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

Curriculum

What is Data Science?

• Introduction
• 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, 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)
• 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 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.