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**Best Data Science Training in Bangalore**

Data Science also known as data – driven science helps you to create models, methodologies, and algorithms that provide practical utility. Data science is also the practice of asking questions and finding solutions to unknown problems which in turn motivate business values. This process involves working with a set of existing data or defining the data all by you.

Data scientist performs research and analysis on data and helps companies to improve their businesses by predicting growth, business insights and trends based on big data. Successful data scientists are very much in demand in today’s world and Data Scientist occupation was rated as the No. 1 job in “Best jobs In America for the year 2016”. This has motivated lot of people to take Data Science Courses in Bangalore quite enthusiastically.

**Why Data Science at SDLC**

With the help of Data Science, One can achieve a person-level customization in almost any kind of services like healthcare, insurance, public services, banking, etc. This is not just reliable but also helps companies save on a lot of money. And that is probably the reason why they remain on a constant lookout for people having knowledge in Data Science. If you too want to make yourself competent enough to join such companies, it would be advisable that you take Data Science Training in Bangalore.

The good news is that you don’t need any special educational qualification to be eligible for Data Science coaching classes. So, why waste time? Join SDLC Training Institute today.

**Trainers Profile and Placement**

- More than 10 Years of experience in Data Science Training
- Has worked on multiple realtime Data Science Training
- Working in a top MNC company in Bangalore
- Trained 2000+ Students so far in Data Science Training.
- Strong Theoretical & Practical Knowledge
- Certified Professionals

**Our Training Process**

**Who should join this Course**

People who want to make a career in Data Science or just want to upgrade your skills by learning Data Science should definitely join this course. Either you are a student or an IT professional or someone who is looking for a job, Our best Data Science Training in Bangalore will not just fit in your budget but will also convert you into a professional Data Science developer/programmer.

**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

Class Time : Flexible Class Timings |
Demo Class : Free Demo Sessions are available for quality purposes |

Regular : 1 Hour per day |
Fast Track : 1 ½ to 2 Hours per Day: Based on Student Expectation |

Weekdays : Available |
Weekend : Available |

Online Training : Available |
Class Room Training : Available |

Course Fee : Talk to our Customer Support |
Duration : 60 Hours |

**How will I do the Lab Practice?**

We have the technically updated lab to give you the best hands-on project experience.

**Who are the instructors?**

Our instructors were the best industry and domain knowledge professionals with 5+ 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 SDLC training institute in Marathahalli, or you can send the query to us from the website then we can arrange the Data Science training demo session for you.

**What are the payment options?**

You can pay directory or you can transfer the money online. We also accept 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 are vary time to time you can check with us.

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## About Us

SDLC Training is an IT training institute that offers tailor-made courses to students and corporates who intend to hone their IT Skills. Our training portfolio covers programs that span across the Software Development Life Cycle, and hence the name.

## Contact Us

#354, 3rd Floor, Aswath Nagar Main Road,

Near Kanti Sweets, Marathahalli,

Bengaluru, Karnataka - 560037

hr@sdlctraining.in

+91 84948 40567

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