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Top Data Science Training

May 27, 2018 - May 27, 2022


Professional Data Science Training

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization

Our Courses prepared by Industry experts and Data Science Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam


Mr Santhosh
Data Science
10+ Years of Experience as Data Science Programmer. Data Science and ADVANCED Certified Professional Programmer. Trained from Data Science Institute Australia. Worked for IBM, Accenture, SunCorp &Commonwealth Bank. Corporate trainer for Accenture, Capgemini.

What do our Students say ?

Their Data Science courses are well structured and taught by recognised professionals which helps one to learn Data Science fast. I have found the videos to be of excellent quality. Thanks
Swaroopa Nour
I had taken the data science master program which is a combo of . Since there are so many technologies involved in the Data Science course, getting your query resolved at the right time becomes the most important aspect.
Working in US
Anwar Pasha
I think this Data Science certification training is a very good way of starting to learn Data Science and make a career in it. The instructors are reasonably good. The projects was also very interesting and relevant to current industry trends.
IT Consultant
Pruthvi Krishna
The classes were highly interactive and also practical oriented. The office staff was cordial and co-operative. Every teaching session was recorded each day and was put on-line by the institute which was really helpful.
Asst Consultant
Shiva Kumar

Course Content

Introduction: What is Data Science?

– Big Data and Data Science hype { and getting past the hype

– Why now? { Datafication }

– Current landscape of perspectives

– Skill sets needed

Statistical Inference

– Populations and samples

– Statistical modeling, probability distributions, tting a model

– Intro to R

Exploratory Data Analysis and the Data Science Process

– Basic tools (plots, graphs and summary statistics) of EDA

– Philosophy of EDA

– The Data Science Process

– Case Study: Real Direct (online real estate rm)

Three Basic Machine Learning Algorithms

-Linear Regression

– k-Nearest Neighbors (k-NN)

– k-means

One More Machine Learning Algorithm and Usage in Applications

– Motivating application: Filtering Spam

– Why Linear Regression and k-NN are poor choices for Filtering Spam

– Naive Bayes and why it works for Filtering Spam

– Data Wrangling: APIs and other tools for scrapping the Web

Feature Generation and Feature Selection (Extracting Meaning From Data)

– Motivating application: user (customer) retention

– Feature Generation (brainstorming, role of domain expertise, and place for


– Feature Selection algorithms

{Filters; Wrappers; Decision Trees; Random Forests

Recommendation Systems: Building a User-Facing Data Product

– Algorithmic ingredients of a Recommendation Engine

– Dimensionality Reduction

– Singular Value Decomposition

– Principal Component Analysis

– Exercise: build your own recommendation system

Mining Social-Network Graphs

– Social networks as graphs

– Clustering of graphs

– Direct discovery of communities in graphs

– Partitioning of graphs

– Neighborhood properties in graphs

Data Visualization

– Basic principles, ideas and tools for data visualization

– Examples of inspiring (industry) projects

– Exercise: create your own visualization of a complex dataset



Introduction to supervised learning

Regression and classification

R-square, RMSE for regression

Confusion Matrix

Accuracy, Precision and Recall

F-1 score


(i) Linear Regression

Simple Liner Regression

Model Development

Model Validation

(ii) Logistic Regression

Model Development

Model Validation

Interpretation and Implementation

(iii) Decision Trees


Decision Nodes Vs Leaf Nodes

Entropy, Gini Index and Information gain

Over fitting ad Pruning

(iv) Bagging and Random Forest

(v) Boosting



Hierarchical clustering

K-Means Clustering

Cluster profiling

Principal Component Analysis

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R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years.

R is a GNU package. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available.

  • Data types and Data structures
  • Mathematical operations in R
  • Conditional statements
  • Loops
  • Packages and Functions in R
  • Data Manipulation
  • Data Pre-Processing
  • Exercises


Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991. An interpreted language, Python has a design philosophy that emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly brackets or keywords), and a syntax that allows programmers
to express concepts in fewer lines of code than might be used in languages such as C++ or Java. It provides constructs that enable clear programming on both small and large scales Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library. Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is open source software ] and has a community-based development
model, as do nearly all of its variant implementations. CPython is managed by the non-profit Python Software Foundation.

  • Python programming Introduction
  • Data types and structures
  • Control statements
  • Functions
  • User defined functions
  • Python Packages
  • Exercises.Note: Practical training of all models in Python and R programming along with reference code


May 27, 2018
May 27, 2022
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+91 040-40208208

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