Core MSSQL Training Course

What the specialist says

If your apps are slow or messy, it’s usually the database — this course fixes that thinking. This Core MSSQL Training Course helps you do that faster.

Keerthi Raj Database & SQL Mentor

Acquire Essential MSSQL Skills: Master Database Management, Earn Certification, Elevate Your IT Career! DevLustro Academy stands out as a leader in the database management domain, offering a comprehensive program that ensures participants efficiently manage, interpret, and utilize data like never before. Our program is designed to provide a deep understanding of MSSQL and its applications. Course was selected for our collection of top-rated courses trusted by businesses worldwide.

Our Core Highlights

World Class Instructor
World Class Instructor Mentorship from global experts
1:1 with Industry Expert
1:1 with Industry Expert Personalised coaching tailored to you
Global Hiring Network
Global Hiring Network 400+ hiring partners around the world
Average Salary Hike
Average Salary Hike 55% average hike for our alumni

Course Description

Acquire Essential MSSQL Skills: Master Database Management, Earn Certification, Elevate Your IT Career! Elysium Academy stands out as a leader in the database management domain, offering a comprehensive program that ensures participants efficiently manage, interpret, and utilize data like never before. Our program is designed to provide a deep understanding of MSSQL and its applications. Course was selected for our collection of top-rated courses trusted by businesses worldwide.

Core MSSQL Training Course

DB Management Course

Course Details

Harness powerful tools like SSIS for data integration and ETL processes.

Visualize data effectively using SSRS.

Explore backup and recovery techniques to safeguard your data

Database Management is the backbone that supports data-driven infrastructure.

Database Management encompasses the comprehensive study of data storage and retrieval systems.

Database Administrators understand database systems and derive valuable insights from data.

Database Management is the foundational layer upon which robust data solutions and applications are built.

Course Goals

  • Master MSSQL fundamentals, including database creation, queries, and data manipulation.
  • Dive into advanced concepts such as performance tuning and optimization.

Future Scope of this Course

  • Industry Based Projects
  • Recognized worldwide
  • Implement security measures and ensure compliance with industry standards.

Our Career Service

  • Personalized coordinator.
  • Trainer feedback.
  • Trainer availability post sessions.
  • Get your staff certified.
  • Certificate from governing bodies.

