Opt For Best Data Scientist Course In Hyderabad And Pursue A Lucrative Salary

Data Analytics is a new arena. Across the globe, different industries face a dearth of Data Scientists, and it will continue for another couple of years. As per a recent survey, organization snow invests in Big Data to support Hadoop and Data Scientist talent. With training in Big Data, you can create a niche for yourself in this arena. You can avail training from RCP Technologies Pvt. Ltd. We provide comprehensive training on Bigdata Scientist  so that professionals can get an edge. Healthcare, life sciences, retail, and IT companies recruit data scientists. With our training, you can find a job in this emerging field.We are a pioneer in this arena of training and the content caters to the need of the industry.Our experts ensure that students can get an insight of the technology.

 

Demand for Data Science

In this era of Data world, Data Science is necessary for processing large volumes of data.

An average number of jobs available in Naukri every month related to Data Science are 75000+ in India. Please click on the link for more details:

naukri.com/data-science-jobs

Harvard Business review also published that “Data Scientist: The Sexiest Job of the 21st Century.”

Click on the link for more information:

hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

Helps to fulfill a role

In case, you are planning to pursue any Data Scientist course in Hyderabad then opt for our training program immediately. We will help you to improve your career graph. Before attending any demo session, you can speak to one of our consultants. Our inputs can help you to gain an edge in your career. This training will help you to get a lucrative salary in IT companies and other industries. A major advantage of our course is that you do not require any pre-requisites. This makes it easy for the newbies.

As part of the job assistance, we help trainees in preparing the resume and provide support for telephonic interview. We offer job support from us in initial days of your career as Data Scientist.

A blend of theory and practical classes provides scope to participants to real-time projects on analytics. The general milieu of topics is covered in the curriculum so that you can pursue a career in this arena. Data Scientists usually have a dual role to paly of an artist and analyst in different settings. Experts utilize data and infer spot trends. We use various advanced tools to equip participants with the necessary expertise. Our course comprises are perfect for

  • Any graduates who want to pursue a career in Data Science
  • People who love numbers

 

A comprehensive curriculum

Each participant gets individual attention from our experts. In each batch, only twenty students are allowed, so you can book your seat in advance. What makes our training program different from others is its content. Professionals and Data Scientists who have extensive experience in this arena design the curriculum. Our experts have profound expertise in this field and develop the course that ensures the each participant is equipped with skill and knowledge of being a data scientist. In the case of any doubt, you can get it solved by our experts. We conduct regular tests and debates to assess knowledge of students and prepare them for a successful career. You can get hands-on experience with the live projects.

Few important features

The design of the training course makes it perfect for both beginners and professionals of advanced level. Once you enroll for our course, we will provide you a series of a tutorial where you can even learn the practical application of this technology. We assure, with our training, you can extract necessary inferences from data in different industries. Our experts maintain the quality of this course. Few familiar features of our training program are

  • It is informative and concise.
  • Training materials are provided.
  • Experts for the better understanding of students simplify complex issues in a layman language
  • The case study was driven approach.

Emphasis on practical training

Hands-on training plays a significant role in this practice. Our method of catering information is unique. We follow practices that make the learning process easy for students. With our training becoming a Data Scientist will not be a daunting task. You can get an insight of best practices in this arena through our course. Every student gets a chance to work on a case study and present to his/her peers every week. So that he/she will get an experience of communicating the inferences to his team. Topics covered by this course are divided into five modules

 

  •  Introduction to Business Intelligence and Business Analytics
  •  Data Preparation
  • Data Exploration
  •  Analytical methodologies
  •  Introduction to coding practices of R
  •  Clustering
  •  Decision Trees
  •  Practical Project on Retail, Web and Financial Analytics

 

Different types of courses

You can opt for regular courses or online training sessions depending on your need and time constraints if any. Duration of the classroom teaching is of forty-five days. There are also weekend training, and it lasts for six weeks. Three hours course are conducted on each Saturday and Sunday; this has proved to be beneficial for professionals. Again, there is also a provision for online training. Date of commencement of these training programs is mentioned in our websites, and you can have a look at it. Irrespective of the training you choose, you can free course on Java.

 

Boost for your career

In case, you are planning to pursue any scientist data course in Hyderabad then opt for our training program immediately. We will help you to improve your career graph. Before attending any demo session, you can speak to one of our consultants. Our inputs can help you to gain an edge in your career. This training will help you to get a lucrative salary in IT companies and other industries. A major advantage of our course is that you do not require any pre-requisites. This makes it easy for the newbies.

 

At a competitive price

You can also opt for certification courses from our training institute. Well, you do not have to shell out an exorbitant amount for this training program. Prices might vary between short-term programs, classroom training, online courses and weekend batches. Your relationship with students does not end only with the training program, as we also provide assistance for placement in various leading IT companies. As part of the job assistance, we help trainees in preparing the resume and provide support for telephonic interview. If you want, you can also opt for after job support from us.

