Business Analytics Foundation With R Tools Tutorial

This is a tutorial about Business Analytics Foundation With R Tools offered by Simplilearn. The tutorial is part of the Data Science with R Language Certification Training course and will help understand the basics of data science and business analytics with types and learn its importance.

Objectives

This business analytics foundation tutorial comprises of four lessons. After completing the business Analytics Foundation With R Tools tutorial, you will be able to:

  • Understand what analytics is, where it is applied, its types, the tools and the process involved in it.

  • Get an overview of the various topics that will be covered in different lessons.

  • Describe the Career Path of a business analyst.

In the next section, we will look at what analytics is.

Analytics

Analytics is a journey that involves a combination of potential skills, advanced technologies, applications, and processes used by a firm to gain business insights from data and statistics. This is done to perform business planning.

In the analytics process, data is collected from various external sources and is stored in a data warehouse. After this data collection, the process of analytics begins. Here, a more in-depth learning of the data is done to derive possible insights from data. In addition to this, all advanced data-driven capabilities are developed, which in turn provides additional value to the business.

In the next section, we will look at the places where analytics is applied.

Places Where Analytics is Applied

Some of the common fields where analytics is implemented are as follows,

Financial services

In this field, analytics is used for credit scoring, fraud detection, pricing and claims analysis.

Retail

In Retail, it is used for promotions, replenishment, demand forecasting, and merchandising optimization.

Healthcare

In Healthcare, it is used for drug interaction, preliminary diagnosis and disease management.

Field of Communication

Customer retention, capacity planning, and network optimization are the primary applications in the field of Communications.

Applications in Energy

Trading, supply, demand forecasting, and compliance are the areas of application in Energy.

Manufacturing

In Manufacturing, it is used for inventory replenishment, product customization, and supply chain optimization.

In the next section, we will look at the topics that are covered in the first and second lessons.

Topics Covered

There are four lessons covered in r tutorial. The lesson names and their brief descriptions are listed below.

Lesson no.

Chapter name

What you will learn

Lesson 1

Introduction to analytics tutorial

  • What analytics is and understand the process involved in it.

  • Get brief details on where you could apply analytics and what insights can be derived from this application.

  • Understanding a problem and knowing how a data scientist should act. Also helps in knowing how to collect data and prepare it for your analytical process.

Lesson 2

Statistical Concepts And Their Application In Business Tutorial

  • To have an overview of all statistical methods, including descriptive statistics, probability theory, concepts of the test of significance and hypothesis testing.

  • Some business analytic uses of these statistical concepts have also been provided via case studies for better understanding.

  • To derive some meaningful pattern from the data. It will help you in finding the distribution pattern that is hidden in the data and in proceeding with further decisions that are to be made at the basic level.

Lesson 3

Basic Analytic Techniques - Using R Tutorial

  • Data exploration, data visualization, and diagnostic analytics and perform these using the R tool.

  • Case studies and examples to help you to understand the concepts in a practical way and also help you use the R tool effectively.

Lesson 4

 

 

 

 

 

 



 

Predictive modelling techniques

  • Some predictive techniques such as regression analysis and the models that could be built from them.

  • Logistic regression, time series analysis, and cluster analysis will also be learned with some example case studies.

  • These predictive modeling techniques will help you look at past data and identify the predicted value that helps in making decisions.

  • All the working procedures which are explained using the R tool in a stepwise manner with suitable examples.

 

In the next section, we will look at the career path of a business analyst.

Career Path

The use of Analytics has evolved over time and the advent of Big Data has made analytics one of the most sought-after skills in the industry across domains. This tutorial will help you take the first step toward understanding analytics and the way it interfaces with the industry.

The tutorial will also help you to start thinking like a business analyst. Career-wise, you can aspire to be a data manager, data miner, or a business manager. The work of data manager involves:

  • Data preparation

  • Deployment services

  • Report administration

The work of a data miner involves:

  • Exploratory analysis

  • Descriptive segmentation

  • Predictive modeling

The work of a business manager involves:

  • Campaign management

  • Domain expertise

  • Process and ROI evaluation

Conclusion

With this, we come to an end about the Business Analytics foundation with R tool tutorial. In the next chapter, we will discuss the Introduction to Analytics.

 

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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