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About Data Science Course Training

Faculty : Realtime experience

Data Science is one of the most encouraging vocation streams. It includes the examination of immense measures of information for different experiences and applications. You can get Data Science Training in Hyderabad and become an information researcher. An information science course in Hyderabad can give preparing in various programming dialects, including Python and R. Notwithstanding, you ought to pick an information researcher course in Hyderabad after careful examination. A respectable Data Science Course in Hyderabad, can offer 100% percent placement Assestent.

Our training will be handled in either weekday or weekends programme depends on participant’s requirement. Here are the major topics we cover under this Data Science course Syllabus Introduction to R, Understanding R data structure, Importing data, Manipulating Data, Using functions in R, R Programming, Charts and Plots, Machine Learning Algorithm and Statistics. Every topic will be covered in mostly practical way with examples.

We are providing Data Science Online Training in Hyderabad. We are one of best Institute to provide Best High Quality Data Science online training all over India. If you are staying in Hyderabad, Bangalore, Chennai, Pune, Delhi, USA, UK, Australia, and Singapore etc. and unable to attend regular class room training programs then contact our training institute for information on online training.


Data Science Course Online Training Content :

 Introduction to Data Science

  • Data Science examples – -Netflix, Money ball, Amazon.
  • Introduction to Analytics, Types of Analytics.
  • Introduction to Analytics Methodology
  • Analytics Terminology, Analytics Tools
  • Introduction to Big Data
  • Introduction to Machine Learning


Introduction to R Programming

  • The importance of R in analytics
  • Installing R and other packages
  • Perform basic R operations
  • R Studio – Install

R Data types

  • Vectors
  • Lists
  • Matrices
  • Arrays
  • Data Frames

R variables and operators

  • Types of operators – arithmetic, relational, logical
  • Variable assignment
  • Deleting variables
  • Finding variables

R Decision Making & Loops

  • R- If statement
  • R- if….else statement
  • R- while loop
  • R- for loop

Basics, Data Understanding

  • Built-in functions in R
  • Subsetting methods
  • Summarize and structure of data
  • Head(), tail(), for inspecting data
  • Reading and Writing Data

R Vectors

  • Vector creation
  • Vector manipulation

R Arrays

  • Naming columns and Rows
  • Accessing array elements
  • Calculations across arrays

R Factors

  • Factors in data frame
  • Changing order of Levels
  • Generating Factor Levels

Preprocessing of Data

  • Handling Missing Values
  • Changing Data types
  • Data Binning Techniques
  • Dummy Variables

Modeling & Validation

  • Splitting of data – Test & Train
  • Dependent & Independent variables
  • Machine learning Algorithm
  • Error terms calculation
  • Accuracy & Precision

Data Visualization

  • Histograms
  • Bar plots
  • Line graphs
  • Customizing Graphical Parameters
  • Usage of ggplot package


Basic Statistical Concepts

  • Statistic Terminology
  • Measure of Central Tendencies
  • Measure of Dispersion

Central Limit Theorem Basic Probability

  • Probability Terminology
  • Probability Rules
  • Probability Types
  • Bayes Theorem

Understanding Distributions

  • Binomial Distribution
  • Poisson Distribution
  • Exponential Distribution
  • Normal/Gaussian Distribution
  • t – Distribution
  • Confidence interval

Advanced Statistical Concepts

  • Hypothesis Testing
  • Chi square testing
  • Z test
  • Correlation & Covariance
  • Multicollinearity

Model Validation/Performance evaluation

  • Confusion matrix
  • Calculation of accuracy, precision, recall
  • ROC and AUC
  • RMSE , MAE


Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Nonlinear Regression
  • Naïve Bayes Classification
  • Neural Network
  • Decision Trees
  • Support Vector Machines(SVM)
  • K Nearest Neighbor(KNN)
  • Lasso & Rigid regression

Unsupervised Learning

  • Concept of Clustering
  • K means Clustering
  • Hierarchical Clustering

Time Series Analysis

  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Smoothening Techniques
  • Understanding ACF & PCF plots
  • ARIMA Modeling
  • Holt – Winter Method

Optimization & Regularization

  • Gradient descent
  • Simulated Annealing
  • Genetic Algorithm – Basics
  • Dimensionality Reduction – SVD & PCA

Ensemble Method & Association rules

  • Market basket Analysis
  • Ensemble Modeling

Recommendation Engine

  • Developing recommendation engines


  • Introduction to Natural Language Processing
  • Sentimental Analysis
  • Text Classification


  • Introduction to Hadoop ecosystems
  • Map Reduce
  • Hive & Pig
  • NoSQL – Hbase
  • Kafka ,Flume ,Sqoop
  • Hadoop machine learning : Mahout


  • Data types and Data Structures
  • Concept of Modules
  • Introduction to pandas , scikit – learn , NumPy
  • Machine learning in Python





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