 

A noted name

With the advent of technology, IT professionals need to stay abreast with different changes. Our training will equip you with required knowledge and dexterity for dealing with Big Data with appropriate Hadoop training. We guarantee that no other institute will impart you multiple modules and concepts as HDFS, SQOOP, PIG, ZOOKEEPER, HIVE, HBASE and Map-Reduce. Owing to our comprehensive approach, we are now a prominent name as a training institute. Use of different tools, course content, and practical training will help you to pursue a career as Data Scientist and be a part of the bandwagon, so enroll in the course fast.

 

  • Acquire the sexiest job of a 21st century, Learn Data Science to became a Data Scientist.
  • A course designed by Data Expert, who have more than 15 years of Experience from NIT and ISI, The Best rated schools for Analytics in India, to provide the knowledge and skills in the field of Data Science and train you to become a successful Data Scientist.
  •  Data Scientist Trainer Profile:
Qualified faculty from
NIT ( National Institute of Technology)
ISI ( Indian Statistical Institute )
16 + years of experience

Our faculty is qualified from reputed institutes Indian Statistical Institute (ISI) and National Institute of Technology(NIT) and have 16+ years of real time experience. He worked for MNC’s WIPRO/GE/HP/WELLS FARGO in his career.

 

  • This 140  hour Data Science course is designed to provide knowledge and skills to become a successful Data Scientist.
  • It is the right time to learn Data Science because:

1. We are in the Big Data era where Data Science is a promising field to harness and process huge volumes of data.

 

2. The data scientist has a dual role – An Analyst and an Artist, Data scientists are very curious, who love a large amount of data, and they like to play with such extensive data to make inferences and spot trends.

 

3. Data Science is an emerging field; there are a lot of opportunities available across the globe. Just browse through any of the job portals, some job openings available for Data scientists in different industries, like IT or Healthcare, Retail or Life Sciences, etc.

 Data scientist Course Content:

BigData Science

Course Overview:

Module: 1 – Descriptive & Inferential Statistics (30Hrs)

Module: 2 – Prediction Analytics (25Hrs)

Module: 3 – Applied Multivariate Analysis (25hrs)

Module: 4 – Machine Learning (30hrs)

Module: 5 – R-Programming (30hrs)

           

About the Course:

In this course, you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas of turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like R Programming, SAS, MINITAB and EXCEL.

Course features:

  • 140+ hours of teaching
  • Exam on every weekend
  • Exclusive doubt clarification session on every weekend
  • Real-Time Case Study drove approach
  • Live Project
  • Placement assistance

Qualification

  • Any Graduate. No programming and statistics knowledge or skills required

Duration of the course:

  • Three months (Every day 2 hours of teaching).
  • Classes on weekdays.

 

·         Module: 1 – Descriptive & Inferential Statistics (30Hrs)
·         Turning Data into Information

·         Data Visualization

·         Measures of Central Tendency

·         Measures of Variability

·         Measures of Shape

·         Covariance, Correlation

·         Using Software-Real Time Problems

·         Probability Distributions

·         Probability Distributions: Discrete Random Variables

·         Mean, Expected Value

·          Binomial Random Variable

·         Poisson Random Variable

·         Continuous Random Variable

·         Normal distribution

·         Using Software-Real Time Problems

·         Sampling Distributions

·         Central Limit Theorem

·         Sampling Distributions for Sample Proportion, p-hat

·          Sampling Distribution of the Sample Mean, x-bar

·         Using Software-Real Time Problems

·         Confidence Intervals

·         Statistical Inference

·         Constructing confidence intervals to estimate a population Mean, Variance, Proportion

·         Using Software-Real Time Problems

·         Hypothesis Testing

·         Hypothesis Testing

·         Type I and Type II Errors

·         Decision Making in Hypothesis Testing

·          Hypothesis Testing for a Mean, Variance, Proportion

·         Power in Hypothesis Testing

·         Using Software-Real Time Problems

·         Comparing Two Groups

·         Comparing Two Groups

·         Comparing Two Independent Means, Proportions

·         Pairs wise Testing for Means

·          Two Variances Test(F-Test)

·         Using Software-Real Time Problems

·         Analysis of Variance (ANOVA)

·         One-Way and Two-wayANOVA

·         ANOVA Assumptions

·         Multiple Comparisons (Tukey, Dunnett)

·         Using Software-Real Time Problems

·         Association Between Categorical Variables

·         Two Categorical Variables Relation

·         Statistical Significance of Observed Relationship / Chi-Square Test

·         Calculating the Chi-Square Test Statistic

·          Contingency Table

·         Using Software-Real Time Problems

 

 

·         Module: 2 – Prediction Analytics (25Hrs)
·         Simple Linear Regression

