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.
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
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
4: Basic Distributions
5.Sampling Technique Big Data
Standard Error of the Mean
Skewed Std. Error
Kurtosis Std. Error
Sampling from Infinity
Sampling Distributions for Mean
Sampling Distributions for proportions
6: Data Validation & Data Normality
Steam and leaf analysis
Univariate normality techniques
Q-Q probability plots
Scores for Normality Check
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
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
Type I and Type II errors
One tail and two tail
Kruskal-Wallis Rank Test Chi-Square
10: Data Transformation
Log, Arcsine, Box-Cox, Square root Inverse and Data normalization
11: Predictive modelling & Diagnostics
Examination Residual analysis üAuto Correlation
Test of ANOVA Significant üHomoscedasticity üHeteroskedasticity üMulticollinearity
Check prediction accuracy.
12: Logistic Regression Analysis
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
13: Big Data Analytics
Introduction to Factor Analysis
Principle component analysis
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
K Means clustering
Maximum distance Linkage Methods
Centroid distance Methods
Minimum distance Linkage Method
15: Data Mining Machine Learning and Artificial Intelligence
Support Vector Machines
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
Note: Open source and commercial Tools is a part of training.