New Batches Started at Sgraph Infotech!!! Data Science (8AM to 9AM), Pega (8AM to 9AM), MSBI (10AM to 11AM), Power BI (9AM to 10AM), Salesforce (6PM to 7 PM), RPA-Automation Anywhere (7AM to 8AM), Oracle DBA 12C (6:30 PM to 7:30 PM), Selenium (7PM to 8PM). Contact Us right now: For more information Call: +91-8049570931 / sgraphinfotech@gmail.com.

Data Science Classes in Bangalore

The Sgraph is known as the best data science institutes in Bangalore designs curricula for data science training and data science education across the Indian Territory. Sgraph establishes a virtuous learning production cycle whereby we have a) analyze the required sector specific skillsets for data scientists across the main industrial sectors in India b) develop modular and adaptable data science curricula to meet industry expectations and c) deliver data science training supported by multi-platform and multilingual learning resources. The curricula and learning resources are continuously evaluated by pedagogical and data science experts during both development and deployment. The sgraph Institute has adapt the best learning pattern which helps the student in getting more knowledge and research on the most latest data and get the knowledge of the latest trends which improves their skill and make them always ahead. Sgraph offers the best knowledge in data science courses training in Bangalore. They make you to analyze the sector specific skillsets for data analysts across Indian main industrial sectors, Develop modular and adaptable curricula to meet these Data Science needs and Deliver training supported by multiplatform and multilingual learning resources based on this curriculum.

Throughout the project, the curricula and learning resources will be guided and evaluated by experts in both Data Science and pedagogy to ensure they meet the needs of the Data Science community. The sections below outline some of the activities that are being carried out to help meet these goals. Sgraph is developing a core Data Science curriculum based on topics extracted from the demand analysis. The first version of this curriculum is available here. We are producing high-quality, multilingual and multimodal training materials to cover these topics, utilizing existing resources available in the public domain and the internal expertise of the Sgraph consortium. Our main aim is to make sure that whoever joins with sgraph gets best learning experience with all modern technologies and achieve the higher growth in the arena of the field they want to be. So if you are also planning to learn make your career in data science with sgraph just get in touch with us and start to fly out on top. Fill out the form or contact us so we can explain you in more details.

The Sgraph is known as the best data science institutes in Bangalore designs curricula for data science training and data science education across the Indian Territory. Sgraph establishes a virtuous learning production cycle whereby we have a) analyze the required sector specific skillsets for data scientists across the main industrial sectors in India b) develop modular and adaptable data science curricula to meet industry expectations and c) deliver data science training supported by multi-platform and multilingual learning resources.

How will I do the Lab Practice?

We have technically updated lab to provide you the best hands-on project experience. Our instructors provides you the best support if get stuck anywhere while practicing. We will provide you the cloud lab that gives access to your PC or Laptop. Hence, you can access our server from anywhere.

Who are the instructors?

Our instructors are the best industry experts with domain knowledge having 7+ years of experience in Data Science training in Bangalore.

What if I miss a class?

We will provide you the backup classes if you miss any session. You can continue the missed classes from next batch.

How can I request for a demo class?

You can either walk-in to our Data Science training institute in Marathahalli, or you can send your queries to us from the website and then we can arrange the Data Science training demo session for you.

What are the payment options?

You can pay directly or can transfer online. We also accept credit/ debit cards.

Will I get the required software from institute?

Definitely you can get or access the software from our server or we can provide the required software to you depending on the course.

Is there any offer or discount I can avail?

Yes, you can find the best offers and discounts which vary with time; you can check with us.

Data Science Training Course Content

Datascience Curriculum

Python :

Goal – Get an overview of the python which is required to work on data science

Objectives - At the end of this Module, you should be able understand the following topics

  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Importing packages
  • If else
  • Loops
  • Comprehensions
  • Functions
  • Map
  • Filter
  • Reduce
  • Numpy
  • Pandas
  • Merging,querying,aggregating
  • Assignments for practice

R :

Goal – Get an overview of the R which is required to work on data science

Objectives - At the end of this Module, you should be able understand the following topics

  • Introduction
  • Basic operations in R
  • Vectors
  • Factors
  • Matrices
  • Data frames
  • Lists
  • Logical and Relational operators
  • Conditional Statements
  • Loops
  • Functions
  • Apply Family

Introduction

  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing R and R Studio (MAC & Windows)
  • Installing Python and Anaconda (MAC & Windows)

 -------------------------- Part  Data Preprocessing --------------------------

  • Welcome to Part  - Data Preprocessing
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • And here is our Data Preprocessing Template!
  • Quiz  Data Preprocessing

------------------------------ Part  Regression ------------------------------

  • Welcome to Part  - Regression

 Simple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Simple Linear Regression Intuition -   
  • Simple Linear Regression in Python -  
  • Simple Linear Regression in R -  
  • Quiz  Simple Linear Regression

Multiple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition -   
  • Multiple Linear Regression in Python -   
  • Multiple Linear Regression in Python - Backward Elimination - Preparation
  • Multiple Linear Regression in Python - Backward Elimination -  !
  • Multiple Linear Regression in Python - Backward Elimination -  Solution
  • Multiple Linear Regression in R -   
  • Multiple Linear Regression in R - Backward Elimination -  !
  • Multiple Linear Regression in R - Backward Elimination -  Solution
  • Quiz  Multiple Linear Regression

Polynomial Regression

  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python -  
  • Python Regression Template
  • Polynomial Regression in R -   
  • R Regression Template

Support Vector Regression (SVR)

