Sai Madiraju

Years of Experience in Data Science Classes

4

Teaching Experience in detail in Data Science Classes

I have been teaching Math/STAT/Aptitude to the aspirants of various competitive exams in India, for the last 20 years .I was into DATA Science since 2015.I was trained in DATA SCIENCE at Hyderabad. I have been teaching DATA SCIENCE/MACHINE LEARNING/ARTIFICIAL INTELLIGENCE at SATHYA TECH.s AMEERPET for the last 4 years. As a faculty of DATA SCIENCE I taught following topics: Descriptive statistics- measures of central tendency -mean, median mode, measures of dispersion -Range , quartile deviation , mean deviation and variance. Probability, Inferential statistics - difference b/w SAMPLE and POPULATION estimation of Mean , population proportion, confidence intervals, variance.. etc. Distribution functions -binomial, poison, Normal. Bivariate analysis , ANOVA. Regression analysis , multi variate analysis ,Machine learning and Neural networks.Deep learning , Artificial intelligence. using python , R and Tablaeu. We are offering an online course in Data Science -Machine learning and Artificial intelligence Course duration is 90 hrs of teaching , every day 1 hour sessions are delivered online , on every Sunday , students are going to solve real time industry related projects under the guidance of experienced data scientist.We also help freshers in resume preparartion, give sample projects so that they may show their work in resume, we also help experienced candidates who want career shift into the world of Data Science and Artificial intelligence succesfully.

Years of Experience in Python Training classes

4

Teaching Experience in detail in Python Training classes

I have been teaching python for the last 4 years you can do every thing in python, especially for statistical analysis and Machine learning Python provides great tools.Python provides excellent graphical facilities and it is easy to impliment advanced machine learning algoriths like Random forests , k-nearest neighbours, Support vector machines , Textmining,Nueralnets, Logistic regression, Cross vallidation , K-means clustering ,Natural language processing,Deeplearning , Bigdata annd Spark with Python. we are providing online training for " Data Science in Python" / , "Data Analytics in Python". Come join , online classes , let us explore data visualisation with Python.

Years of Experience in Data Visualization Training

20

Teaching Experience in detail in Data Visualization Training

I have been teaching Statistics /Mathematics to the aspirants of various competitive exams in India, for the last 20 years .Recently I was into DATA ANALYTICS. I was trained in DATA SCIENCE at Hyderabad. I have been teaching DATA ANALYTICS online for the last 4 years. As a faculty of DATA SCIENCE I taught following topics; Descriptive statistics- measures of central tendency -mean, median mode, measures of dispersion -Range , quartile deviation , mean deviation and variance. Probability, Inferential statistics - difference b/w SAMPLE and POPULATION estimation of Mean, population proportion, confidence intervals, variance.. etc. Distribution functions -binomial, poison, Normal . Bivariate analysis, ANOVA. Regression analysis , multi variate analysis, Machine learning and Neural networks. As part of data visualization we look at Bar graphs, pie charts, Ogive curves, Histograms and we v visualize the using ggplot2 in R. we also use Tableau for Dash board visualization. We are offering an online course in STATISTICAL DATA ANALYTICS./DATA SCIENCE/MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE . Course duration is 90 hrs of teaching , every day 1 hour sessions are delivered online ,

Years of Experience in Advanced Statistics Training

20

Teaching Experience in detail in Advanced Statistics Training

I have been teaching Math/STAT/Aptitude to the aspirants of various competitive exams in India, for the last 20 years .I was into DATA Science since 2015.I was trained in DATA SCIENCE at Hyderabad. I have been teaching DATA SCIENCE/MACHINE LEARNING/ARTIFICIAL INTELLIGENCE at SATHYA TECH.s AMEERPET for the last 4 years. As a faculty of DATA SCIENCE I taught following topics: Descriptive statistics- measures of central tendency -mean, median mode, measures of dispersion -Range , quartile deviation , mean deviation and variance. Probability, Inferential statistics - difference b/w SAMPLE and POPULATION estimation of Mean , population proportion, confidence intervals, variance.. etc. Distribution functions -binomial, poison, Normal. Bivariate analysis , ANOVA. Regression analysis , multi variate analysis ,Machine learning and Neural networks.Deep learning , Artificial intelligence. using python , R and Tablaeu. We are offering an online course in Data Science -Machine learning and Artificial intelligence Course duration is 90 hrs of teaching , every day 1 hour sessions are delivered online , on every Sunday , students are going to solve real time industry related projects under the guidance of experienced data scientist.We also help freshers in resume preparartion, give sample projects so that they may show their work in resume, we also help experienced candidates who want career shift into the world of Data Science and Artificial intelligence succesfully..

