Data Science is an emerging technology which is making its way to several sectors of the IT industry. It’s a future-proof technology which helps you increase your market value and hired at a really handsome salary. SDLC Training provides **best Data Science Training in Marathahalli, Bangalore** with 100% Placement Assistance.

Data Science also known as data-driven science helps you to create models, methodologies, and algorithms that provide practical utility. There are many MNCs and startups who are still based on traditional data research techniques. But now they are realising that there should be some better way to optimize the process. Data science is the practice of asking questions and finding solutions to unknown problems which in turn motivate business values.

## Best Data Science Training in Marathahalli, Bangalore

SDLC Training is well equipped with advanced labs and offers the best learning environment with experienced professional trainers. 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 of Data Science. If you too want to make yourself competent enough to join such companies, it would be advisable that you take our **best Data Science Training in Bangalore**.

### Why Data Science?

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 a lot of people to take Data Science Courses in Bangalore quite enthusiastically. We, at SDLC, strives to provide industry standard best Data Science Training in Marathahalli.

### DATA SCIENCE TRAINER PROFILE

- More than 10 Years of experience in Data Science Training
- Has worked on multiple real-time Data Science Training
- Working in a top MNC company in Bangalore
- Trained 500+ Students so far in Data Science Training.
- Strong Theoretical & Practical Knowledge
- Certified Professionals

### Course Content

**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 to 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 to 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, a 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
- The 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
- The 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
- The 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

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Our trainers always start teaching with fundamentals and then move to advance stuff. In this way, Students have a clear understanding of every concept. Our **best data science training in Bangalore** will also introduce you with other future-oriented emerging technologies.. So, If you are someone who looks for quality training rather than anything else, Book a **FREE DEMO CLASS** Now.

For any other query or feedback, please feel free to contact us.

**Contact Info:**

**email: hr@sdlctraining.in**

**Phone no: +91 84948 40567**