Data Analytics for Beginners Basic Guide to Master Data Analytic...

Data Analytics for Beginners

Basic Guide to Master Data Analytics

Table of Contents:

Introduction

Chapter 1: Overview of Data Analytics

Foundations Data Analytics

Getting Started

Surveys

Experiments

Gathering Data

Selecting a Useful Sample

Avoiding Bias in a Data Set

Explaining Data

Descriptive analytics

Charts and Graphs

Chapter 3: Measures of Central Tendency

Mean

Chapter 4: Charts and Graphs

Pie Charts

Create a Pie Chart in MS Excel

Bar Graphs

Create a Bar Graph with MS Excel

Customizing the Bar Graph

Time Charts and Line Graphs

Create a Line Graph in MS Excel

Customizing Your Chart

Annual Employee Losses

Spatial Plots and Maps

Chapter 5: Applying Data Analytics to Business and Industry

Business Intelligence (BI)

Data Analytics in Business and Industry

BI and Data Analytics

Chapter 6: Final Thoughts on Data

Conclusion

Introduction

We live in thrilling and innovative times. As business moves to the digital environment, virtually every

action we take produces data. Information is collected from every online interaction. All sorts of devices

Data analytics is a way to make valuable use all types of information. Analytics is used to help

categorize data, identify patterns, and predict results. Data use has become so ubiquitous that it has

become necessary for individuals in every profession to learn how to work with data. Those who

become the most proficient at working with data in useful and creative ways will be the most successful

in the new world of business.

Until recently, data analytics was limited to an exclusive culture of data analysts, who characteristically

presented this topic in complicated and often unintelligible terminology. Fortunately, data analytics is

not as complicated as many believe. It simply consists of using analytical methods and processes to

develop and explain specific and useful information from data. The point of data analytics is to enhance

practices and to support better-informed decisions. This can result in: safer practices within an industry,

Chapter 1: Overview of Data Analytics

outcomes. Data analytics gives people the insights that they need to plan for improvements and specific

results. Analytics are generally used for the following purposes:

• To enhance business organizations and increase returns on investment (ROIs).

• To improve the success of sales and marketing campaigns.

• To identify trends and emerging developments.

• To make society more safe.

Foundations Data Analytics

Data analytics requires the use mathematical and statistical procedures. It also requires the skills to work

with certain software applications and a knowledge of the subject area you are working with. Without

knowledge of the subject-matter, analytics is reduced to simple analytics. Due to the increasing demand

unstructured, and partially-structured data. The amounts of data that you are likely to be working with

can be too large for a normal database system to effective process. A data set that is too large, changes

too quickly, or it does not conform to the structure of standard database designs requires a special

skillset to manage. Data analytics consists of analyzing, predicting, and visualizing data. When data

analysts gather, query, and interpret data, they conduct a process that is quite similar to data engineering.

Although useful insights can be produced from an individual source of data, the blending of several

sources gives context to the data that is necessary to make more informed decisions. As a data analyst,

you can combine multiple datasets that are maintained in a single database. You can also work with

several different databases maintained within a large data warehouse. Data can also be maintained and

managed within a cloud-based platform specially designed for that purpose. However the data is pooled