·         Simple Linear Regression Model

·         Least-Square Estimation of the Parameters

·         Hypothesis Testing on the Slope and Intercept

·         Coefficient of Determination

·         Estimation by Maximum Likelihood

·         Using Software-Real Time

·         Multiple Regression

·         Multiple Regression Models

·         Estimation of Model Parameters

·         Hypothesis Testing in Multiple Linear Regression

·         Multicollinearity

·         Using Software-Real Time Problems

·         Model Adequacy Checking

·         Residual Analysis

·         The PRESS Statistic

·         Detection and Treatment of Outliers

·         Lack of Fit of the Regression Model

·         Using Software-Real Time Problems

·         Transformations

·          Variance-Stabilizing Transformations

·         Transformations to Linearize the Model

·         Analytical Methods for selecting a Transformation

·         Generalized and Weighted Least Squares

·         Using Software-Real Time Problems

·          Multiple Linear Regression

·         The Multiple Linear Regression Model

·         Using Software-Real Time Problems

 

   Diagnostics for Leverage and Influence

·         Leverage/Cook’s  D/DFFITS/DFBETAS

·         Treatment of Influential Observations

·         Using Software-Real Time Problems

·        Polynomial Regression

·         Polynomial Model in One/Two /More Variable

·         Orthogonal Polynomials

·          Using Software-Real Time Problems

·        Dummy Variables

·         The General Concept of Indicator Variables

·         Using Software-Real Time Problems

·         Variables Selection and Model Building

·         Forward Selection/Backward Elimination

·         Stepwise Regression

·         Using Software-Real Time Problems

·         Generalized Linear Models

·         Concept of GLM

·         Logistic Regression

·         Poisson Regression

·         Negative Binomial Regression

·         Exponential Regression

·         Autocorrelation

·         Regression Models with Autocorrelation Errors

 

 

 

 

 

·         Module: 3 – Applied Multivariate Analysis (25hrs)
·    Measures of Central Tendency, Dispersion, and Association

·         Measures of Central Tendency/ Measures of Dispersion

·         Using Software-Real Time Problems

·         Multivariate Normal Distribution

·         Exponent of Multivariate Normal Distribution

·          Multivariate Normality and Outliers

·         Eigenvalues and Eigenvectors

·         Spectral Value Decomposition

·         Single Value Decomposition

·         Using Software-Real Time Problems

·  Sample Mean Vector and Sample Correlation

·         Distribution of Sample Mean Vector

·         Interval Estimate of Population Mean

·         Inferences from Correlations

·         Using Software-Real Time Problems

·         Principal Components Analysis (PCA)

·         Principal Component Analysis (PCA) Procedure

·         Using Software-Real Time Problems

·          Factor Analysis

·         Principal Component Method

·         Communalities

·          Factor Rotations

·         Varimax Rotation

     Using Software-Real Time Problem

·          Discriminant Analysis

·         Discriminant Analysis (Linear/Quadratic)

·          Estimating Misclassification Probabilities

·          Using Software-Real Time Problems

·         MANOVA

·          MANOVA

·         Test Statistics for MANOVA

·          Hypothesis Tests

·          MANOVA table

·         Using Software-Real Time Problems

 

 

·         Module: 4 – Machine Learning (30hrs)
·          Introduction

·          Application Examples

·          Supervised Learning

·         Unsupervised Learning

·          Regression Shrinkage Methods

·         Ridge Regression

·         Lasso Regression

·         Using Software-Real Time Problems

·          Classification

·         Logistic Regression

·         Bayes Rule and Classification Problem

·          Discriminant Analysis(LDA/QDA)

·         Nearest-Neighbor Methods (K-NN Classifier)

·         Using Software-Real Time Problems

·          Tree-based Methods

·         The Basics of Decision Trees

·         Regression Trees

·         Classification Trees

·         Ensemble Methods

·         Bagging, Bootstrap, Random Forests

·         Using Software-Real Time Problems

·         Neural Networks

·         Introduction

·         Single Layer Perceptron

·         Multilayer Perceptron

·         Forward Feed and Backward Propagation

·         Using Software-Real Time Problems

 

·         Support Vector Machine

·         Maximum Marginal Classifier

·         Support Vector Classifier

·         Support Vector Machine

·         SVMs with More than Two Classes

·         Using Software-Real Time Problems

·         Cluster Analysis

·         Agglomerative Hierarchical Clustering

·         K-Means Procedure

·         Meloid Cluster Analysis

·         Using Software-Real Time Problems

·         Dimensionality Reduction

·         Principal Component Analysis

·         Using Software-Real Time Problems

·         Association rules

·         Market Basket Analysis

·         Using Software-Real Time Problems

 

 

 

·         Module: 5 – R-Programming (30hrs)
·         R Programming

·         R Basics

·         Numbers, Attributes

·         Creating Vector

·         Mixing Objects

·         Explicit Coercion

·          Formatting Data Values

·         Matrices, List, Factors, Data Frames, MissingValues, Names

·         Reading and Writing Data

·         Using Dput/DDump

·         Interface to the Outside world

·         Subsetting R objects

·         Vectorized Operations

·         Dates and Times

·         Managing Data Frames with the DPLYR package

·         Control Structures

·         Functions

·         Lexical /Dynamic Scoping

·         Loop Functions

·         Debugging

 

·         Data Analytics Using/ R

·         Module 1-4 demonstrated using R programming