Beginners guide to Data Science

Data Preprocessing Data preprocessing is undeniably one of the most critical steps in the data analysis process. It serves as the foundation upon which reliable and meaningful insights can be derived from raw data. The preparatory phase is indispensable because it ensures that the data is properly structured, accurate, and consistent, thus mitigating any potential obstacles that may arise during subsequent analysis phases. One of the primary objectives of data preprocessing is to handle missing, incorrect, or inconsistent data. Real-world datasets are often imperfect, containing various anomalies such as missing values, outliers, and errors. These anomalies can significantly lead to wrong analysis results and compromise the reliability of any subsequent models built upon that data. Raw datasets typically contain features that may have different scales, units, or distributions, making them incomparable or biased towards certain features during analysis or modeling. Therefore, data p...

 OLTP(Online Transaction Processing):- Online transaction processing shortly known as OLTP supports transaction-oriented applications in a 3-tier architecture.  OLTP administers day to day transaction of an organization. It refers to a class of systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval.  OLTP systems are designed to handle a large number of short online transactions in real-time. These transactions could include inserting, updating, or deleting small amounts of data in a database, such as recording sales transactions in a retail environment or processing banking transactions.  OLTP systems are commonly used in various industries, including retail, banking, e-commerce, and telecommunications, where real-time transaction processing is essential for business operations. OLAP(Online Analytical Processing):- Online Analytical Processing, a category of software tools which provide analysis of data for busin...

  What is Data Science? The term "data science" inherently suggests the application of scientific methods or mathematical formulas to manipulate data. With data being generated incessantly, often in petabytes, there's an increasing need for various methods and algorithms to extract insights and facilitate decision-making. Data science integrates principles from statistics, computer science, and domain-specific knowledge to analyze datasets and operational efficiency in businesses and organizations. Crucially, data science thrives on its interdisciplinary nature. Data scientists possess a diverse skill set encompassing good amount of knowledge in programming languages like Python or R, and a profound grasp of data visualization techniques as data visualization plays an important role in it. Where is data stored? Data from different places like transaction systems, databases, and even social media or IoT devices are all stored in a data warehouse. Data warehouse:- It'...