R Programming with Statistics for Data Science & Machine Learning Course

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Mega Hills, Madhapur, Hyderabad - 500081

Course ID: 45603

Mega Hills, Madhapur, Hyderabad - 500081

Students Interested 0 (Seats Left 15)

₹ 18,575

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Batch 1

Date : 28 Jul, 18 - 04 Nov, 18

Timings : 10:00 AM-12:00 PM

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Batch 2

Date : 04 Aug, 18 - 11 Nov, 18

Timings : 02:00 PM-04:00 PM

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Lakshmi Padmasree

MSc Statistics, Oracle Certified Professional

About Lakshmi Padmasree

She is a technocrat with over 14 years of rich experience as a trainer and currently is the competency head at The Skill Enhancers in the Oracle, DBA and BI. She has imparted high quality training in technologies like Oracle, Teradata, MySQL, Informatica, ABinitio and COGNOS.

Areas of Expertise:
Databases (DBA) :
Oracle 8i / 9i / 10g / 11g/ 12c: SQL, PLSQL, Administration, Performance Tuning, SQL Statement Tuning, New Features, Streams, ASM, Dataguard, Real Application Clusters
Teradata Versions 11/12/13: Basics, SQL, Architecture, BTEQ & Utilities, SQL Statement Tuning
My SQL: Development and Administration
ETL: Oracle 11g : Warehouse Builder (OWB), Informatica 9 / 8.x , COGNOS, Ab Initio
Business Intelligence : Cognos 8 , Oracle OBIEE

Professional Experience:
Head Technology at The Skill Enhancers (TSE). In the past, she has worked for Wilshire Software Technologies, Software Technology International Ltd, Software Solution Integrated Ltd. and Compusoft in different technical positions involving Systems analysis, design, development and implementation of application software and training.
More than 500 programs executed for various corporates on the database, data-warehousing, Business Intelligence and Analytics.

Education:
MSc in Mathematics from Osmania University
Diploma in Citation in Application Programming.
Corporate Training conducted
Associated with more than 150 corporates, some note-worthy clients are Accenture India, Capgemini, CA, CISCO Systems, Deloitte Consulting, JDA, Oracle India, Oracle University, Wipro Technologies, Infosys Technologies, Societe Generale, Verizon, SUMTOTAL, GENPACT
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About the Course

Statistics & R Programming for Data Science


A beginnerâ??s course designed to suit professionals, students, analysts, researchers and programmers to take a dive into the wonderful world of Data Science.


Explore the founding science behind Data Analytics, Data Science, Machine Learning and Artificial Intelligence.


The course in conceptualized, designed and delivered in a structured flow that will suit any beginners from a mathematical background.


Pre-requisites:



  • Good knowledge of mathematics and entry level understanding of statistics

  • Ability to use computers and basic software like MS Excel.


Who can do?



  • Anyone with an interest on Data Science and Analytics

  • Graduates who BTech, MCA, BCom, BSc, MBA

  • Aspiring professionals seeking to change careers towards Data Analysts or Data Scientist.


What next?



  • This course builds a foundation for getting deeper into

    • data analysis< Machine Learning, Artificial Intelligence with Python

    • Integrate your analytical skills to the world of Big Data

    • Work on modern day applications like Scala, Apache Spark and Kafka




 


Duration:  4-5 weeks


Available: In class and Online


Module 1: Data Visualization, Statistics & Computational Science


Exploring Data



  • Data and Visualisation

  • Variables and levels of Measurement

  • Data matrix and frequency table

  • Graphs and shapes of Distributions

  • Measures of Central Tendency and dispersion

  • Mean, Median and Mode

  • Range, interquartile range and box plot

  • Variance and standard deviation

  • Z-scores and Example


Correlation and Regression



  • Correlation

  • Crosstabs and scatter plots

  • Regression

  • Reference


 


Probability



  • Probability and randomness

  • Sample space, events and tree diagrams

  • Quantifying probabilities with tree diagram

  • Conditional probability and independence Decision trees and Bayes â??Law


 


Probability Distributions



  • Cumulative probability distribution

  • Mean and variance of random variable

  • The mean of random variable

  • Variance of a random variable

  • The normal distribution

  • The binomial Distribution


Sampling distribution



  • Sampling distribution of sample mean and central limit theorem

  • The sampling distribution

  • The central limit theorem

  • Sampling distribution proportion


Confidence intervals



  • Inference and confidence interval for mean

  • Confidence interval and proportion and confidence levels

  • Sample size and Example


Significance Tests



  • Hypothesis and significance tests

  • Step-by-step plan and confidence interval

  • Type I and Type II errors and example

  • Significance tests


 


 


Module 2: Machine Learning With R


This Machine Learning with R dives into the basics of machine learning using an approachable, and well-known, programming language.


R Contents


R is a powerful language for data analysis, data visualization, machine learning and statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science. You'll be learning about the basics of R


 R basics



  • Math, Variables, and Strings

  • Vectors and Factors

  • Vector operations


 Data structures in R



  • Arrays & Matrices

  • Lists

  • Data frames


 R programming fundamentals



  • Conditions and loops

  • Functions in R

  • Objects and Classes

  • Debugging


 Working with data in R



  • Reading CSV and Excel Files

  • Reading text files

  • Writing and saving data objects to file in R


 Strings and Dates in R



  • String operations in R

  • Regular Expressions

  • Dates in R


Machine Learning


 


Machine learning vs. Statistical modeling & supervised vs. Unsupervised Learning



  • Machine LearningLanguages, Types, and Examples 

  • Machine Learning vs Statistical Modelling

  • Supervised vs Unsupervised Learning

  • Supervised Learning Classification

  • Unsupervised Learning


Supervised Learning



  • K-Nearest Neighbors

  • Decision Trees

  • Random Forests

  • Reliability of Random Forests

  • Advantages & Disadvantages of Decision Trees


Supervised Learning



  • Regression Algorithms

  • Model Evaluation

  • Model Evaluation: Over fitting &Under fitting

  • Understanding Different Evaluation Models


 


Unsupervised Learning



  • K-Means Clusteringplus Advantages & Disadvantages 

  • Hierarchical Clustering plus Advantages & Disadvantages

  • Measuring the Distances Between Clusters- Single Linkage Clustering 

  • Measuring the Distances Between Clusters- Algorithms for Hierarchy Clustering

  • Density-Based Clustering


 Dimensionality Reduction & Collaborative Filtering



  • Dimensionality Reduction: Feature Extraction & Selection

  • Collaborative Filtering & Its Challenges


 

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