  • How to get the dataset
  • SVR in Python
  • SVR in R

Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R

 Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R

 Evaluating Regression Models Performance

  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance - 's Final Part
  • Interpreting Linear Regression Coefficients
  • Conclusion of Part  - Regression

 ---------------------------- Part  Classification ----------------------------

  • Welcome to Part  - Classification

 Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in Python -  
  • Python Classification Template
  • Logistic Regression in R -   
  • R Classification Template
  • Quiz  Logistic Regression

 K-Nearest Neighbors (K-NN)

  • K-Nearest Neighbor Intuition
  • How to get the dataset
  • K-NN in Python
  • K-NN in R
  • Quiz  K-Nearest Neighbor

 Support Vector Machine (SVM)

  • SVM Intuition
  • How to get the dataset
  • SVM in Python
  • SVM in R
    • SVMzip

Kernel SVM

  • Kernel SVM Intuition
  • Mapping to a higher dimension
  • The Kernel Trick
  • Types of Kernel Functions
  • How to get the dataset
  • Kernel SVM in Python
  • Kernel SVM in R

Naive Bayes

  • Bayes Theorem
  • Naive Bayes Intuition
  • Naive Bayes Intuition (Challenge Reveal)
  • Naive Bayes Intuition (Extras)
  • How to get the dataset
  • Naive Bayes in Python
  • Naive Bayes in R

 Decision Tree Classification

  • Decision Tree Classification Intuition
  • How to get the dataset
  • Decision Tree Classification in Python
  • Decision Tree Classification in R

 Random Forest Classification

  • Random Forest Classification Intuition
  • How to get the dataset
  • Random Forest Classification in Python
  • Random Forest Classification in R

 Evaluating Classification Models Performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy Paradox
  • CAP Curve
  • CAP Curve Analysis
  • Conclusion of Part  - Classification

 ---------------------------- Part  Clustering ----------------------------

  • Welcome to Part  - Clustering

 K-Means Clustering

  • K-Means Clustering Intuition
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in Python
  • K-Means Clustering in R
  • Quiz  K-Means Clustering

Hierarchical Clustering

  • Hierarchical Clustering Intuition
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • How to get the dataset
  • HC in Python -   
  • HC in R -   
  • Quiz  Hierarchical Clustering
  • Conclusion of Part  - Clustering

 ---------------------- Part  Association Rule Learning ----------------------

  • Welcome to Part  - Association Rule Learning

 Apriori

  • Apriori Intuition
  • How to get the dataset
  • Apriori in R -   
  • Apriori in Python -  

Eclat

  • Eclat Intuition
  • How to get the dataset
  • Eclat in R
    • Eclatzip

 ------------------------ Part  Reinforcement Learning ------------------------

  • Welcome to Part  - Reinforcement Learning

 

Upper Confidence Bound (UCB)

  • The Multi-Armed Bandit Problem
  • Upper Confidence Bound (UCB) Intuition
  • How to get the dataset
  • Upper Confidence Bound in Python -   
  • Upper Confidence Bound in R -  

Thompson Sampling

  • Thompson Sampling Intuition
  • Algorithm Comparison UCB vs Thompson Sampling
  • How to get the dataset
  • Thompson Sampling in Python -  
  • Thompson Sampling in Python -  
  • Thompson Sampling in R -  
  • Thompson Sampling in R -  

 --------------------- Part  Natural Language Processing ---------------------

  • Welcome to Part  - Natural Language Processing
  • How to get the dataset
  • Natural Language Processing in Python -   
  • Challenge
  • Natural Language Processing in R -  
  • Natural Language Processing in R -  
  • Challenge

 ---------------------------- Part  Deep Learning ----------------------------

  • Welcome to Part  - Deep Learning
  • What is Deep Learning?

 Artificial Neural Networks

  • Plan of attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • How to get the dataset
  • Business Problem Description
  • ANN in Python -   - Installing Theano, Tensorflow and Keras
  • ANN in R -   
  • ANN in R -   (Last )

 Convolutional Neural Networks

  • Plan of attack
  • What are convolutional neural networks?
  •  - Convolution Operation
  • (b) - ReLU Layer
  •  - Pooling
  •  - Flattening
  •  - Full Connection
  • Summary
  • Softmax & Cross-Entropy
  • How to get the dataset
  • CNN in Python -  
  • CNN in R

 ----------------------- Part  Dimensionality Reduction -----------------------

  • Welcome to Part  - Dimensionality Reduction

 Principal Component Analysis (PCA)

  • How to get the dataset
  • PCA in Python -   
  • PCA in R -  

Linear Discriminant Analysis (LDA)

  • How to get the dataset
  • LDA in Python
  • LDA in R

 Kernel PCA

  • How to get the dataset
  • Kernel PCA in Python
  • Kernel PCA in R

 --------------------- Part  Model Selection & Boosting ---------------------

  • Welcome to Part  - Model Selection & Boosting

Model Selection

  • How to get the dataset
  • k-Fold Cross Validation in Python
  • k-Fold Cross Validation in R
  • Grid Search in Python -   
  • Grid Search in R

XGBoost

  • How to get the dataset
  • XGBoost in Python -   
  • XGBoost in R

Book Your Course

Sgraph Infotech
MSBI | DOT NET | AWS | Data Science | Python
Address :
3rd Floor, JP Royale- 90/4, Above ICICI Bank, Outer Ring Road, Opposite Radisson Blu, Marathahalli,
Bengaluru,
Karnataka - 560037
India.
Tel : 9620885025
Email : sgraphinfotech@gmail.com

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