Data Science



Start 14th August 2020
Course Duration 90 Days
Class Monday – saturday
Seats Available 15 Per Batch
Total Classes 100
Timings 10am- 11am & 06 pm-08 pm


As IT industry is already a boom nowadays, it has become imperative for every Data Science IT Professional to owe an Data Science Cloud Certification. No prior knowledge or experience of any programming language to do Data Science certification program is required. Getting certification of Data Science will help you understand deployment and effective designs of Data Science and also make apposite use of Data Science architecture to design scalable and robust websites on Data Science. So, join the race and take the lead in taking decisions on making the most of Data Science in your corporate needs.

This course serves as an introduction to the data science principles required to tackle real-world, data-rich problems in business and academia, including:

  • Data acquisition, cleaning, and aggregation.
  • Exploratory data analysis and visualization.
  • Feature engineering
  • Model creation and validation
  • Basic statistical and mathematical foundations for data science.
Professor Experience
Ravi Krishna 10 Years+
Shankar 15 Years+
Sri Vani 10 Years+


What is Data Science?

Big Data and Data Science hype
And getting past the hype – Why now?
Current landscape of perspectives
Skill sets needed

Statistical Inference

Populations and samples

Statistical modeling, probability distributions, fitting 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: RealDirect (online real estate firm)

Three Basic Machine Learning Algorithms

Linear Regression

k-Nearest Neighbors (k-NN)


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 imagination)
Feature Selection algorithms
Filters; Wrappers; Decision Trees; Random Forests

Recommendation Systems : Building u User-Facing Data Product

Algorithmic ingredients of a Recommendation Engine
Dimensionality Reduction
ingular 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 graph

Data Visualization

Basic principles, ideas and tools for data visualization
Examples of inspiring (industry) projects
Exercise: create your own visualization of a complex dataset

Data Science and Ethical Issues

Discussions on privacy, security, ethics
A look back at Data Science
Next-generation data scientists

Frequently Asked Questions

Who should enroll for the course?

Any professional working in the IT industry or a fresh graduate or a job seeker may enroll for course to upskill themselves and be a part of the fastest growing industry.

Do you provide placement assistance?

Yes, we have very strong corporate tie ups, the organizations would reach out to us as and when the job openings comes up. Our dedicated placement cell would assist all of our students in preparing the resumes, interview preparation and support them for the placement.

Enroll now as an Instructor