Data Analytics Training

 
Data Analytics Online Training

“LucidTechSystems” IT is premier a technology and software training institute in Hyderabad and we are best trusted by experts for providing quality training with the best infrastructure and expert faculty. LucidTechSystems proudly offers a list of trending technologies and software application courses in which students can get trained by some of the finest faculty across the nation who are considered as well reputed industry experts. Aspirants who are aiming at securing a career in the ingenious Data Analytics technology can now get enrolled for our advanced & real-time industry based Data Analytics Training In Hyderabad & as well as Data Analytics Online Training.

LucidTechSystems Best Data Analytics Online Training & Data Analytics Corporate Training program will make the aspirants become complete industry ready professionals capable to handle all the real-time challenges in this profession. We are well known for delivering online and classroom based training in multiple disciplinary courses.

Data Analytics Training in Hyderabad by LucidTechSystems is a prominent career program which is delivered from the hands of real-time domain experts. We provide Data Analytics Corporate Training & LucidTechSystems Online Courses. We surely help the aspirants towards boosting their career graph in this ingenious technology.

Who Can Learn Data Analytics?
Best Data Analytics Training In Hyderabad by the “LucidTechSystems” is specially meant for aspirants who are very much keen at making a career in the disruptive & progressive Data Analytics technology. This course is also meant for
• Technology Professionals
• Software Specialists
• Market Professionals
• Job Aspirants
• Career Enthusiasts

Best Data Analytics Online Training through Online across India, like Hyderabad, Bangalore, Mumbai, Delhi, Pune, Chennai, Vijayawada, Kerala and etc. LucidTechSystems providing corporate training worldwide depending on Company requirements with well experience real time experts

Course Objectives of Data Analytics Include
• Introduction to Data Analytics
• Introduction to R Programming
• Data Manipulation in R
• Data Import Techniques in R
• Exploratory Data Analysis
• Data Visualization in R
• Data Mining Clustering Techniques
• Data Mining Association Rule Mining & Collaborative filtering
• Linear and Logistic Regression
• Anova and Sentiment Analysis
• Data Mining Decision Trees and Random Forest

Benefits To You

LucidTechSystems is a Best training center for Data Analytics Training given corporate trainings to different reputed companies. In Best Data Analytics Training all sessions are teaching with examples and with real time scenarios. We provide all recordings for classes, materials, sample resumes, and other important stuff. We are helping in real time how approach job market, Resume preparation, Interview point of preparation, how to solve problem in projects in job environment, information about job market etc. Data Analytics classroom Training in Hyderabad and online from anywhere

Our courses are carefully planned and are in accordance with the highly valued certification exams. By the time of course completion, our students will become industry ready Data Analytics professionals capable to handle all the real-world challenges of this domain. Master the Data Analytics concepts and become a Data Analytics expert by enrolling in our Data Analytics Course. Best Data Analytics Online Training through worldwide like India, USA, Japan, UK, Malaysia, Singapore, Australia, Sweden, South Africa, and etc. LucidTechSystems providing corporate training worldwide depending on Company requirements with well experience real time experts

LucidTechSystems gives an opportunity to work on real-time projects which would be guided by our real-time trainers. A technical back end team would always be available to answer your quires at any point of time and will also assist you to arrange your training process.

Course Content
Core topics of Data Analytics Online Course

Introduction to Data Analytics
Learning Objectives – This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems. This module tells you what is R and how it is used by the giants like Google, Facebook, Bank of America, etc. Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
Topics – Introduction to terms like Business Intelligence, Business Analytics, Data, Information, how information hierarchy can be improved introduced, understanding Business Analytics and R, knowledge about the R language, its community and ecosystem, understand the use of ‘R’ in the industry, compare R with other software in analytics, Install R and the packages useful for the course, perform basic operations in R using command line, learn the use of IDE R Studio and Various GUI, use the ‘R help’ feature in R, knowledge about the worldwide R community collaboration.

