Data Science

Data Science Online Training In Hyderabad

Supreeet IT Provides Best Data Science Online Training in Hyderabad by 15+yrs of Real-time Industry Experts, Data Science online training will give you a very clear introduction of Data Science and can make you understand the 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 trying to upgrade their career into top level 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 the decision-making process
  • Responding to business user needs for availability of data on a 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

Hypergeometric

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

Univariate 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 the 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 modelling & 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

Nagelkirk 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

Agglomerative 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.