Data Dive Daily

  Key concepts in Data Science Understanding the key concepts of Data Science is crucial for anyone who wants to work with data. In this chapter we can delve into some key concepts like statistics, probability and Machine Learning. In this part of the chapter we will focus about the Statistics . Statistics Statistics is fundamental to Data Science. We can't use our data without involving statistics. It offers various help to analyze, interpret and present the data. Statistics helps us to derive informative decisions based on the data we have. Important concepts in Statistics There are two types of statistics, 1. Descriptive statistics and 2. Inferential statistics . 1. Descriptive statistics - In this type of statistics the collected data are described in a summarized way, which helps to describe the main features of the data. This descriptive statistics include Measures of Central Tendency and Measures of Dispersion .  Measures of Central Tendency are the mean, median and ...

  Data Science Lifecycle: The Data Science Lifecycle has the whole process of drawing insights from the data. Understanding each and every part of this lifecycle is important to attain expertise in the Data Science. The stages of this lifecycle include: 1. Data Collection: This involves collecting data from various sources like databases, APIs, social media, surveys, public data repositories, internal data sources and web scraping. 2. Data Cleaning : This process involves cleaning the data which we have collected. It can be anything like eliminating duplicates, removing nulls, removing inconsistencies, finding outliers filling up the empty data with averages or the same neighboring values just to make sure that we have a high quality data. 3. Data Exploration : The Data Exploration involves analyzing data with various visualizations like bar chart, line chart, heat maps to get an idea about the data we have. Visualizations and Statistical analysis helps to understand our data...

  Data Science Data Science is a field that involves using various algorithms, techniques and processes to draw valuable insights from data which can be structured or unstructured. To get the best out of the data we combine the  statistics, computer science, mathematics and the domain knowledge which helps us to analyze and interpret the data better. What is Data? Data is a collection of raw facts, figures and information about anything which can be used to analyze, draw insights and make decisions. Types of Data Data can be structured, unstructured or semi-structured. Structured data The data which are organized and has a standardized form like a table with rows and columns are called the structured data. For example, data from databases and spreadsheets. Structured data are easy to use, analyze and sort. Unstructured data  The data which are not organized and doesn't have a proper structure are called the unstructured data. For example, media like images and videos, tex...