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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

Top 5 University Courses to Learn Databases and SQL

Yana Khare
Yana Khare Last Updated : 19 Mar, 2024
4 min read

Introduction

Proficiency with databases and SQL is essential for many job choices in today’s tech-driven world, from ambitious software engineers to fledgling data professionals. In this post, we discuss the importance of developing these abilities and examine some of the best college courses that provide an in-depth understanding of databases and SQL. These courses offer priceless insights and valuable skills for navigating the constantly changing field of data management and analysis, from comprehending the foundations of databases to learning SQL syntax and advanced methodologies. Enrolling in these courses will open doors to a world of database and SQL options, whether your goals are to expand your skill set, improve your career chances, or acquire a competitive advantage in the employment market.

Table of contents

What is a Database?

Top 5 University Courses to Learn Databases and SQL

A database is an electronically stored collection of structured data in an ordered manner within a computer system. Its effective management, retrieval, and manipulation of data by preset criteria is its design. Databases act as repositories for storing, organizing, and retrieving enormous volumes of data, making them crucial parts of contemporary information systems. They offer an organized method for organizing data into tables that may be connected by shared characteristics, making data retrieval and analysis easier.

Also Read: What is Database and Why Do We Need It?

What is SQL?

Top 5 University Courses to Learn Databases and SQL

A specific computer language called SQL, or Structured Query Language, is used to interact with and work with databases. It offers a uniform syntax for maintaining, updating, and querying data in relational database management systems (RDBMS). SQL enables users to work with databases in various ways, such as getting specific subsets of data, adding new records, changing current data, and removing unnecessary data. Database administrators, software developers, data analysts, and other data management and analysis experts utilize it extensively.

Learn More: Top 5 Data Analytics Certifications

Importance of Doing Courses to Learn Databases and SQL

To grasp the significance of database and SQL courses in technology-oriented modern world, it is important to understand that they are very critical. Here are some specifics:

  1. Relevance to Career: Databases and SQL are part of proficiency in different fields and are widely applicable. Software development, data analysis, and data management all require these skills as basics for effective data handling and analysis.
  2. Promotion of a professional career: Many employers today look for individuals who have specialist knowledge in databases and SQL. These classes will add value to your CV, making you more competitive in the labor market and gaining access to better job prospects.
  3. Data Handling: In an age when information is easily accessible and valuable, one must know how to handle it efficiently. Databases offer an organized platform for keeping and managing any form of information while SQL makes retrieval and manipulation possible effortlessly.
  4. Learning Database Skills Is Essential For Problem Solving: Learning SQL allows one to acquire knowledge on the use of databases and develop problem-solving techniques. Designing databases, writing complex queries that derive meaningful insights from big data, and optimizing databases for performance are some examples of such skills that can be used by someone working in a technical position.
  5. Job Need: Businesses need more people who know about databases and SQL. Many types of companies use databases to save and control their information. If you learn these things, you can become helpful in filling this need.
  6. Changeability: Databases and SQL are building block systems for many other IT tools and systems. If you learn these basics well, you can easily adjust to new tools and changes in the fast-growing tech area.

Top 5 University Courses to Learn Databases and SQL

Here are the top 5 courses to learn Database and SQL:

1. Databases: Relational Databases and SQL

  • Offered By: Stanford University
  • Suitability: Suitable for beginners and intermediate learners
  • Cost: Free (Audit) or USD 79 (Certificate)
  • Skills Covered: Relational database concepts, SQL fundamentals, database design
  • Time to Complete: Self-paced, approximately 6-8 weeks

2. Introduction to Databases

  • Offered By: Cornell University
  • Suitability: Ideal for beginners with no prior database experience
  • Cost: Free (Audit) or USD 49 (Certificate)
  • Skills Covered: Database modeling, SQL queries, database management systems (DBMS)
  • Time to Complete: Self-paced, approximately 4-6 weeks

3. Introduction to Databases with SQL

  • Offered By: Harvard University
  • Suitability: Suitable for beginners and those familiar with basic programming concepts
  • Cost: Free (Audit) or USD 99 (Certificate)
  • Skills Covered: SQL syntax, database design principles, data manipulation
  • Time to Complete: Self-paced, approximately 8-10 weeks

4. Intro to Database Systems

  • Offered By: Carnegie Mellon University
  • Suitability: Suitable for beginners and intermediate learners
  • Cost: Free (Audit) or USD 49 (Certificate)
  • Skills Covered: Database architecture, relational database management systems (RDBMS), SQL queries
  • Time to Complete: Self-paced, approximately 4-6 weeks

5. Advanced Database Systems

  • Offered By: Carnegie Mellon University
  • Suitability: Geared towards intermediate to advanced learners with prior database knowledge
  • Cost: Free (Audit) or USD 49 (Certificate)
  • Skills Covered: Advanced SQL techniques, database optimization, transaction management
  • Time to Complete: Self-paced, approximately 6-8 weeks

Conclusion

In today’s technologically advanced world, becoming proficient in databases and SQL is imperative. It improves professional skill sets and provides a plethora of job options. Whether you’re a novice trying to establish a solid foundation or a seasoned professional trying to sharpen your skills, these courses provide thorough understanding and useful insights necessary for success in the ever-evolving data management and analysis field.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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