Our Training Program Benefits

  • Hands on assignment
  • Curriculum that focuses on the learner.
  • Live, interactive training by DevLustro experts.
  • Curriculum aligned with current industry practices.
  • Portfolio / project support for real-world use.
01Chapter-1 Getting Started MSSQL
  • 01.01What is MSSQL?
  • 01.02What is the difference between MSSQL and MySQL?
  • 01.03Purpose of MSSQL
  • 01.04Versions of SQL
  • 01.05Advantages and drawbacks
  • 01.06Installation Setup
  • 01.07What is a database?
  • 01.08What are tables?
  • 01.09Create, Alter and Drop Database
  • 01.10Create, Alter and Drop Table
  • 01.11Backup and Restore a Database
  • 01.12RDBMS
  • 01.13ER Model
  • 01.14Hands-on Create Database and Tables
  • 01.15Data Types
  • 01.16Constraints
  • 01.17Foreign Key Constraints
  • 01.18Unique and Check Constraint
  • 01.19Hands-on Data Types and Constraints
  • 01.20Quiz
  • 01.21What is Normalization?
  • 01.22First Normal Form
  • 01.23Second Normal Form
  • 01.24Third Normal Form
  • 01.25Practically Normalizing Tables
  • 01.26Hands-on Normalization
02Chapter-2 MSSQL Commands
  • 02.01What is DDL?
  • 02.02Purpose of DDL
  • 02.03CREATE Table
  • 02.04ALTER Table
  • 02.05TRUNCATE Table
  • 02.06RENAME
  • 02.07DROP
  • 02.08Hands on DDL
  • 02.09What is DML?
  • 02.10SELECT
  • 02.11UPDATE
  • 02.12DELETE
  • 02.13INSERT
  • 02.14Hands on DML
  • 02.15What is DCL?
  • 02.16Purpose of DCL
  • 02.17REVOKE
  • 02.18GRANT
  • 02.19Hands on DCL
  • 02.20WHERE
  • 02.21DISTINCT
  • 02.22ORDER BY
  • 02.23GROUP BY
  • 02.24HAVING
  • 02.25FROM
  • 02.26Hands on MSSQL clauses
03Chapter-3 Visualizations
  • 03.01Plotting with matplotlib
  • 03.02Bar charts
  • 03.03Pie charts
  • 03.04Scatter plots
  • 03.05Box plots
  • 03.06Histogram
  • 03.07Bubble Chart
  • 03.08Heat maps
  • 03.09Graph/line graph
  • 03.10Geographical data
  • 03.11Plotting with seaborn
  • 03.12Histogram with grid
  • 03.13Distplot
  • 03.14Pairplot
  • 03.15Scatter plots
  • 03.16Box plots
  • 03.17Lmplots
  • 03.18Histogram
  • 03.19Challenge: Visualize titanic dataset
  • 03.20Feature Extraction
  • 03.21Feature Selection
  • 03.22Pairplot
  • 03.23Scatter plots
  • 03.24Box plots
  • 03.25Lmplots
  • 03.26Histogram
  • 03.27Challenge: Visualize titanic dataset
04Chapter-4 Machine Learning
  • 04.01Al vs ML vs DL
  • 04.02Application of machine learning
  • 04.03How do machine learns
  • 04.04Types of Machine learning
  • 04.05Supervised Learning
  • 04.06Un-supervised Learning
  • 04.07What is Native Bayes?
  • 04.08Types of Naive Bayes
  • 04.09IRIS Classification
  • 04.10Data Processing
  • 04.11Train and create model
  • 04.12Performance Estimation
  • 04.13Analyse and create confusion matrix
  • 04.14Ordinary Least Square and Regression Errors
  • 04.15Data Processing
  • 04.16Train and Test Model
  • 04.17Test the model and Predict Y Values
  • 04.18R-Squared and its Importance
  • 04.19Score and Get coefficients
  • 04.20Calculate RMSE (Root Mean Squared Error)
  • 04.21Plot the predictions
  • 04.22Ordinary Least Square and Regression Errors
  • 04.23Data Processing
  • 04.24Train and Test Model
  • 04.25Test the model and Predict Y Values
  • 04.26R-Squared and its Importance
  • 04.27Score and Get coefficients
  • 04.28Calculate RMSE (Root Mean Squared Error)
  • 04.29Plot the predictions
  • 04.30Ordinary Least Square and Regression Errors
  • 04.31Data Processing
  • 04.32Train and Test Model
  • 04.33Test the model and Predict Y Values
  • 04.34R-Squared and its Importance
  • 04.35Score and Get coefficients
  • 04.36Calculate RMSE (Root Mean Squared Error)
  • 04.37Plot the predictions
  • 04.38What is SVM?
  • 04.39SVM kernel types
  • 04.40Problem: IRIS Classification
  • 04.41Data Processing
  • 04.42Train and create model
  • 04.43Performance Estimation
  • 04.44Analyse and create confusion matrix
05Chapter-5 Algorithms
  • 05.01What is Logistic Regression?
  • 05.02Problem: Heart Disease Prediction
  • 05.03Build Model
  • 05.04Performance Estimation
  • 05.05Analyse and create confusion matrix
  • 05.06What is decision tree?
  • 05.07Decision Tree Parameters
  • 05.08Problem: IRIS Classification
  • 05.09Data Processing
  • 05.10Train and create model
  • 05.11Evaluate Model
  • 05.12What is random forest?
  • 05.13Ensemble Learning
  • 05.14Bagging and Boosting Classifiers
  • 05.15Problem: Cardio Vascular Disease
  • 05.16lmplementation
  • 05.17Evaluate Model
  • 05.18Class
  • 05.19Objects
  • 05.20Constructors
  • 05.21Constructors
  • 05.22Encapsulation
  • 05.23Inheritance
  • 05.24Polymorphism
  • 05.25Super and this Keyword
  • 05.26Abstraction
  • 05.27Inheritance
  • 05.28What is KNN?
  • 05.29KNN parameters
  • 05.30Problem: Cardio Vascular Disease
  • 05.31Data collection and preprocessing
  • 05.32lmplementation
  • 05.33Evaluate Model
06Chapter-6 Evaluation Metrics
  • 06.01Evaluate Accuracy
  • 06.02Classification metrics
  • 06.03What is Threshold and PHAPTER Adjusting Thresholds
  • 06.04AUC ROC Curve
  • 06.05Why to reduce dimensions and Importance of PCA?
  • 06.06Steps to calculate PCA
  • 06.07Implementation of PCA
  • 06.08Visualization
  • 06.09What is Ridge regression?
  • 06.10Implement Ridge Regression
  • 06.11Plot Ridge Regression Line
  • 06.12Lasso Regression or L1 Penalty
  • 06.13lmplement lasso Regression
