About Data Analytics
Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
The next essential part of data analytics is advanced analytics. The collection of big data sets is instrumental in enabling these techniques. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI).
“You can have data without information, but you cannot have information without data.”
– Daniel Keys Moran, an American computer programmer and science fiction writer.
The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. This is the process of describing historical trends in data. This part of data science takes advantage of advanced tools to extract data, make predictions, and discover trends.
Role of Data Analyst
The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data. They combine these fields in order to help businesses and organizations succeed. Data analysts exist at the intersection of information technology, statistics, and business.
The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis. The final step in most data analytics processes is data presentation. This is an essential and mandatory step that allows insights.
About R Programming
R is a programming language and an analytics tool, and It is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. It is one of the most popular analytics tool used in Data Analytics and Business Analytics. It has numerous applications in domains like healthcare, academics, consulting, finance, media, and many more. Its vast applicability in Statistics, Data Visualization, and Machine Learning have given rise to the demand for certified trained professionals in R.
Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. “R” wins on Statistical Capability, Graphical capability, Cost, rich set of packages, and is the most preferred tool for Data Scientists. Having Data Science skills is a highly preferred learning path after Data Analytics with R training.
Who should go for this Course?
This course is meant for all those students and professionals who are interested in working in the analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become ‘Data Analysts’ in near future. This is a must-learn course for professionals from Mathematics, Statistics, or Economics background and interested in learning Business Analytics.
About this course
Eureka’s Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc. Before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc.
What you will learn:
- You will understand concepts around Business Intelligence and Business Analytics.
- Explore Recommendation Systems with functions like Association Rule Mining, user-based collaborative filtering, and Item-based collaborative filtering among others.
- Apply various supervised machine learning techniques.
- Perform Analysis of Variance (ANOVA).
- Learn where to use algorithms – Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques, etc.
- Use various packages in R to create fancy plots.
- Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights.
1. Introduction to R Programming
- The various kinds of data types in R and its appropriate uses.
- The built-in functions in R like: seq, cbind , rbind and merge.
- Knowledge on the various subsetting methods, summarize data by using functions like: str, class, length, nrow and 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.
2. Data Manipulation in R
- 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.
3. Data Import Techniques in R
- 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.
4. Exploratory Data Analysis
- 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, list, multiple packages in R for data analysis, the Fancy plots like the Segment plot, HC plot in R.
5. Data Visualization in R
- Understanding on Data Visualization, graphical functions present in R, plot various graphs like tableplot, histogram and Boxplot .
- Customizing Graphical Parameters to improvise plots, understanding GUIs like Deducer and R Commander, introduction to Spatial Analysis.
6. Data Mining: Clustering Techniques
- Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering.
7. Data Mining: Association Rule Mining & Collaboration Filtering
- Association Rule Mining, User-Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF).
8. Linear and Logistic Regression
- Linear Regression, Logistic Regression.
9. ANOVA and Sentiment Analysis
- ANOVA, Sentiment Analysis.
10. Data Mining: Decision Trees and Random Forest
- 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.
At the end of the course, you will be working on a live project. You can choose from any of the categories as your Project work
The pre-requisites for learning ‘Data Analytics with R’ includes basic statistics knowledge. We provide a complimentary course “Statistics Essentials for R” to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.
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- This course needs to have in-depth exercises.
- Need more description for the topics- Linear regression & data mining
Specification: Data Analytics- R certification Training