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Data Science Training in Hyderabad


Data Science, Statistics with R & Python: This course is an introduction to Data Science and Statistics using the R programming language with Python training in Hyderabad.. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R and Python. If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems. – PowerPoint PPT presentation

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Title: Data Science Training in Hyderabad

Data Science Data Analytics Training in
Hyderabad Call_at_
7993762900-GENIUS IT
Address behind mythrivanam, Ameerpet, Telangana
500038 Website
What Is Data Science This course is an
introduction to Data Science and Statistics using
the R programming language with Python training
in Hyderabad.. It covers both the theoretical
aspects of Statistical concepts and the practical
implementation using R and Python.
Why Choose Data Science The IT industry is
expecting to add around 180000200000 fresh job
vacancies that are related to recent technologies
like Data Science and Machine Learning This job
profile offers great opportunities to freshers
who have the relevant skills and they have an
extremely bright future ahead A large no. of
Indian developers are running towards data
science since it the most in-trend job role and
is expected to have a great future ahead as
well. As per Team Lease Services - a popular
staffing solutions co. - by the year 2020, India
will face a demand-supply gap of 2,00,00 data
analytics professionals
Data science Course ContentIntroduction to
Data Science
  • Introduction to Data Analytics
  • Introduction to Business Analytics
  • Understanding Business Applications
  • Data types and data Models
  • Type of Business Analytics
  • Evolution of Analytics
  • Data Science Components
  • Data Scientist Skillset
  • Univariate Data Analysis
  • Introduction to Sampling

  • Basic Operations in R Programming
  • Introduction to R programming
  • Types of Objects in R
  • Naming standards in R
  • Creating Objects in R
  • Data Structure in R
  • Matrix, Data Frame, String, Vectors
  • Understanding Vectors Data input in R
  • Lists, Data Elements
  • Creating Data Files using R

  • Data Handling in R Programming
  • Basic Operations in R Expressions, Constant
    Values, Arithmetic, Function Calls, Symbols
  • Sub-setting Data
  • Selecting (Keeping) Variables
  • Excluding (Dropping) Variables
  • Selecting Observations and Selection using Subset
  • Merging Data
  • Sorting Data
  • Adding Rows
  • Visualization using R
  • Data Type Conversion
  • Built-In Numeric Functions
  • Built-In Character Functions
  • User Built Functions
  • Control Structures
  • Loop Functions

  • Introduction to Statistics
  • Basic Statistics
  • Measure of central tendency
  • Types of Distributions
  • Anova
  • F-Test
  • Central Limit Theorem applications
  • Types of variables
  • Relationships between variables
  • Central Tendency
  • Measures of Central Tendency
  • Kurtosis
  • Skewness
  • Arithmetic Mean / Average
  • Merits Demerits of Arithmetic Mean
  • Mode, Merits Demerits of Mode
  • Median, Merits Demerits of Median
  • Range

  • Introduction to Probability
  • Standard Normal Distribution
  • Normal Distribution
  • Geometric Distribution
  • Poisson Distribution
  • Binomial Distribution
  • Parameters vs. Statistics
  • Probability Mass Function
  • Random Variable
  • Conditional Probability and Independence
  • Unions and Intersections
  • Finding Probability of dataset
  • Probability Terminology
  • Probability Distributions

  • Introduction to Machine Learning
  • Overview Terminologies
  • What is Machine Learning?
  • Why Learn?
  • When is Learning required?
  • Data Mining
  • Application Areas and Roles
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement learning

  • Machine Learning Concepts Terminologies
  • Steps in developing a Machine Learning
  • Key tasks of Machine Learning
  • Modeling Terminologies
  • Learning a Class from Examples
  • Probability and Inference
  • PAC (Probably Approximately Correct) Learning
  • Noise
  • Noise and Model Complexity
  • Triple Trade-Off
  • Association Rules
  • Association Measures

  • Regression Techniques
  • Concept of Regression
  • Best Fitting line
  • Simple Linear Regression
  • Building regression models using excel
  • Coefficient of determination (R- Squared)
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Variable transformation
  • Reading coefficients in MLR
  • Multicollinearity
  • VIF
  • Methods of building Linear regression model in R
  • Model validation techniques
  • Cooks Distance
  • Q-Q Plot
  • Durbin- Watson Test

  • Market Basket Analysis
  • Applications of Market Basket Analysis
  • What is association Rules
  • Overview of Apriori algorithm
  • Key terminologies in MBA
  • Support
  • Confidence
  • Lift
  • Model building for MBA
  • Transforming sales data to suit MBA
  • MBA Rule selection
  • Ensemble modelling applications using MBA

  • Time Series Analysis (Forecasting)
  • Model building using ARIMA, ARIMAX, SARIMAX
  • Data De-trending data differencing
  • KPSS Test
  • Dickey Fuller Test
  • Concept of stationarity
  • Model building using exponential smoothing
  • Model building using simple moving average
  • Time series analysis techniques
  • Components of time series
  • Prerequisites for time series analysis
  • Concept of Time series data
  • Applications of Forecasting

  • Decision Trees using R
  • Understanding the Concept
  • Internal decision nodes
  • Terminal leaves.
  • Tree induction Construction of the tree
  • Classification Trees
  • Entropy
  • Selecting Attribute
  • Information Gain
  • Partially learned tree
  • Overfitting
  • Causes for over fitting
  • Overfitting Prevention (Pruning) Methods
  • Reduced Error Pruning
  • Decision trees Advantages Drawbacks
  • Ensemble Models

  • K Means Clustering
  • Parametric Methods Recap
  • Clustering
  • Direct Clustering Method
  • Mixture densities
  • Classes v/s Clusters
  • Hierarchical Clustering
  • Dendogram interpretation
  • Non-Hierarchical Clustering
  • K-Means
  • Distance Metrics
  • K-Means Algorithm
  • K-Means Objective
  • Color Quantization
  • Vector Quantization

  • Tableau Analytics
  • Tableau Introduction
  • Data connection to Tableau
  • Calculated fields, hierarchy, parameters, sets,
    groups in Tableau
  • Various visualizations Techniques in Tableau
  • Map based visualization using Tableau
  • Reference Lines
  • Adding Totals, sub totals, Captions
  • Advanced Formatting Options
  • Using Combined Field
  • Show Filter Use various filter options
  • Data Sorting
  • Create Combined Field
  • Table Calculations
  • Creating Tableau Dashboard
  • Action Filters
  • Creating Story using Tableau

  • Analytics using Tableau
  • Clustering using Tableau
  • Time series analysis using Tableau
  • Simple Linear Regression using Tableau
  • R integration in Tableau
  • Integrating R code with Tableau
  • Creating statistical model with dynamic inputs
  • Visualizing R output in Tableau
  • Case Study 1- Real time project with Twitter Data
  • Case Study 2- Real time project with Google
  • Case Study 3- Real time project with IMDB Website