  • 06.14Plot lasso Regression Line
  • 06.15What is over fitting?
  • 06.16How to avoid over fitting?
  • 06.17What is under fitting?
  • 06.18How to avoid under fitting?
  • 06.19What is Cross Validation?
  • 06.20How Cross Validation Works
  • 06.21Prepare for Cross Validation
  • 06.22Parameter and implementation of Cross Validation
  • 06.23Understand the results of Cross Validation)
  • 06.24Hands On - Analyse the Result
07Chapter-7 Hypertunning Model and Clustering
  • 07.01What is Hyper parameter Tuning?
  • 07.02Grid Search and Randomized Search Approach
  • 07.03GridSearchCV Parameters Explained
  • 07.04Create GirdSearchCV Object
  • 07.05Fit data to GridSearchCV
  • 07.06Understand GridSearchCV Results
  • 07.07GridSearchCV using Logistic Regression
  • 07.08GridSearchCV using Support Vector
  • 07.09Randomized Search using random forest
  • 07.10Select Best Model
  • 07.11Randomized Search
  • 07.12Model Selection Summary
  • 07.13What is Clustering?
  • 07.14How the clusters are formed?
  • 07.15Problem Understanding: Customer Segmentation
  • 07.16Get, Visualize and Normalize the data
  • 07.17Import KMeans and Understand Parameters
  • 07.18Understanding KMeans++ Initialization Method
  • 07.19Create Clusters
  • 07.20Visualize and create different number of clusters
  • 07.21Understand Elbow Method to Decide number of Cluster
  • 07.22Implement Elbow Method
08Chapter-8 NLP and Recommendation System
  • 08.01What is NLP
  • 08.02Application of NLP
  • 08.03Remove punctuation
  • 08.04Tokenize
  • 08.05Remove stop words
  • 08.06Stem words
  • 08.07Lemmatize
  • 08.08Padding
  • 08.09Part of Speech Tagging
  • 08.10Name Entity Relationship
  • 08.11Sentiment Analysis
  • 08.12Read and preprocess data
  • 08.13NLP techniques
  • 08.14Tokenization
  • 08.15Tokens to vectors
  • 08.16Naive Bayes
  • 08.17Logistic Regression
  • 08.18Sentiment Analysis
  • 08.19Evaluate Metrics
  • 08.20What is Recommendation System?
  • 08.21How Do Recommendation Works?
  • 08.22Types of Recommendation
  • 08.23What is Content based recommendation?
  • 08.24Advantages and drawbacks
  • 08.25Hands on Content based recommendation code
  • 08.26What is Collaborative filtering?
  • 08.27Advantages and drawbacks
  • 08.28Kinds of collaborative filtering
  • 08.29Hands on collaborative filtering code
  • 08.30What is hybrid recommendation?
  • 08.31Advantages and Drawbacks
09Chapter-8 NLP and Recommendation System
  • 09.01What is NLP
  • 09.02Application of NLP
  • 09.03Remove punctuation
  • 09.04Tokenize
  • 09.05Remove stop words
  • 09.06Stem words
  • 09.07Lemmatize
  • 09.08Padding
  • 09.09Part of Speech Tagging
  • 09.10Name Entity Relationship
  • 09.11Sentiment Analysis
  • 09.12Read and preprocess data
  • 09.13NLP techniques
  • 09.14Tokenization
  • 09.15Tokens to vectors
  • 09.16Naive Bayes
  • 09.17Logistic Regression
  • 09.18Sentiment Analysis
  • 09.19Evaluate Metrics
  • 09.20What is Recommendation System?
  • 09.21How Do Recommendation Works?
  • 09.22Types of Recommendation
  • 09.23What is Content based recommendation?
  • 09.24Advantages and drawbacks
  • 09.25Hands on Content based recommendation code
  • 09.26What is Collaborative filtering?
  • 09.27Advantages and drawbacks
  • 09.28Kinds of collaborative filtering
  • 09.29Hands on collaborative filtering code
  • 09.30What is hybrid recommendation?
  • 09.31Advantages and Drawbacks
10Chapter-9 Deep Learning
  • 10.01What is Neuron and Artificial Neural Network?
  • 10.02How Artificial Neural Network works?
  • 10.03What is Keras and Tensorflow?
  • 10.04What is a Tensor in Tensorflow?
  • 10.05Installing Keras, backend and Tensorflow
  • 10.06Keras Model Building and Steps
  • 10.07Layers - Overview and Parameters
  • 10.08Activation Functions
  • 10.09Layers - Softmax Activation Function
  • 10.10What is a Loss Function?
  • 10.11Cross Entropy Loss Functions
  • 10.12Optimization - What is it?
  • 10.13Optimization - Gradient Descent
  • 10.14Initializers - Vanishing and Exploding Gradient Problem
  • 10.15Layers - Initializers explained
  • 10.16Problem Understanding: Disease Prediction
  • 10.17Read and process the data
  • 10.18Define the Keras Neural Network Model
  • 10.19Compile the Keras Neural Network Model
  • 10.20Evaluate the result
11Chapter-10 Projects
  • 11.01Problem understanding: Loan Approval Prediction
  • 11.02&nbspRead and preprocess data
  • 11.03Data splitting
  • 11.04Classification - Naïve Bayes
  • 11.05Classification – Support Vector Machine
  • 11.06Classification - Random Forest
  • 11.07Classification - Logistic Regression
  • 11.08Train the model
  • 11.09Evaluate performance metrics
  • 11.10Problem Understanding : Zomato Restaurant Review
  • 11.11Read and preprocess data
  • 11.12Data Splitting
  • 11.13Classification - Naïve Bayes (Multinomial)
  • 11.14Classification - Support Vector Machine
  • 11.15Classification - Random Forest
  • 11.16Classification – Decision Tree
  • 11.17Classification - Logistic Regression
  • 11.18Train the model
  • 11.19Evaluate performance metrics
  • 11.20Visualization using matplotlib and seaborn
  • 11.21Problem Understanding : Product Recommendation
  • 11.22Read and preprocess data
  • 11.23Collaborative filtering
  • 11.24Content based filtering
  • 11.25Recommend the product
  • 11.26Evaluate metrics

