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Data Science Course In Hyderabad

Data Science Course In Hyderabad

Data Science Course is a new arena. Across the globe, different industries face a dearth of Data Science Course In Hyderabad, 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 Orapro Technologies Pvt. Ltd.Hyderabad. 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.

Data Science Course In Hyderabad

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:


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

Click on the link for more information:


Helps to fulfill a role

In case, you are planning to pursue any Data Science 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 Data Science 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


  • 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


  1. When we create a directory via the name node, how can we identify which storage node the directory is physically located?
    Is the directory created on all storage nodes?

  2. Difference between Data Scientist vs Data Engineer?

  3. Data Engineer is part of data science. A good scientist can do everything the engineer can do as well. That’s why real data scientists are like unicorns.The current usage of the term data scientist is so broad that it encompasses all the skills.

  4. Hi,
    I am analyzing a waste generation data of a small municipality.Here, a dustbin is used which can send the level it’s filled up to, i.e. Level 1, Level 2, Level 3. Here I have different attributes like:
    The Region, Locality, Street, Date and Time stamp, Dustbin ID, Level_filled, weekday.
    What are the different type of analysis and algorithms I can put on this data set to generate some good conclusions?

  5. Is anyone of you aware of interactive data presentation tools/software?

  6. I am interested to do my research on big data. Can any one help me to find the problem in it?

  7. Hi!
    On data locality the data that is stored on another node of a Hadoop cluster or any Database?
    The code that is sent to the machine is a MapReduce job?
    Thanks for your support!

  8. you may look into problem in two aspects,
    1. bird eye view considering industry => sector => company => specific product/services
    2. Directly you may jump to the niche area like sports, political impact, weather forecast, geographic etc.
    However going ahead you may concentrate on volume of data in respective concern and modulate according to the improvement..
    A) in sports; to understand the batsman or bowler strategy quickly,
    B) in banking sector fraudulent cases of credit cards so how to avoid the same by using Big Data Analysis
    C) in Aviation industry, booking and cancellation, alerts etc can be managed by Big Data implementation, so on so forth..

  9. It is very important what kind of data u want to represent & the way u want to represent, more important is the analytical output u need to draw from that data. Tools are many such as
    Tableau, Qlikview, Spitfire & Google Charts API along with Google – Interactive data visualization tools.

    Qlikview – Too much a typical BI types tool and visualization really sucks, boring charts and all that stuff (Not recommended for ad-hoc analysis) Cost – $$$ Efforts: ###

    D3 – Awesome tool for people who like to code and have some familiarity with web programming (Truly recommended) Cost: Open Source Efforts: ##

    R – One word.. Mmmuah !! Possibilities are infinite Cost: Open Source Efforts: ####

    Tableau – Cool! Cool ! Cool ! Nice clean color rich charts and visualization. Works pretty well with large DWs and you can play around Cost: $$$ Efforts: ## Hope that helps you choose your right tool

  10. Think about %utilization in each of the cities, regions etc., You can also find out the time when most of the dustbins are filled. Think also about social awareness in using dustbin…. This can help us find out the best time to install the dustbins every day if the utilization is less…

  11. From one of my friends, I got an understanding that SAS is very demanding right now… Would you suggest me to learn SAS over R or otherwise?

  12. which software is generally used in data mining now days like for Stata, R for statistical analysis ??I am the novice in this field and want to start with a software. please someone tell me about the software and any fruitful site for learning

  13. Kindly i want to know about data mining…..

  14. Hi….i just wanted to do analysis on online marketing campaign data…can suggest me how to proceed…

  15. Has Can you solve any classification problem for an uneven data set?

  16. KNN is a good classifier for such data sets but remembers to train on entire data set as bootstrapping doesn’t work well with KNN. Keep K lower than number of event instances, but not too low as well Alternatively you can use a sampling method to sample rare classes more ( oversampling) and use the random forest.

  17. Begin with tracking opens and clickthroughs. Then proceed with who buys and what they buy. Measure each against the mailed population and population segments. See what’s significant.

