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Advanced Data Science

Become a Data Scientist and learn Statistical Analysis, Machine Learning, Predictive Analytics, and many more.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 184 Hours of Intensive Classroom & Online Sessions
  • 2 Capstone Live Projects
  • Receive Certificate from Technology Leader - IBM
  • Job Placement Assistance
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The Indian Data Science Market will be worth 6 million dollars in 2025 and the Data analytics outsourcing market in India is worth $26 Billion." India will undoubtedly witness around three lakh job openings in data science by 2021. India is second to the United States in terms of the number of job openings in Data Science. In 2019, 98,123 positions in data science and analytics were vacant due to a lack of qualified candidates. The top sectors creating the most data science jobs are BFSI, Energy, Pharmaceutical, Healthcare, E-commerce, Media, and Retail. Today large companies, medium sized companies and even startups are willing to hire data scientists in India. The five most sought after digital skills are Big Data, software and user testing, Mobile Development, Cloud Computing, and software engineering management.

Certified Data Scientist Course

Program Cost

INR 47,200 33,040/-

Data Science Course Programme Overview

This Data Science course using Python and R endorses the CRISP-DM Project Management methodology and contains a preliminary introduction of the same. Data Science is a 90% statistical analysis and it is only fair that the premier modules should bear an introduction to Statistical Data Business Intelligence and Data Visualization techniques. Students will grapple with Plots, Inferential Statistics, and various Probability Distributions in the module. A brief exposition on Exploratory Data Analysis/ Descriptive Analytics is huddled in between. The core modules commence with a focus on Hypothesis Testing and the "4" must know hypothesis tests. Data Mining with Supervised Learning and the use of Linear Regression and OLS to enable the same find mention in succeeding modules. The prominent use of Multiple Linear Regression to build Prediction Models is elaborated. The theory behind Lasso and Ridge Regressions, Logistic Regression, Multinomial Regression, and Advanced Regression For Count Data is discussed in the subsequent modules.

A separate module is devoted to Data Mining Unsupervised Learning where the techniques of Clustering, Dimension Reduction, and Association Rules are elaborated. The nitty-gritty of Recommendation Engines and Network Analytics are detailed in the following modules. The various Machine Learning algorithms follow next like k-NN Classifier, Decision Tree and Random Forest, Ensemble Techniques, Bagging and Boosting, Adaboost, and Extreme Gradient Boosting. Text Mining, Natural Language Processing, Naive Bayes, Perceptron, and Multilayer Perceptron are the focal points of the succeeding modules.

The fundamentals of Neural Network ANN and Deep Learning Black Box Techniques like CNN, RNN, and SVM find prominent features as well. The concluding modules contain model-driven and data-driven algorithms for Forecasting and Time Series Analysis.

What is Data Science?

Data science is an amalgam of methods derived from statistics, Data Analytics, and Machine Learning that are trained to extract and analyze huge volumes of structured and unstructured data.

Who is a Data Scientist?

A Data Scientist is a researcher who has to prepare huge volumes of big data for analysis, build complex quantitative algorithms to organize and synthesize the information, and present the findings with compelling visualizations to senior management.

A Data Scientist enhances business decision making by introducing greater speed and better direction to the entire process.

A Data Scientist must be a person who loves playing with numbers and figures. A strong analytical mindset coupled with strong industrial knowledge is the skill set most desired in a data scientist. He must possess above the average communication skills and must be adept in communicating the technical concepts to non - technical people. Data Scientists need a strong foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and modeling to build winning algorithms.

They must be proficient in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS.

Data Science Training 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
data science course - 360digitmg

184 hours

Classroom Sessions

data science course - 360digitmg

150+ hours


data science course - 360digitmg

120 hours

2 Live Projects

Who Should Sign Up?
  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

Data Science Certification Course Modules

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

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