Introduction to R Programming
Learning Objectives – This module starts from the basics of R programming like datatypes and functions. In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario. You will also learn how to apply the ‘join’ function in SQL.
Topics – The various kinds of data types in R and its appropriate uses, the built-in functions in R like seq(), cbind (), rbind(), merge(), knowledge on the various subsetting methods, summarize data by using functions like str(), class(), length(), nrow(), ncol(), use of functions like head(), tail(), for inspecting data, Indulge in a class activity to summarize data, dplyr package to perform SQL join in R

Data Manipulation in R
Learning Objectives – In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis. Thus using and exploring the popular functions required to clean data in R.
Topics – The various steps involved in Data Cleaning, functions used in Data Inspection, tackling the problems faced during Data Cleaning, uses of the functions like grepl(), grep(), sub(), Coerce the data, uses of the apply() functions.

Data Import Techniques in R
Learning Objectives – This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website. This module teaches you various data importing techniques in R.
Topics – Import data from spreadsheets and text files into R, import data from other statistical formats like sas7bdat and spss, packages installation used for database import, connect to RDBMS from R using ODBC and basic SQL queries in R, basics of Web Scraping.

Exploratory Data Analysis
Learning Objectives – In this module, you will learn that exploratory data analysis is an important step in the analysis. EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics – Understanding the Exploratory Data Analysis(EDA), implementation of EDA on various datasets, Boxplots, whiskers of Boxplots. understanding the cor() in R, EDA functions like summarize(), llist(), multiple packages in R for data analysis, the Fancy plots like the Segment plot, HC plot in R.

Data Visualization in R
Learning Objectives – In this module, you will learn that visualization is the USP of R. You will learn the concepts of creating simple as well as complex visualizations in R.
Topics – Understanding on Data Visualization, graphical functions present in R, plot various graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots, understanding GUIs like Deducer and R Commander, introduction to Spatial Analysis.

Data Mining Clustering Techniques
Learning Objectives – This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types. We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.
Topics – Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering.

Data Mining Association Rule Mining & Collaborative filtering
Learning Objectives – In this module, you will learn how to find the associations between many variables using the popular data mining technique called the Association Rule Mining, and implement it to predict buyers’ next purchase. You will also learn a new technique that can be used for recommendation purpose called Collaborative Filtering. Various real-time based scenarios are shown using these techniques in this module.
Topics – Association Rule Mining, User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF)

Linear and Logistic Regression
Learning Objectives – This module touches the base of ‘Regression Techniques’. Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
Topics – Linear Regression, Logistic Regression.

Anova and Sentiment Analysis
Learning Objectives – This module tells you about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module. Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.
Topics – Anova, Sentiment Analysis.

Data Mining Decision Trees and Random Forest
Learning Objectives – This module covers the concepts of Decision Trees and Random Forest. The algorithm for creation of trees and classification of decision trees and the various aspects like the Impurity function Gini Index, Pruning, Entropy etc are extensively taught in this module. The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples. At the end of the class, these concepts are implemented on a real-life data set.
Topics – Decision Tree, the 3 elements for classification of a Decision Tree, Entropy, Gini Index, Pruning and Information Gain, bagging of Regression and Classification Trees, concepts of Random Forest, working of Random Forest, features of Random Forest, among others.

Project Work
Learning Objectives – This module discusses various concepts taught throughout the course and their implementation in a project.
Topics – Analyze census data to predict insights on the income of the people, based on the factors like age, education, work-class, occupation using Decision Trees, Logistic Regression and Random Forest. Analyze the Sentiment of Twitter data, where the data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same.

Practice & Interview Questions
LucidTechSystems strongly believes in the concept that practical knowledge along with theoretical can drive the best learning experience among the students, thereby we work towards developing the theoretical & practical skills among our students and guide them towards working on multiple real-time projects. We also help our students in preparing for interviews and resumes. We also provide soft copy and hard copy subject materials for your future references.

Upon successful completion of the course program of Data Analytics Online Training program from LucidTechSystems students can work towards availing a certification. This certification will act as a gateway for the rising career opportunities in this domain.