Descriptive Analytics, that helps de...

  The Data Science process is a framework that outlines the steps involved in solving a data-driven problem. The process typically involves the following stages: 1.      Problem Definition: The first step is to define the problem or question that needs to be answered. This involves understanding the business problem and the data available to solve itData Collection: Once the problem is defined, the next step is to collect the relevant data. This may involve accessing data from various sources, such as databases, APIs, or web scraping. 2.      Data Preparation: After the data is collected, it needs to be cleaned and transformed into a format that is suitable for analysis. This may involve tasks such as removing missing values, encoding categorical variables, or scaling the data. 3.      Data Exploration: In this stage, the data is explored to understand its properties and relationships between variables. This involves perfo...

Data science is an interdisciplinary field  that combines statistical and computational techniques with domain expertise to extract insights and knowledge from data.  It involves using various statistical and machine learning methods to analyze, process, and interpret large and complex data sets, with the aim of discovering hidden patterns, trends, and relationships that can be used to inform decision-making and drive business outcomes. Data science is a broad field that encompasses various sub-disciplines, including data engineering, data visualization, machine learning, deep learning, natural language processing, and more.  It involves working with data from various sources, such as structured data from databases and spreadsheets, unstructured data from social media and text documents, and semi-structured data from APIs and web services Data science has become an essential field in today's digital age, where...

  The terms Data Analysis and Data Analytics are often used interchangeably However it is important to note that there is a subtle difference between the terms and meaning of the words Analysis and Analytics . In fact some people go far as saying that these terms mean different things and should not be used interchangeably. Yes, there is a technical difference... The dictionary meanings are: Analysis - detailed examination of the elements or structure of something Analytics - the systematic computational analysis of data or statistics Analysis can be done without numbers or data, such as business analysis psycho analysis, etc. Whereas Analytics , even when used without the prefix "Data", almost invariably implies use of data for perfoming numerical manipulation and inference. Some experts even say that Data Analysis is based on inferences based on historical data whereas Data Analytics is for predicting future performance. The design team of this course does not subsc...

EDA stands for Exploratory Data Analysis. It is an approach to analyzing and summarizing data in order to gain a better understanding of the patterns, relationships, and trends that may exist within the data.  EDA is an important first step in data analysis, as it helps to identify any outliers, missing data, or other issues that may need to be addressed before more advanced analysis can take place. EDA can be done using a variety of techniques, including statistical analysis, visualization, and data mining. The goal of EDA is to uncover insights and patterns in the data that can be used to inform further analysis or decision-making.

  A modern data ecosystem includes a network of interconnected and continually evolving entities that include:  Data that is available in a host of different formats, structure, and sources. Enterprise Data Environment in which raw data is staged so it can be organized, cleaned, and optimized for use by end-users. End-users such as business stakeholders, analysts, and programmers who consume data for various purposes. Emerging technologies such as Cloud Computing, Machine Learning, and Big Data, are continually reshaping the data ecosystem and the possibilities it offers. Data Engineers, Data Analysts, Data Scientists, Business Analysts, and Business Intelligence Analysts, all play a vital role in the ecosystem for deriving insights and business results from data.  Based on the goals and outcomes that need to be achieved, there are four primary types of Data Analysis:  Descriptive Analytics, that h...