BEST DATA SCIENCE

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DATA SCIENCE COURSES IN BANGALORE

Indras Academy is leading Data science courses in Bangalore Marathahalli, koramangala, BTM Layout. This is a comprehensive course which builds on the knowledge and experience a business analyst and data scientist will have obtained after some years in the role. This course takes the business analysts / predictive modelers to the next level in terms of delivering effective and realistic solutions to machine learning and big data problems. This course provides techniques for data cleaning, visualizing the data, redictive modeling and machine learning. R, Predictive Modeling, Machine Learning, Python and SAS.

DATA SCIENCE COURSE DETAILS

data science iconCOURSE DURATION

  • 140-150 hrs long Self-paced and instructor-led online and off line data analytics training.
  • 100% hands-on data analyticsn training
  • Data analytics training Contains real word business applications and examples. Project work at the end of each module
  • Rich material and handouts for student reference.

data science iconCOURSE BENEFITS

  • Gain exposure to key disciplines and skills needed to fulfill the role of a business analyst
  • predictive modeler / data scientist
  • Build predictive models using linear, logistic regression and decision trees
  • Build machine learning models using Neural nets, SVM and Random forest

data sciencePREREQUISITES

  • Before attending this course, candidate should Have experience using applications, such as SAS/R/word processors/spreadsheets
  • No statistical background is necessary
  • Data Analysis and Reporting background is necessary
  • This is a 100% hands on training. Every participant should have access to a computer.
  • Introduction to SAS
  • Types of Libraries and Variables
  • Data –Reading ,Writing ,Importing and Exporting
  • Functions and Options
  • Conditional Statements and Logical Operators
  • Datasets –Introduction ,Appending ,Merging and Sorting
  • Report Generation ,Data set Manipulation
  • Introduction to Databases ,RDBMS Concepts
  • Structured Query Lanauage
  • Introduction to Statistics
  • Graphical and Tabular Descriptive Statistics
  • Probability
  • Probability Distribution
  • Hypothesis Testing
  • Statistical Tests(Z-Test, Chi-Square, T-Tests,etc.)
  • Introduction Analytics Tool(R)
  • Introduction to Data Analysis
  • Introduction to R programming
  • R Environment and Basic Commands
  • Importing data
  • Sampling
  • Data Exploration
  • Creating calculated fields
  • Sorting & removing duplicates
  • Population and Sample
  • Measures of Central tendency
  • Measures of dispersion
  • Percentiles & Quartiles
  • Box plots and outlier detection
  • Creating Graphs and Reporting
  • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers identification
  • Data Cleaning
  • Basic Descriptive statistics
  • Correlation
  • Simple Regression models
  • R-Square
  • Multiple regression
  • Multicollinearity
  • Individual Variable Impact
  • Need of logistic Regression
  • Logistic regression models
  • Validation of logistic regression models
  • Multicollinearity in logistic regression
  • Individual Impact of variables
  • Confusion Matrix
  • Segmentation
  • Entropy
  • Building Decision Trees
  • Validation of Trees
  • Fine tuning and Prediction using Trees
  • How to validate a model?
  • What is a best model?
  • Types of data d. Types of errors
  • The problem of over fitting
  • The problem of under fitting
  • Bias Variance Tradeoff
  • Cross validation
  • Boot strapping
  • Objective
  • Model building-1
  • Model building-2
  • Model validation
  • Variable selection
  • Model calibration
  • Out of time validation
  • Neural network Intuition
  • Neural network and vocabulary
  • Neural network algorithm
  • Math behind neural network algorithm
  • Building the neural networks
  • Validating the neural network model
  • Neural network applications
  • Image recognition using neural networks
  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion
  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion
  • Objective
  • ML Model-1
  • ML Model-2
  • What is Python & History?
  • Installing Python & Python Environment
  • Basic commands in Python
  • Data Types and Operations
  • Python packages
  • Loops
  • My first python program
  • If-then- else statement
  • Data importing
  • Working with datasets
  • Manipulating the datasets
  • Creating new variables
  • Exporting the datasets into external files
  • Data Merging
  • Taking a random sample from data
  • Descriptive statistics
  • Central Tendency
  • Variance e. Quartiles, Percentiles
  • Box Plots
  • Graphs
  • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers identification
  • Data Cleaning
  • Basic Descriptive statistics
  • Correlation
  • Simple Regression models
  • R-Square
  • Multiple regression
  • Multicollinearity
  • Individual Variable Impact
  • Need of logistic Regression
  • b. Logistic regression models
  • c. Validation of logistic regression models
  • d. Multicollinearity in logistic regression
  • e. Individual Impact of variables
  • f. Confusion Matrix
  • Segmentation
  • Entropy
  • Building Decision Trees
  • Validation of Trees
  • Fine tuning and Prediction using Trees
  • How to validate a model?
  • What is a best model?
  • Types of data
  • Types of errors
  • The problem of over fitting
  • The problem of under fitting
  • Bias Variance Tradeoff
  • Cross validation
  • Boot strapping
  • Neural Networks
  • Neural network Intuition
  • Neural network and vocabulary
  • Neural network algorithm
  • Math behind neural network algorithm
  • Building the neural networks
  • Validating the neural network model
  • Neural network applications
  • Image recognition using neural networks
  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion
  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion
  • Objective
  • ML Model-1
  • ML Model-2
  • Data Science Hackathon / Competition
  • Data exploration
  • Model building
  • Testing the score and rank
  • Variable selection
  • Future reengineering
  • Checking the score and rank
  • Final Submission

LOCATION ON MAP:

Postal Address:

No. 270, Sigma Arcade, RSI Business Complex, 1st Floor,
Marathahalli, Near Tulasi theater, Marathahalli Junction, Bangalore-560037

CONTACT INFO:

Phone:

+91 9739993935, 9739993934

Skype:

royal.indra1

Email:

contact@indrasacademy.com

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