  18. Data mining is like mining in the mountains of data and comes out with precious minerals in the form of end results.

  19. R is the good platform which could look after, as this is open source and many companies do make us of the same. Apart from that SAS can be considered. But it requires licence version, if your company has an availability of the same then it would be great. Going ahead there are good tools which can be use for Data Visualization like Tableau, Qlikview, IBM Cognos etc. which would have added advantage. On youtube, there are videos available for self-learning. Take the inputs and go ahead.

  20. For SAS use kickass.to you shall find it there. If looking for a job right now learn SAS. Want to learn stats and data science completely learn R and Python(pandas,scikit).

  21. I have a query…..I am currently working on SPSS on a survey data…..It Contains Many Missing value’s……… Its is Not a Random sample(MNAR)…..what method should I use to replace Missing data?

  22. Before computing missing values, I ask, “Why are they missing?” This can be especially important with survey data.

    Often a missing value is a valid value. There may be a valid reason why it exists. If that’s the case, it deserves a value that’s different from other value possibilities.

    To illustrate, suppose a survey asked about the marital status and gave two options … “Married” or “Single”. These two options omit other possibilities such as “Widowed” and “Cohabiting”.

    Another, more common example is that surveys skip around based on an answer to a question. For example, if you’re married, you may be asked one block of questions and if you’re single, you may skip to a different block of questions.

  23. I did my Master’s in Math and Bachelor’s in Education. Am working as Math Instructor in Mississippi State. I would like to take the coaching and take the data science as my career. Could you tell me the process of your training and placement??

  24. If you like to know on which data node any file or folder, click on the link “Browse File System” and if you notice the address bar, it would show on which data node the file or the directory was created.
    Hope this answers the question.

  25. The data would be one a file or a database and it need not have to be part of the Hadoop cluster.
    Yes, it would be a MapReduce job.
    Hope this answers your question, please do let me know if you need any further clarification.

  26. can any one tell me differnce between hadoop and SAP-Hana

  27. Hi Team,

    I want to do big data scientist course.What are the opportunities available and will you provide placement after the course is completed? Kindly send me details regarding this to my mail address.


  28. Hi,
    I completed my masters with statistics..I want to know about data analyst and want learn this..can pls send me the regardings about that..

  29. is it job with placement and project.duration of course

  30. Would like to know more about this course

  31. I am working in Bank in Analytic Team,Interested in Data Scientist Course. How will the same course will boost my career.

  32. Hello
    I am a B.tech graduate (2012 passed out ) having 59.25 aggregate (II class) can I eligible for job if I got trained on Hadoop.
    please reply.

  33. Course detail send

  34. Hello,

    i have completed my MBA(no Maths). what course can you offer for me.Can i do a Data analyst?.

  35. Hi Sir,

    Thanks for the information. I have been through the documents and also your website. In the Data Scientist course there are many concepts related to Hadoop. I’m ready to pay 30000/- but just want to check if I can first attend the Haddop sessions and later the Data Scientist one. Can that be possible? Actually I’m a fresher with zero knowledge in IT. I’ve also been through the websites of other institutes, but in there curriculum they are concentrating a lot on Hadoop. So I think its better to learn both the concepts, as Hadoop is also an hot cake in the market want to learn both of them.


  36. Hello,
    I am from Mumbai.
    I have viewed the pdfs(Data Scientist training) sent by you in the last email. Can you please guide me looking at my following points?
    1)My qualifications are- B.E CIVIL, Master of Engg(Structural Engg. I have non-IT work experience of more than 12years in Structural Engg( Analysis and design of RCC buildings)
    2) I am looking for a complete career change in Data Science.
    3) I can start as a fresher.
    4) Does the industry accept non-IT workforce if he/she has training in Data science?

    Since you are in this field I thought you can guide me so that I can take an informed decision and make a new beginning if possible.

    Thanking you in anticipation.


  37. I am interested in online Big Data Hadoop Developer course in Hyderabad.please send demo and other details.Is it java is mandatory for Hadoop Developer?

  38. I would like to know more about Data Science program.I am from a non-IT background.
    Need info on Biga data science training..
    1. Course Material/ Content
    2. Course schedule – online
    3. Course schedule – classroom
    4. Weekend course schedule …

  39. Hi

    I am interested in taking/learning “Data science” training. May I know how long is the training? Is there training in August 2016

  40. Hi I wanted to be data scientist , only ur course structure is enough to get placed as data scientist or any other courses or experience required

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