We have built careers of thousands of Data Science professionals with 100% job assistance in various MNCs. “Training to Job Placement” – is our vision.

3 Months - Intensive Training By IITN's

Free Workshop & Demo : 2019-11-03 (Sun) @ 10:00 AM

120

+

hr

Lecture

3

Real Time Projects

100

+

hrs

Coding Assignments

10

+

Industry

Case Studies

22,000/-

- Duration : 12 weeks
- 8Hrs / week
- Next Batch Starts at
- 2019-11-03

25,000/-

- Duration : 10 weeks
- Avg 10 Hrs / week
- Next Batch Starts at
- 2019-11-03

25,000/-

- Duration : 2 weeks
- 16Hrs / week
- Next Batch Starts at
- 2019-11-03

22,000/-

- Duration : 10 weeks
- Avg 2 Hrs / day
- Next Batch Starts at

22,000/-

- Duration : 12 weeks
- Avg 8 Hrs / week
- Next Batch Starts at

22,000/-

- Please Book your slot

for one to one training

We will Help you to Build your resume efficiantly by Industry experts This will make you to land in your desired Job

Dealing with real time projects which will makes you ready as experianced data scientist

Helps candiates to know about their skills on Perticular subject and getting interview tips

Start your work with your desired job through placements that we offer to you

Nov

Class Room

22,000/-

Limited Seats Available

Oct

Online

22,000/-

5 seats Left

Nov

Class Room

22,000/-

Limited Seats Available

Online

22,000/-

5 seats Left

Introduction to R

Overview of R, R data types and object

Reading and writing data

control structures, functions, scoping rules

Introduction to Python

Use of Python in Data Science

Where to use python in real time

Python Environment setup, operators

Making Decisions & Loop Control

Simple if Statement, if-else Statement

if-elsif Statement.

Introduction To while Loops.

Introduction To for Loops, Using continue and break

Python Data Types

List, Tuples, Dictionaries

Python Lists, Tuples, Dictionaries

Accessing Values

Basic Operations

Indexing, Slicing

Built-in Functions & Methods

Defining Functions

Calling Functions

Anonymous Functions – Lambda

Python ML Packages

Numpy

Pandas

Matplot lib

Scikit learn

Exploratory Data Analysis

Numerical Analylsis

Categorical Analysis

Visualizing Data: Box, Scatter, Bar & Histogram

Introduction To Machine Learning

What is Machine Learning?

What is the Challenge?

Introduction to Supervised Learning,Unsupervised Learning.

Linear Regression

Introduction to Linear Regression

Linear Regression with Multiple Variables

Disadvantage of Linear Models

Interpretation of Model Outputs

Understanding assumptions of linear regression

Descriptive Statistics

Describe or sumarise a set of data

The mean,median,mode, Kurtosis and skewness

Computing Standard deviation and Variance

Covariance, Correlation and Causation

Logistic Regression

Introduction to Logistic Regression.– Why Logistic Regression

Introduce the notion of classification.

Cost function for logistic regression

Confusion Matrix, Odd’s Ratio And ROC Curve

Advantages And Disadvantages of Logistic Regression.

Decision Trees

How to build decision tree?

Understanding Kart Model

Classification Rules- Overfitting Problem

Stopping Criteria And Pruning

Model A decision Tree, Bayes

Random Forests

Unsupervised Learning

Hierarchical Clustering

k-Means algorithm for clustering – groupings of unlabeled data points.

Principal Component Analysis(PCA)

Natural Language Processing

Non-probabilistic Parsing, Probability

Information Theory, Language modeling and Naive Bayes

Viterbi Algorithm for Finding Most Likely HMM Path

Lexical Semantics, Sentiment Analysis

Artificial Intelligence

Production system, Ontology

Propositional logic,Pattern Recognition

Distance-Based Neural Networks

Population-based search

Deep Learning

Introduction to deep learning

Deep L-layer neural network

Forward Propagation in a Deep Network.

Why deep representations, Forward and Backward Propagation

Parameters vs Hyperparameters

In Bangalore Marathahalli is known as IT hub as well as contains of more Training institutes. So in this scenario it is difficult to choose best training Institute.

But for sure we are recommending Huddle Rise Technologies is Best data science training in Marathahalli. Best Institute is judge by having some factors such as Demo and Training and Giving Backup Facility for students and providing different Platform of students like providing classroom as well as online training.

Huddle Rise Technologies are providing there service since from 4 years with great training and showing good efforts on there students this factor will sounds as a Best institute. Data Science is a special training program in Huddle rise carrying forward with many batches. Starting from Demo to till end of doubts clarification Huddle Rise try to provide their best in all. Trainers who engaged with students that makes more impressive.

Huddle Rise come up with their unique ideas and scenarios that makes students to make place in different companies with good packages. This all to gather makes Huddle Rise Technologies as a best data science training institute in marathahalli Bangalore.

The "**data science**" has become one of the disciplines with the highest growth in enterprises and research institutions
due to the versatility of solutions that can be offered in different industries.
Many times without the user being aware, large amounts of data are processed to recommend a movie,
predict the price of a flight or classify a purchase as fraudulent or legitimate.

However, the process of analyzing data is expensive, complicated and imperfect,
since data science involves many different disciplines , in addition, there is no recipe for the "perfect model".
Unlike software development, which is clear about the final result, an analytical process can yield unexpected results
and sometimes a viable solution is not reached. **Data science **is an inaccurate science.

The field of data science employs mathematics, statistics and computer disciplines, and incorporates techniques such as machine learning, cluster analysis, data extraction and visualization.

We can define data scientists as professionals who, using large volumes of information and of different types, solve business problems and obtain answers from data.

Although there are no "typical" data science projects, since each project is a world, generally companies start with a structure similar to this one:

- It is required to solve a business problem or answer a question.
- The data scientist should obtain data from the origin that was in order to solve that question.Here, structured data comes into play (eg, databases such as SQL), unstructured (eg, images, audios) and semi-structured data (eg, texts with a certain structure).In companies that are just beginning, only structured data is used and accessed by a typical query language such as SQL.
- A series of techniques, algorithms, etc. are applied to these data. who try to solve the case.Here tools such as Python, R, SAS, etc. are used.
- The final result that can be obtained can be an analysis, a productivization of some statistical model, results for business, etc.

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