Years of Experience in Data Analytics classes

4

Teaching Experience in detail in Data Analytics classes

I am a senior lecturer/trainer with over 16 years of experience of teaching Statistics/Mathematics for various competitive exams in India. Believe in concepts based learning and training thus I see that my students learn the concepts thoroughly. Over the years I helped thousands of aspirants to form a career in Private/Public sectors.I cover following modules in Data analytics.Differentiating algorithmic and model based frameworks, Bias-variance Dichotomy, Model Validation Approaches, Supervised Learning with Regression and Classification techniques Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, Classification: Logistic Regression, K Nearest Neighbours Regression & Classification, Baye’s Rule and Classification Problem, Linear Discriminant Analysis, Quadratic Discriminant Analysis: Decision Trees: Regression and Classification Trees Bagging, Boosting Ensemble Methods, Random Forest, Bootstrap. Neural networks: Introduction, Single Layer Perception.Multi-layer Perception Forward Feed and backward propagation. Support Vector Machine: Maximum Marginal Classifier, Support Vector Classier, Support Vector Machine, SVMs with More than Two Classes Unsupervised learning: Principle component analysis, clustering methods, K-means clustering, Hierarchical clustering, Meloid Cluster Analysis Dimensionality Reduction, Principal Component Analysis, Association rules, Market Basket Analysis

Years of Experience in R Programming

4

Teaching Experience in detail in R Programming

R - Programming is fun to explore and train , its a open source language developed by committed ,strong ever growing community. Easy to understand , easy to explain , almost for every domain there is some application /algorithm available in CRAN repository, you just have to download suitable package from the library and go for trial and error for model accuracy. Best part is ,if you want to become data scientist , it is enough to learn R-anguage ..instead of learning many ..packages like ANAKONDA, NUMPY , SCIPY, PYTHON ...

Years of Experience in Data Mining Data

4

Teaching Experience in detail in Data Mining Data

I have been teaching Math/STAT/Aptitude to the aspirants of various competitive exams in India, for the last 20 years .I was into DATA Science since 2015.I was trained in DATA SCIENCE at Hyderabad. I have been teaching DATA SCIENCE/MACHINE LEARNING/ARTIFICIAL INTELLIGENCE at SATHYA TECH.s AMEERPET for the last 4 years. Now I offer an online course in Data Science -Machine learning and Artificial intelligence Course duration is 90 hrs of teaching , every day 1 hour sessions are delivered online , on every Sunday , students are going to solve real time industry related projects under the guidance of experienced data scientist.We also help freshers in resume preparartion, give sample projects so that they may show their work in resume, we also help experienced candidates who want career shift into the world of Data Science and Artificial intelligence succesfully. Differentiating algorithmic and model based frameworks, Bias-variance Dichotomy, Model Validation Approaches, Supervised Learning with Regression and Classification techniques Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, Classification: Logistic Regression, K Nearest Neighbours Regression & Classification, Baye’s Rule and Classification Problem, Linear Discriminant Analysis, Quadratic Discriminant Analysis: Decision Trees: Regression and Classification Trees Bagging, Boosting Ensemble Methods, Random Forest, Bootstrap. Neural networks: Introduction, Single Layer Perception.Multi-layer Perception Forward Feed and backward propagation. Support Vector Machine: Maximum Marginal Classifier, Support Vector Classier, Support Vector Machine, SVMs with More than Two Classes Unsupervised learning: Principle component analysis, clustering methods, K-means clustering, Hierarchical clustering, Meloid Cluster Analysis Dimensionality Reduction, Principal Component Analysis, Association rules, Market Basket Analysis

Years of Experience in Data Analysis Classes

4

Teaching Experience in detail in Data Analysis Classes

I have been teaching DATA ANALYTICS online for the last 4 years.As a faculty of DATA SCIENCE I taught following topics ;Descriptive statistics- Probability, Inferential statistics -.Regression analysis , , Machine learning and Neural networks.We are offering an online course in STATISTICAL DATA ANALYTICS. duration is 90 hrs of teaching , every day 1 hour sessions are delivered online .Differentiating algorithmic and model based frameworks, Bias-variance Dichotomy, Model Validation Approaches, Supervised Learning with Regression and Classification techniques Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, Classification: Logistic Regression, K Nearest Neighbours Regression & Classification, Baye’s Rule and Classification Problem, Linear Discriminant Analysis, Quadratic Discriminant Analysis: Decision Trees: Regression and Classification Trees Bagging, Boosting Ensemble Methods, Random Forest, Bootstrap. Neural networks: Introduction, Single Layer Perception.Multi-layer Perception Forward Feed and backward propagation. Support Vector Machine: Maximum Marginal Classifier, Support Vector Classier, Support Vector Machine, SVMs with More than Two Classes Unsupervised learning: Principle component analysis, clustering methods, K-means clustering, Hierarchical clustering, Meloid Cluster Analysis Dimensionality Reduction, Principal Component Analysis, Association rules, Market Basket Analysis