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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A beginner’s Guide to Database: Part 1

Amruta
Amruta Last Updated : 07 Jul, 2021
5 min read

This article was published as a part of the Data Science Blogathon

Pre-requisites

A Basic understanding of Databases.

Introduction

Here I am going to discuss with you that how databases are widely used in industry, what are they, and how it helps store and retrieve data.

Basically, DBMS (Data Base Management System)  is an efficient system or software by which we manage databases like MS Access, Oracle, SQL, and so on. We will look briefly at all these concepts.

image 1 Database
Source: https://www.bloggersinsights.com/images/embed_image/hr7v9dbms.jpg

Agenda

1. What is the database?

2. What is a Database management system?

3. Database Models

4. The Advantages and Disadvantages of DBMS(Database Management System)

5. Conclusion


What is the Database?

It is a collection of well-organized related records incoherent manner. These records are stored in the form of a table. As we know there rows and columns. Rows are known as tuples and columns are known as attributes of the database.

Example:- Employee records, Students records, telephone Directory, Inventory control, Patients records, Sales reports, etc.

what is Database
Image Source: https://exceltable.com/en/database-xml/images/database-xml1-1.png

The Database stores metadata in an area which is called the data dictionary, which represents the tables, columns, indexes, constraints, and other items that creates the database.

Metadata is the data that describes the structure of data within a database. If you know how your data is arranged, then you easily can retrieve it. Because the database consists of a description of its structure, it’s “self-describing”. The database is integrated because it includes not only data items but also the relationships between data items.

What is Database Management System(DBMS)?

A database management system (called DBMS) is a set of programs that are used to define, administer, and process databases and their associated applications. The database is being managed in essence, a structure that you build to hold valuable data.

We can say a DBMS is the tool you use to build that structure and operate on the data contained within the database. You can find many DBMS programs in the market today. Some DBMS programs will run on large and powerful machines, some on personal computers, notebooks, tablets, etc.
Some programs even run on smartphones.

This is for such products to work on multiple platforms or we can say on networks that contain various classes of machines. An even stronger trend is that to store data in data centers or even to store it out in the cloud also, which could be a public cloud run by a large company such as
Amazon, Google, or Microsoft, through the Internet, or it could be a private cloud that is operated by the same organization that is storing the data on its own intranet.

A DBMS that runs on platforms of multiple classes, large and small that is called Scalable.

Database DBMS
Image Source:-  https://blog.udemy.com/wp-content/uploads/2014/02/shutterstock_132463097.jpg

Database Models

Database Model gives us a brief idea of our final system after its complete implementation. It defines the data elements and the relationships among the data elements. Database Models are used to show how data is stored, how it is connected, how it can be updated, and how to access the database management system that is nothing but its own structure.

Here, we are going to use a set of symbols and text to represent the information so that members of the organization can communicate and understand it very well. Although many database models are being used nowadays the Relational Model is the most widely used.

Apart from the Relational models, there are various other types of database models about which we will study in detail in this blog. Some of the Database Models in DBMS are as below:

1) Entity-Relationship Model:

The Entity-Relationship model (or we can say ER model) graphically shows the logical relationships of entities (or say objects) to create a database. It is also beneficial for the developers to understand the overall system by just looking at the “ER diagram”.

– Entity:- An entity is a real-world object, which is distinguishable from different objects. Like it can be a place, concept, or thing.

Example:-
Enrollment=000968347, Teacher, Building, etc

– Entity Set:-
Collection of entity is called entity set.

Example:- Employee Records table, Students Records table.

– Relationship (Mapping):- The Relations between attributes of any two entity sets are called Mapping.
There are four types of relationships as follow:-

1. 1:1(One to One) Mapping

2. 1: M (One to Many) Mapping

3. M: 1 (Many toOne) Mapping

4. M: N (Many to Many) Mapping

For Example:-

Entity-Relationship Model database
Image source:- https://i.stack.imgur.com/oQCZe.png

2) Network Model

The network model is a part/extension of the hierarchical model that we will see below. The network Model was the most famous model before the Relational model. The network model is the same as the hierarchical model, there is the only difference is that a “record can have more than one parent”. It can replace the Hierarchical tree with a Graph.

For Example: In the example below, we can see that the node student has two parents i.e. Dept and Library of college. Hence It is not possible in the hierarchical model.

Network Model network model
Image Source: https://sqlversity.files.wordpress.com/2013/03/network-dbms1.png

3) Hierarchical Model

The hierarchical model was the first “DBMS model”. This model arranges the data in the hierarchical tree structure. Basically, the hierarchy starts from the root which has root data, and then it expands in the form of a tree adding child node to the parent node and so on. The hierarchical model easily describes some real-world relationships like Food Recipes, etc.

For Example:- We can represent the relationship between the Employees that represent different types of employees in the Company in the following way:-

Hierarchical Model
Image Source: https://cdn.briefmenow.org/wp-content/uploads/cisa-v5-part1/image25.jpeg

Advantages And Disadvantages of Database Management Systems (DBMS)

 

* Advantages of DBMS are:- 

1. DBMS has Centralized Control.

2. Data independence allows the dynamic changes and growth potential.

3. Data quality is enhanced.

4. Security enforcement of DBMS is possible.

* Disadvantages of DBMS are:- 

1. Problems associated with centralization are one of the disadvantages.

2. Cost of Software/Hardware required for database and migration too.

Conclusion

It was all about the basics of the Database, we will learn all advanced concepts in the upcoming article. I hope you liked my article. Do share with your friends. Thank You!

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
Amruta
Amruta

I am Software Engineer, data enthusiast , passionate about data and its potential to drive insights, solve problems and also seeking to learn more about machine learning, artificial intelligence fields.

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