Datascience Classroom Training

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About Datascience Course

Datascience Course Description

sun trainings is a brand and providing quality online and offline trainings for students in world wide. sun trainings providing Best DataScience training in Hyderabad and Datascience classroom training in Hyderabad

Course Name: Datascience
faculty:Real time Experience

Frequently Asked Questions

  • You will never miss a class at Suntrainings! You can choose either of the two options:
  • 1. You can go through the recorded session of the missed class and the class presentation that are available for online viewing through the LMS.
  • 2. You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the course.
  • Sun Trainings is committed to provide you an awesome learning experience through world-class content and best-in-class instructors.
  • We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
  • We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
  • All instructors at Sun Trainings are senior industry practitioners with minimum 10 - 12 years of relevant IT experience. They are subject matter experts who trained by Sun Trainings to provide impeccable learning experience to all our global users.
  • You can Call us at +91 9642434362 OR Email us at We shall be glad to assist you.

Datascience Course Curriculum

  • Data Science and Business analytics
  • Introduction to Advanced Data Analytics
  • Charts for Data Science and Business Analytics
  • Hadoop for Data Science
  • Descriptive Statistical
  • Inferential Statistics
  • Types of Variables
  • Measures of central tendency
  • Data Viability Dispersion
  • Five number Summary Analysis
  • Data Distribution Techniques
  • Exploration Techniques for Numerical and Character data
  • Summary and Visualization Exploration
  • Simple
  • Marginal
  • Joint
  • Conditional
  • Bayes’ Theorem
  • Discrete
  • Binomial
  • Hyper geometric
  • Poisson
  • Continuous
  • Normal
  • Scandalized
  • Sampling Distributions
  • Simple Random
  • Systematic Sample
  • Cluster Sample
  • Standard Error of the Mean
  • Skewed Std. Error
  • Kurtosis Std. Error
  • Sampling from Infinity
  • Sampling Distributions for Mean
  • Sampling Distributions for proportions
  • Theorem’s
  • Steam and leaf analysis
  • Unvariate normality techniques
  • Multivariate techniques
  • Q-Q probability plots
  • Cumulative frequency
  • Explorer analysis
  • Histogram
  • Box plot
  • Scores for Normality Check
  • Testing
  • PCA for Big Data Analysis or Unsupervised data
  • PCA Regression Scores for Supervised data
  • Noise Data detecting
  • Data cleaning with Regression Residual
  • Data scrubbing with statistical sense
  • Outlier treatment with central tendency Mean
  • Outlier with Min Max
  • Outlier Detection
  • Visualize Outlier Treatment
  • Summarized Outlier Treatment
  • Outlier with Residual Analysis
  • Outlier Detection with PCA Analysis
  • Data Imputation with series Central Tendency
  • Null Hypothesis formulation
  • Alternative Hypothesis
  • Type I and Type II errors
  • Power Value
  • One tail and two tail
  • T-TEST’s
  • Chi Square Test
  • Kendall Chi Square
  • Kruskal-Wallis Rank Test Chi Square
  • Mann-Whitney, Chi Square
  • Wilcoxon, Chi Square
  • Log, Arcsine, Box- Cox, Square root Inverse and Data normalization
  • Correlation
  • Regression
  • Examination Residual analysis
  • Auto Correlation
  • Test of ANOVA Significant
  • Homoscedasticity
  • Heteroskedasticity
  • Multicollinearity
  • Cross validation
  • Check prediction accuracy
  • Logistic Regression
  • Discriminate Regression Analysis
  • Multiple Discriminate Analysis
  • Stepwise Discriminate Analysis
  • Logic function
  • Test of Associations
  • Chi-square strength of association
  • Binary Regression Analysis
  • Estimation of probability using logistic regression,
  • Hosmer Lemeshow
  • nagelkerke R square
  • Pseudo R square
  • Model Fit
  • Model cross validation
  • Discrimination functions
  • Introduction to Factor Analysis
  • Principle component analysis
  • Reliability Test
  • KMO MSA tests, etc..
  • Rotation and Extraction steps
  • Conformity Factor Analysis
  • Exploratory Factor Analysis
  • Factor Score for Regression
  • Introduction to Cluster Techniques
  • Hierarchical clustering
  • K Means clustering
  • Wards Methods
  • Agglomerative Clustering
  • Variation Methods
  • Maximum distance Linkage Methods
  • Centroid distance Methods
  • Minimum distance Linkage Method
  • Cluster Dendrogram
  • Euclidean distance
  • Prediction
  • Support Vector Machines
  • Gaussian Models
  • Neural Network
  • Classification Models
  • Ordinal Regression
  • Multinomial Regression
  • Discriminate analysis
  • Simple Cluster
  • Hierarchical Cluster
  • Auto Regression
  • Moving Average
  • Multiplicative
  • ARMA
  • Additive Model
  • AIC, BIC, Kappa Statistics, ROC, APE, MAPE, Lift Curve, Errors
  • Pig,Hive,Map Reduce,NoSQL,etc

Trainer Information

  • Trainer has 15+ years of experience in IT industry.