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What is Data Science

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Learn Data Science - Introduction Introduction to Data Science History The field of data science has its roots in statistics and computer science and has evolved to encompass a wide range of techniques and tools for understanding and making predictions from data. The history of data science can be traced back to the early days of statistics when researchers first began using data to make inferences and predictions about the world. In the 1960s and 1970s, the advent of computers and the development of new algorithms and statistical methods led to a growth in the use of data to answer scientific and business questions. The term "data science" was first coined in the early 1960s by John W. Tukey, a statistician and computer scientist . In recent years, the field of data science has exploded in popularity, thanks in part to the increasing availability of data from a wide range of sources, as well as advances in computational power and machine learning. Today, data science is us...
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What is Data Exploration and Visualization

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Learn  Data Exploration Techniques and  Data Visualization Tools Content of  Data Exploration and  Data Visualization : Data Exploration Techniques Descriptive Statistics Data Visualization Tools Exploratory Data Analysis Data Exploration Techniques Data exploration techniques are used to gain an understanding of the data and its characteristics. Some common data exploration techniques include:      Summary Statistics :  This involves calculating summary statistics such as mean, median, mode, variance, standard deviation, etc. These statistics provide a basic understanding of the data's central tendency, spread, and distribution.      Histograms :  Histograms are used to visualize the distribution of a numerical variable. They show the number of data points that fall into specific intervals or bins.      Box Plots :  Box plots show the distribution of a numerical variable an...
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What is Data Preparation and Feature Engineering

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Data Preprocessing and  Feature Selection Techniques Contentsn of  Data Preprocessing Techniques Data Preprocessing Techniques Feature Engineering Techniques Feature Selection Techniques Dimensionality Reduction Techniques Data Preparation and Feature Engineering are crucial steps in the machine learning pipeline. In this step, we prepare and preprocess the raw data to make it suitable for machine learning algorithms. The act of turning unprocessed data into features that may be used in machine learning algorithms is known as feature engineering. Feature selection and dimensionality reduction are also part of feature engineering, where we select the most relevant features and reduce the dimensionality of the data to improve the model's performance. Data Preprocessing Techniques: Data preprocessing is the process of cleaning, transforming, and preparing raw data for machine learning algorithms. The following are some common data preprocessing techniqu...
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