datascience training in hyderabad

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desktop systems. Genius IT provides comprehensive Data Science Course in Hyderabad with extensive statistical concepts, wide-ranging Machine Learning classes in Hyderabad and unlimited hands-on practice sessions in R and Python along with adequate placement support post completion. Later one may also opt for project internship programmer, to acquire multiple real-life project experience along with supporting project experience certificate, which helps strengthening the credential and assisting in placement further. Faculties at Genius IT are senior Data Scientists from the industry with extensive implementation experience and most of them are qualified from premium institutions like IIT, IIM, IIS, BITS-Pilani etc.


Presentation Transcript

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Data Science & Data Analytics Training in Hyderabad Call@ 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.:

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.

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Why Choose Data Science: The IT industry is expecting to add around 180000–200000 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 Content: Introduction to Data Science:

Data science Course Content: Introduction 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

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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

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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 Function 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

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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 Concept of Quantiles , Quartiles, percentile

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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

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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

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Machine Learning Concepts & Terminologies Steps in developing a Machine Learning application 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

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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

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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

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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

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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

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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

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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

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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 Analytics Case Study 2- Real time project with Google Finance Case Study 3- Real time project with IMDB Website

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