# Data Science Training in Hyderabad

Data Science is considered as the new arena, which is the most emerging technology that can easily enhance the Organizational growth. Data Administration and Management is being the biggest challenges that can face real time challenges in the explosion of happening these days.

### What is Data Science?

Data Science is the software library framework which allows for the distributing processing large sets of data across a cluster of computers by using simple programming tools. It can easily scale up from a single server to thousands of machines in an easy manner.

### About Data Science course

ORIEN IT is the most reputed **Data Science Training Institute in Hyderabad** that provides quality and comprehensive training to the students. It helps in surveying the foundational topics as per the current IT Industry. The entire course is divided into various major sections which are data manipulation, data at scale while working with big data, data analysis with machine learning and statistics and data communication with informative visualization. Our main aim is to provide In-depth knowledge on each and every topic as per current IT field.

### What are Prerequisites and Requirements of Data Scientist?

Candidates will acquire 100 percent subject knowledge skill set by the end of the Data Science course in Hyderabad.

- Candidates who are actively interested in the field of Data Science
- Candidates who are having knowledge in area of intro level statistics
- Basic knowledge in Python programming experience
- Candidates who are having basic knowledge in programming concepts such as Functions, Variables, Basic Python data structures, and loops. Some of the structures are like list and dictionaries.

It will be added an advantage for the candidates who are having subject knowledge in quantitative skills and technical background.

### Who should prefer this course?

ORIEN IT is the most reputed Data Science Institute in Hyderabad that provides good support to the desired aspirants. There is great scope for every IT enthusiastic aspirant who is looking for this newly growing field.

- Managers
- Operators
- Freshers or Graduates
- Data analysts
- Business analysts
- Job Seekers
- Developers
- End users
- IT professionals

### Learning Outcomes of Data Science Training Hyderabad

Candidates will acquire subject knowledge skill set with real time scenarios for easy understanding.

- Enhances skills about Business Intelligence and Business Analysis
- Knowledge in descriptive statistics of Data Analysis
- Excel with working with Tableau
- Skills in introduction and Data exploration to R
- Grasps knowledge in creation of Decision Trees
- Skills in importance of Big Data Technologies
- Knowledge in loop functions and debugging tools

### Best Data Scientist Training in Hyderabad

Data Scientist is considered as the hottest future career to equip with relevant skill set to rule the current IT Industry. We provide strong theoretical and practical knowledge skills with best real time examples in this Data Science Training in Hyderabad. Project Oriented **Data Scientist Training in Hyderabad **is provided to the students to acquire the best job in the MNCs. We make the candidates as ‘Job-ready’ to enhance professional career growth. The classes comprise of various lecture videos in every aspect to enrich subject knowledge skills. Standalone Data Science Training in Hyderabad is ensured to the students to boost up the career graph.

### MODULE 1: INTRODUCTION

- 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

### MODULE 2: R & R STUDIO SOFTWARE

- 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

### MODULE 3: DATA EXPLORATION USING STATISTICAL METHODS

- 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
- ANNOVA
- Z test
- Correlation & Covariance
- Multicollinearity

- Model Validation/Performance evaluation

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

### MODULE 4: MACHINE LEARNING

- 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

### MODULE 5: TEST MINING

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

### MODULE 6: HADOOP ECOSYSTEMS

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

### MODULE 7: PYTHON PROGRAMMING

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

__WORKSHOP__

- REAL TIME LIVE PROJECTS
- RESUME PREPARATION ASSISTANCE
- INTERVIEW QUESTION & ANSWER DISCUSSIONS

### raavi

### palanivel

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