What is the Core MSSQL Course offered by DevLustro Academy?

The Core MSSQL Course at DevLustro Academy teaches you the fundamentals of SQL Server, including database design, query optimization, and data management. This course is ideal for those looking to specialize in database administration and development.

What makes DevLustro Academy the best Core MSSQL training center near me?

DevLustro Academy excels in Core MSSQL training due to its comprehensive curriculum, hands-on labs, and expert faculty who provide deep insights into SQL Server administration and development.

What is the duration of the Core MSSQL course?

The course spans 8 weeks, combining live sessions with self-paced learning materials.

Who is the course intended for?

This course is designed for IT professionals, database administrators, developers, and anyone looking to enhance their MSSQL skills.

What are the prerequisites for enrolling in this course?

Basic knowledge of databases and SQL is recommended, but the course is suitable for both beginners and experienced users.

What topics are covered in the course?

The course covers Database Creation, Advanced Query Techniques, Data Visualization, Performance Optimization, Data Security, and Backup and Recovery.

Will I receive a certificate upon completion?

Yes, participants will receive a certificate from DevLustro Academy, recognized by governing bodies and widely acknowledged in the industry.

Is there any hands-on training included?

Absolutely, the course includes hands-on assignments and industry-based projects to ensure practical understanding and application of MSSQL skills.

Are there any opportunities for personalized support?

Yes, you will have a personalized coordinator and access to trainer feedback, with trainers available for queries post-sessions.

How can this course benefit my career?

Mastering MSSQL can significantly enhance your database management capabilities, making you more valuable in your current role and opening up new career opportunities.

What if I miss a live session?

All live sessions are recorded and made available to participants, so you can catch up at your convenience.

Can I access the course materials after completion?

Yes, you will have lifetime access to all course materials, including recorded sessions, resources, and assignments.

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Core MSSQL Training Course

Duration: 45 Hours

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Core MSSQL Training Course

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