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Opt For Best Data Scientist Course In Hyderabad And Pursue A Lucrative SalaryData 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 ScienceIn 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-jobsHarvard 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 roleIn 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
- 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.
- Introduction to Business Intelligence and Business Analytics
- Data Preparation
- Data Exploration
- Analytical methodologies
- Introduction to coding practices of R
- Decision Trees
- Practical Project on Retail, Web and Financial Analytics
- 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 experienceOur 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:
Data scientist Course Content:BigData ScienceCourse 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
- Three months (Every day 2 hours of teaching).
- Classes on weekdays.
Module: 2 - Prediction Analytics (25Hrs)
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: 3 - Applied Multivariate Analysis (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: 4 - Machine Learning (30hrs)
· 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: 5 - R-Programming (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
· 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