Thursday, February 21, 2019

Data Science Training Course

Data Science Training-SDLC Training
Data Science Training

4.5 out of 5
based on 1049 ratings.

Best Data Science Training in Bangalore

Data Science CourseData Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, the activity of cleansing, preparing and aligning the data.

In SDLC learn basics to advance of Data Science. Our trainers are experienced working professionals. Get job with our free placement assistance program. We are providing the online, classroom and corporate training.

How we will start the course?

  • Learn from basics
  • Practice coding
  • Set your algorithm carefully
  • Trace your codes on paper
  • Read sources on Data Science regularly

At end of the course?

  • Trainees  will understand the core concepts of Data Science.
  • Participants  will have an understanding of how to create and implement algorithms.
  • Candidates  will have detailed knowledge about Data Science.
  • Real time project experience.

Contact Us


divider
Demo Class : Free Demo Session, Flexible TimingsFree Class : Attend 3 Free Classes to check training Quality
Regular : 1 Hour per dayFast Track : 2 – 3 Hours per day: 10 days
Weekdays : AvailableWeekend : Available
Online Training : AvailableClass Room Training : Available
Course Fee : Talk to our Customer SupportDuration : 30 Hours

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
What is the batch size?

SDLC training providing the limited batch size, so we can provide quality teaching. If you want to get trained individually, we are also providing.

How you people will help for the Job?

SDLC training providing the 100% job assistance and mock interviews.

How you people will help in the projects?

SDLC training is providing the training with live projects and real-time practice.

How you people will provide the doubt clarification?

SDLC training providing the 24/7 interact access with faculties and after course also engagement between the faculties and students.

What are the extra services?

SDLC training providing the back up classes, soft skill training, interview skills workshop and resume preparation assistance.

How you people will help to enhance the students knowledge?

SDLC training providing the topics wise ppts, case studies, assignments and doubt solving.

divider

Steps To Build A Successful Career at SDLC

Theory

Practical

Assignment

Hands-on live projects

Resume preparation

Mock interviews

Attend interview

Get job

divider

Google Reviews

  • review rating 5  I have a good experience with SDLC training , I was looking Sap support, I tried to search on Google so many vendor , but I didn’t proper response and details. Finally I found this, than I got my solution with quick service and proper details. In my opinion academy is doing good job, I appreciate it.

    thumb Allay Criyons
    2/16/2019

    review rating 5  I joined phyton course recently based on my experience trainer is professional and clear and gives pratical examples real time scenarios and also SDLC team very helpful in all the terms and am very happy with “SDLC” training.I would recommend everyone to join this institute.

    thumb Aishwarya G R
    2/14/2019
  • review rating 5  One of best institute for Fresher and experience Dotnet Training .I would highly recommend in SDLC Training every one to joined DOTNET MVC .

    thumb Kiranmayeekuruba Kiranmayeekuruba
    2/05/2019

    review rating 5  I completed my oracle RAC DBA training from SDLC. Institute is very good , they provide all type facility like practical lab. Trainer was Manab sir.He having 25 year experience.He having good knowledge. My overall experience is very good with SDLC. Thank you manab sir thank you sdlc.

    thumb Gaurav justdial1
    2/02/2019
  • review rating 5  Great place to learn python,RPA ,Data science ,Angular,Java ,Oracle SCM,Oracle DBA rac, SDLC Trainers are working professional in MNC, They have a Good communication skills They are providing Realtime project Practically , I joined PYTHON course in SDLC Telraining Marathahalli Branch Trainer took great pains to explain with Good example….

    thumb Akash das
    1/19/2019

    review rating 5  Very Good Training fir learning python D-Jango .ThecTrainer was professional and helped me understand the concepts very well .I would recommed SDLC TRAUNING For anyone Looking for Python with D-Jango Training .

    thumb Amit Patra
    1/09/2019
  • review rating 5  Very good institute for learning real time training. The trainers are working professional. I would recommend SDLC training for anyone looking for real time training with placement.

    thumb Bhagya R
    1/07/2019

    review rating 3  I joined SDLC for Java training. It’s a good place for getting trained in java. The trainer is very good in this center. He has very good knowledge in complete java. Santhosh sir also very good and interactive.

    thumb Shilpa Hegde
    1/05/2019
divider

Features of SDLC​

  • Limit the batch size so we can provide personal attention to everyone in the session
  • Real-time practice
  • Live projects
  • 24/7 interact access with faculties
  • Experienced and passionate trainers
  • After course engagement
  • We give topics wise ppt, case studies, assignments and doubt solving
  • 100% job assistance
  • 24/7 support
  • Classroom training, Online training and Corporate training.
  • Student can attend their missed classes
  • Soft skill training, interview skills workshop, resume preparation assistance
divider

All courses list

Visit Our Other Website: SDLC Training| Interview Questions | Maintained By Nilam Software Solution.