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Best Data Science Online Training Institute In Hyderabad

Supreeet Solutions Provides Best Data Science Online Training Institute In Hyderabad by 15+yrs of Real time Industry Experts, Data Science Online Training will gives you a very clear introduction of Data Science and can make you understand clear vision of Data Science Management.

We in Supreeet IT Data Science Online Training Specially designed for the fresher’s to understand in-depth knowledge of Data Science and Endeavour their career in  Industry and also we mind the Data Science existing professionals who are try to upgrade their career into top level of Management in Data Science.

Course objectives:-

The course is designed to provide in- depth subject and knowledge of handling business data and Analytics’ tools that can be used for problem solving and decision making using real business case studies.

The outcome of the course, the participants will be able to:

  • Understand the foundations of data science; the role of descriptive, predictive and prescriptive analytics.
  • Understand the emergence of business analytics as a competitive strategy.
  • Analyze data using statistical and data mining techniques
  • Understand relationships between the underlying business processes of an organization.
  • Data visualization
  • Storytelling through data.
  • Decision-making tools
  • Operations Research techniques.
  • Use advanced analytical tools to Analyze complex problems.
  • Manage business processes using analytical and management tools.
  • Analyze and solve customer problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace, etc.
  • Analytics through case studies published by different business schools
  • Understand sources of Big Data and the technologies
  • Algorithms for analyzing big data for inferences.
  • Ability to analyze unstructured data such as social media data and machine generated data.
  • Hands on experience with software such as free software’s Microsoft Excel, Python, R, SAS,SQL,etc and commercial software’s

Benefits from the course

  • Increase revenues to the business
  • Realizing cost efficiency
  • Improving competitiveness
  • Sharing information with a business with presentations
  • Improving the decision-making process
  • Speeding up of decision-making process
  • Responding to business user needs for availability of data on timely basis

Data Science Course Modules :-

Module-1: Introduction Data science & Business Analytics

Module-2: Descriptive Statistics

Module-3: Basic Probability for Business issues:

Module-4: Basic Distributions:

Module-5: Sampling Technique Big Data

Module-6: Data Validation & Data Normality

Module-7: Data cleaning process Quality check

Module-8: Data Imputation and outlier treatment

Module-9: Test of Hypothesis

Module-10: Data Transformation

Module-11: Predictive modeling & Diagnostics

Module-12: Logistic Regression Analysis

Module-13: Big Data Analytics

Module-14: Cluster Analysis and Methods

Module-15: Data Mining Machine Learning and Artificial Intelligence

Module-16: Time series

Module-17: Model Validation and Testing

Module-18: Hadoop Ecosystem

Note : Open source and commercial Tools is a part of training.

1:Introduction Data science & Business Analytics

  • Data Science and Business analytics
  • Introduction to Advanced Data Analytics
  • Charts for Data Science and Business Analytics üHadoop for Data Science

2:Descriptive Statistics

  • 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

3.Basic Probability for Business issues

  • Simple
  • Marginal
  • Joint
  • Conditional
  • Bayes’ Theorem

4:Basic Distributions

  • Discrete
  • Binomial
  • Hyper geometric
  • Poisson
  • Continuous
  • Normal
  • Scandalized

5.Sampling Technique Big Data

  • 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

6:Data Validation & Data Normality

  • 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

7: Data cleaning process Quality check

  • PCA for Big Data Analysis or Unsupervised data üPCA Regression Scores for Supervised data üNoise Data detecting
  • Data cleaning with Regression Residual üData scrubbing with statistical sense

8:Data Imputation and outlier treatment

  • 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

9: Test of Hypothesis

  • Null Hypothesis formulation
  • Alternative Hypothesis
  • Type I and Type II errors
  • Power Value
  • One tail and two tail
  • T-TEST’s
  • ANOVA
  • MANOVA
  • Chi Square Test
  • Kendall Chi Square
  • Kruskal-Wallis Rank Test Chi Square
  • Mann-Whitney, Chi Square
  • Wilcoxon, Chi Square

10: Data Transformation>

  • Log, Arcsine, Box- Cox, Square root Inverse and Data normalization

11:Predictive modeling & Diagnostics

  • Correlation üRegression
  • Examination Residual analysis üAuto Correlation
  • Test of ANOVA Significant üHomoscedasticity üHeteroskedasticity üMulticollinearity
  • Cross validation
  • Check prediction accuracy.

12:Logistic Regression Analysis

  • 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

13: Big Data Analytics

  • 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

14:Cluster Analysis and Methods

  • Introduction to Cluster Techniques
  • Hierarchical clustering
  • K Means clustering
  • Wards Methods
  • Aglomerative Clustering
  • Variation Methods
  • Maximum distance Linkage Methods
  • Centroid distance Methods
  • Minimum distance Linkage Method
  • Cluster Dendrogram
  •  Euclidean distance

15:Data Mining Machine Learning and Artificial Intelligence

  •  Prediction
  •  Support Vector Machines
  • Gaussian Models
  • Neural Network
  • Classification Models
  • Ordinal Regression
  • Multinomial Regression
  • Discriminate analysis
  • Simple Cluster
  • Hierarchical Cluster

16:Time series

  • Auto Regression, Moving Average, Multiplicative, ARMA, Additive Model

17:Model Validation and Testing

  • AIC, BIC, Kappa Statistics, ROC, APE, MAPE, Lift Curve, Errors

18: Hadoop Ecosystem

  • Pig,Hive,Map Reduce,NoSQL,etc

Note : Open source and commercial Tools is a part of training.

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