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NSHM KNOWLEDGE CAMPUS
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NSHM KNOWLEDGE CAMPUS
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The program will build the foundation in mathematics and Statistics so that participants will be able to learn and apply the fundamental Mathematical techniques used for analytics and machine learning; and learn and appreciate the Staistical concepts that form the foundation of data science. To solve problems to new situations using Data Science and Analytics by employing the knowledge of statistics, data modeling, data mining, soft computing, and big data to store, process, analyze, describe, classify business data to discover patterns and insights, and build predictive machine learning models. Besides proposing alternative solutions to complex data science problems with the help of higher order intelligence-backed strategies for data collection, validation, organization, analysis and quantitative modeling to demonstrate discoveries from data analysis for medical, financial, and scientific applications for the benefit of business and sustainable development.
Review of n-dimensional vector spaces, linear independence, bases, dimension, subspaces,matrix representations, transformations, matrix decomposition, and use in modeling.
Understanding of the principles, and concepts for probability, limit, continuity, derivative and integrals. Making use of its various applications
Gain understanding of time series, decomposition, smoothing techniques. Apply time series concepts for data analysis and forecasting. Making use of its various applications
Gain appreciation of stochastics processes, stationarity, brownian motion, Markov chain. Apply stochastic processes for data analysis.
Programming languages for analysis – R, Python, learn object oriented concepts, develop programs for machine learning models. Software tools for advanced analytics and programming – Tableau, SPSS, Hadoop, SAS.
Algorithm complexity and optimization, biology inspired methodologies such as genetics, evolution, ant’s behaviors, particles swarming, human nervous systems to solving computational problems.
Apply common statistical distributions, confidence intervals, parametric and non-parametric hypothesis testing, p-values, and resampling techniques.
Develop models to predict categorical and continuous outcomes, using such supervised and unsupervised machine learning techniques such as neural networks, decision trees, logistic regression, support vector machines and Bayesian network models. Learn business analytics and big data.
Multivariate problems, dimension reduction techniques, assumpotions, regression and other techniques to analyze multivariate data.
Machine learning in solving social, economics and industrial problems. Demonstrate the applications in biostatistics, econometrics, supply chain, retail, marketing, and more.
Research and development in machine learning, define and formulate research problems, perform literature review, acquire, preprocess, analyze data and make inferences.
Payment Date |
One Time Payment | Yearly Payment Payment Deadline | Half-Yearly Payment |
Within 15 days of Admission | INR 1,40,000 | INR 90,000 | INR 48,000 |
By 30th Nov, 2022 | INR 48,000 | ||
By 31st May, 2023 | INR 60,000 | INR 32,000 | |
By 30th Nov, 2023 | INR 32,000 | ||
Tuition Fee Total | INR 1,40,000 | INR 1,50,000 | INR 1,60,000 |
Total Fees: | INR 1,90,000 | INR 2,00,000 | INR 2,10,000 |
Payment Date |
One Time Payment | Yearly Payment Payment Deadline | Half-Yearly Payment |
Within 15 days of Admission | INR 95,000 | INR 60,000 | INR 32,500 |
By 30th Nov, 2022 | INR 32,500 | ||
By 31st May, 2023 | INR 40,000 | INR 21,500 | |
By 30th Nov, 2023 | INR 21,500 | ||
Tuition Fee Total | INR 95,000 | INR 1,00,000 | INR 1,07,500 |
Total Fees: | INR 1,45,000 | INR 1,50,000 | INR 1,57,500 |