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Advanced Data Science

Grasp the fundamental concepts of Data Science and learn Statistical Analysis, Machine learning, Predictive Analytics many more.
  • 24 Hours Classroom & Online Sessions
  • 40+ Hours Assignments & Real-Time Projects
  • Complimentary Python and R Programming Beginners Course
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"The Data Science course has been designed for senior professionals, Managers, Senior Executors, and Decision Makers with an aptitude for analyzing the business data to take advantage while making appropriate decisions. The course details the aspects of Machine learning techniques like Data Analysis, Explainable Machine Learning Algorithms, the black box model Neural Network, and its architectures. Finally, the program enlightens and encourages the adoption of Analytics for Data-Driven Decision Making.

Data Science

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Total Duration

3 Months

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Prerequisites

  • Computer Skills
  • Basic Programming Knowledge
  • Analytical Mindset

Advanced Data Science Overview

This course will enable you to understand Data Science and its applications to leverage the Data. Perform statistical computation on the Data to estimate results for business decisions. Gain knowledge on the most popular CRISP-DM project management methodology for Data Mining projects. You will understand the difference between Descriptive and Predictive analytics and learn to develop Prediction models. By the end of the foundation course in Data Science, students will have great insights on the application of programming tools R, Python, and will be confident enough to grab opportunities in Data Science and have a flourishing career.

Data Science

Data Science is one of the most promising and in-demand courses for skilled professionals. It is all about analyzing huge amounts of data, Data mining, and programming. Data science is still evolving and has abundant opportunities.

Data Scientist

Data Scientist are a group of analytical experts who inscribe data and find out the patterns in the data. A Data Scientist has efficient skills in handling raw, structured, and unstructured data, analyzing it, implementing in various statistical procedures, visualizing, and generating insights from the data.

Data Scientists should have adequate knowledge about tools like R, Python, Tableau, Hadoop, SAS, etc. Ten years ago there was no noise about Data Science, but all of a sudden the hype about Data Science has increased immensely. A Data Science professional should be able to balance business-related work and IT-work. A Data Scientist should be in a position to handle the situations and resolve the issues which might arise in the future.

Advanced Data Science Learning Outcomes

Understand the Applications of Data Science
Understand the Project Management Methodology used for Data science-related projects
Learn to deal with various types of Data
Be introduced to Predictive Analytics and distinguish it from Descriptive Analytics

Who Should Attend

Analytics Managers/Professionals, Business Analysts, Software Developers
Professionals who are looking to get an understanding of Data Analytics, and Data Processing
Management who are aiming to get an understanding to embark on the journey of Data Science

Block Your Time

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24 hours

Classroom Sessions

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40 hours

Assignments &
e-Learning

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40 hours

Live Projects

Who Should Sign Up?

  • Graduates
  • Freshers, Junior/Mid-Level Executives
  • Employees of Organizations
  • Managers
  • Business Analytst
  • IT Engineers
  • Data Analyst

Modules for Advanced Data Science Course

Extension to logistic regression We have a multinomial regression technique used to predict multiple categorical outcomes. Understand the concept of multi logit equations, baseline, and making classifications using probability outcomes.
Learn about handling multiple categories in output variables including nominal as well as ordinal data.

 
  • Logit and Log-Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Handling Ordinal Categorical Data
  • Interpreting the results of coefficient values
 
  • Lasso Regression
  • Ridge Regression
  • Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques Lasso and Ridge techniques are discussed in this module.

Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros, and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

 
  • User-based Collaborative Filtering
  • A measure of distance/similarity between users
  • Driver for Recommendation
  • Computation Reduction Techniques
  • Search based methods/Item to Item Collaborative Filtering
  • SVD in recommendation
  • The vulnerability of recommendation systems

Learn about improving the reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go-to techniques in ensemble techniques. The parallel and sequential approaches are taken in Bagging and Boosting methods are discussed in this module.

 
  • Overfitting
  • Underfitting
  • Pruning
  • Boosting
  • Bagging or Bootstrap aggregating

The study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

 
  • Definition of a network (the LinkedIn analogy)
  • The measure of Node strength in a Network
  • Degree centrality
  • Closeness centrality
  • Eigenvector centrality
  • Adjacency matrix
  • Betweenness centrality
  • Cluster coefficient
  • Introduction to Google page ranking
How We Prepare You
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    Additional Assignments of over 60-80 hours
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    Live Free Webinars
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    Resume and LinkedIn Review Sessions
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    6 Months Access to LMS
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    24/7 support
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    Job Assistance in Data Science Fields
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    Complimentary Courses
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    Unlimited Mock Interview and Quiz Session
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    Hands-on Experience in a Live Project
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    Life Time Free access to Industry Webinars

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