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Best Data Science Online Training Institute In Hyderabad

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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.

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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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