DATA SCIENCE TRAINING

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High Customizability
Lots of options – change width, layouts and appearance. All in just a few clicks.
Unlimited Color Choice
Choose from countless color options. Style separate elements, text blocks or headings. Full control over the color palette.
Fully Responsive
TiLT is a fully responsive theme looking perfect on any device and screen size.
Various Blog Layouts
Choose from several available blog templates to suit your general website style.
Unique Slanted Dividers
Make your website look original with firm TiLT dividers. Add them to any row you want and stand out from the crowd.
Header Styling
Choose from predefined color schemes for each page or make your own color for each header element.
Frequent Updates
Permanent improvement process with frequent update roll-outs ensure you own a bug-free and trendy theme.
Professional Support
Have a question or need some help? We are always here for you. Do not hesitate asking in case you need our assistance.
Styling & Customization
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Responsiveness
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Professional Support
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Unique Design
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Retina Ready
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Custom Elements
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Predictive Analysis
Leverage structured and unstructured data to predict future events -eg trends, pricing decisions, fraud risk, customer attrition etc.
Machine Learning
New Age Algorithm to derive value from complex and Bigdata -eg: real time bidding , IOT sensor database maintanance.
Text Analytics
Draw meaningful insights from text data – e.g. conversation themes, topics, sentiment, sales leads, customer satisfaction etc.
Forecasting
Drive superior operations and planning through demand forecasting, cost planning, granular sales, shipment forecasting etc.
Styling & Customization
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Responsiveness
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Unique Design
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Retina Ready
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High Customizability
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Unlimited color choice
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Unique Slanted Dividers
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Fully responsive
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Frequent Updates
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Professional Support
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Why Data Science ?
why do we choose Data Science
BETTER CAREER

Many top Business Companies in the world are offering Data Science Jobs

HIGH SALARIES

Average Salary for a Data Scientist will be from 9-16 LPA

MORE JOBS

By 2020 there will be 7,00,000 job openings for Data Scientist , Data Engineers .

It is estimated that by 2018, 4 million to 5 million jobs in the United States will require data analysis skills, and a recent study found “a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge (for the U.S.).”
When Harvard Business Review called Data science “The Sexiest Job of the 21st Century” the term became a buzzword and is now often applied to Business analytics and in the Bio-medical data science. There are two components to this course. It tells you the ideas behind turning data into actionable knowledge. Organizations across industries are looking to make sense of the information they’ll currently collect from new technologies – from predicting the next hot product to determining the risk of an infectious disease outbreak.

Our values

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Backend 90 %
Design 75 %
Marketing 85 %
PR 70 %
Why our customers return to us
the true stories

See our small representational video

Fully responsive
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Functional shortcodes
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Best shortcode practices
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CONTENT OF COURSE

DATA MODELLING

Descriptive / Inferential statistics
Various data types
Exploratory data Analysis
Random Variables
Probability

DISTRIBUTIONS

Probability Distributions
Discrete Probability distribution’
Continuous probability distribution

MODEL FITTING

How to fit the data
Hypothesis test
Fitting a linear model
Discrete terms
Multivariate models
Interaction terms
Generalised models

MODEL SELECTION

How to select an algorithm
Predictive Accuracy
Cross validation
Estimates of predictive accuracy
Model under fitting and over fitting

MACHINE LEARNING

Decision Tree
Theory of Decision tree
Statistics behind decision tree
Case study approach
Hands on using R & Python
Random Forest
Theory on Random forest
Hands on using R & Python
K-NN
Theory on K-NN
Case study approach
Hands on using R & Python
Bayesian classifier
Theory on Naïve Bayes
Case study approach
Hands on using R and Python
k-Means cluster
Distance measures
Case study approach
Hands on using R & Python
Agglomerative clustering
Case study approach
Hands on using R & Python
Text Mining
Sentimental Analysis using R & Python
Kernel SVM classifier
Theory on SVM
Case study approach
Hands on using R & Python
Neural Networks
Simple neural Network
Multi-layer perceptron
Hidden Layers
Hands on using R & python

DATA WRANGLING USING R

Data Frame
Matrices
Vectors
Lists
Packages
Reading different data into R
Graphical representation
Imputation
Dummy Variable creation
Summary statistics
Correlation
Covariance

DATA VISUALIZATION

• Understand Tableau Desktop Architecture and how to use Tableau in real life
• Tableau statistics and Tableau interactive dashboard
• Master Tableau Reporting, Graphs, Maps, Table Calculation
• Simplify and organize data with data connections
• Master Special Field Types and Tableau Generated Fields
• Learn to implement Data Aggregation and Data Blending in tableau
• Understand R Connectivity with Tableau
• Gain knowledge on using R scripts in Tableau
• Perform real time analytics and Tableau data visualization
• Prepare for Tableau Desktop Qualified Associate Exam

TOOLS

TABLEAU
Introduction to the various file types
How to access help
Quick introduction to the user interface in tableau
How to connect to the data sources
How to join various data sources
How to create data visualizations using tableau feature “show me”
Recorder and remove visualization fields
How to sort and filter data
How to create a calculated field
How to perform operations using cross – tab
Working with workbook data & worksheets
How to create a packaged workbook
Creating various charts
Creating maps & setting map options
Creating dashboards & working with dashboard

R & R-STUDIO
Introduction to r
Working with packages
Performing various regression and
Data mining techniques using R studio

PYTHON
Installation of python
Importance of python
Difference between r studio and python
How to perform various functions
How to view module content in python
How to request libraries in python
How to access the content
How to create content
Performing various regression and data mining techniques using python

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