The Ultimate Guide to Data Analytics: Tools and Techniques

Introduction

Data Analytics is the process of collecting, transforming, and organizing data to draw conclusions, make predictions, and drive decision-making. In some cases, data analytics is often confused with data analysis. Although the two may be related, they refer to different things. At its most basic level, Data Analytics is a field by itself and focuses on the overall management of data along with the tools and methods used. On the other hand, data analysis focuses on dissecting data sets into their different components and analyzing them to understand how they (components) are related to each other. That is to say, Data Analysis is a subset of Data Analytics.

In this article, we shall delve into the subject of Data Analytics and get to know what it entails, its key concepts, and how it is used. Moreover, we will get to explore Data Analytics skills providing a guide on how to break into a Data Analytics career.

What is Data Analytics

Data Analytics is a broader term used to describe the entire process of data management, methods, and techniques used. It is a multidisciplinary subject that leverages several analytical skills and techniques such as mathematics, computer science, and statistics to get insights from data. Data Analytics includes everything from analysis of data to developing frameworks that can be used to collect and store data.

Understanding Data Analytics

Today, almost every kind of information can be subjected to techniques used in Data Analytics to obtain insights that can be used to improve processes or decision-making. This is the reason why Data Analytics is pivotal in many businesses as it helps in optimizing performance and/or profits while at the same time reducing costs. This increases the overall efficiency of the organizations.

Manufacturing firms, for example, record factory runtimes, workload queues, and downtimes and leverage data analytics to better plan machine operations and achieve optimal operations.

Marketing firms use Data Analytics to understand their customers better. Through data analytics, marketers easily analyze the behavior, trends, tastes, and preferences of their customers helping them to create personalized marketing campaigns. As a result, marketers not only improve engagement with their customers but also increase the effectiveness of their marketing strategies.

Customer historical data forms the basis for banks to leverage data analytics to understand the individual financial habits of their customers. This way, banks can accurately predict and recommend things such as savings and investment decisions helping their customers reach their financial goals. To mitigate credit risk, banks and other lenders also leverage historical data to forecast the likelihood of their customers to default.

Steps in Data Analytics

When starting a new Data Analytic project, data professionals often follow a five-stage process as discussed below;

Stage 1: Determine Business Requirements or Questions
Typically, the quality of data collected for Data Analytics is determined by the business questions asked. An organization can collect as much data as it can but fail at obtaining value from it. That is to say, data will only be beneficial to the organization when the right business questions have been developed. Developing the right business questions requires data professionals to;

• Know what the company needs.
• Know the problems to be solved.
• Know the place of data in answering the business questions.
• Know the type of data needed.
• Know how results will be measured.
• Know what technologies to apply.

- Stage 2: Data Collection
- Stage 3: Data Cleaning and Preparation
- Stage 4: Data Analysis
- Stage 5: Data Visualization and Storytelling

Data Analytics Tools and Techniques

Several tools and techniques are used at different stages of Data Analytics. Data professionals use a wide range of software tools and technologies to acquire, store, process, and report findings. Below are some of the tools used in data analytics.

• Python and R
• SQL
• Google Sheets and Excel
• Power BI and Tableau

To extract valuable insights from data, data professionals employ different methods and techniques. Such include;

• Time Series Analysis
• Machine Learning
• Classification
• Regression Analysis

Understanding how to use and apply these tools and techniques is what empowers data analysts to uncover insights, make informed decisions, and contribute towards strategic goal-setting in organizations across industries.

How Can One Become a Data Analytics Expert

If you are intrigued by numbers, uncovering patterns, and making data-informed decisions then a Data Analytics career is your perfect field. Below is the brief roadmap you can use to become a data professional.

Step 1: Understand Data Analytics
Before breaking into a Data Analytics career, it is important to have a solid understanding of what the field entails. Typically, data professionals collect, process, and analyze data to uncover insights to help their organizations make better data-driven decisions.

Step 2: Build a Foundation in Data Analytics
To break into a Data Analytics career, you may (or may not) need formal education. However, having formal education can greatly improve your domain knowledge especially if you want to be a data analytics expert in a certain industry. Common formal education degrees that aspiring data professionals may consider include; bachelor's Degrees in Mathematics, Statistics, Computer Science, Economics, Business Administration, and Data Science.
Online courses and boot camps have also become essential in preparing aspiring data professionals for a career in data analytics. This is a perfect option for individuals who prefer self-paced and flexible learning environments. Typically, online courses and bootcamps provide accelerated learning paths providing practical hands-on training to learners. Over the years, this option has empowered many people to become job-ready within a short time. An example of a platform providing data courses is DataCamp.

Step 3: Master Essential Tools and Technologies
Becoming an expert in data analytics requires one to be well-versed in different data tools and technologies. Some of these tools include;

• Spreadsheet software such as Google Sheets and Excel.
• Programming languages such as Python and R.
• Data Visualization tools such as Power BI and Tableau.
• Databases skills particularly SQL.

Step 4: Get Practical Experience
You will need to put your skills to work by working on hands-on projects, hackathons, or even getting an internship. Projects are a perfect way to hone your data skills. You can look up data analytics projects online from platforms such as Kaggle which can easily help you build your data portfolio.

Step 5: Earn Certifications
In case you want to validate your data analytics skills, certifications may come in handy. Certifications may come as an added advantage especially if you want to stand out before potential employers.

Step 6: Build a Network
In any career development, networking plays a very critical role. Through networking, people have landed data opportunities such as jobs, collaborations, or even mentorship. Building a professional network may require one to participate in online platforms, attend data conferences, or even join data communities.

The Bottom Line

In a world that is becoming increasingly dependent on data, individuals and organizations can leverage data analytics to be sure of their data. A set of raw and meaningless data points can be transformed using different tools and techniques to obtain informative insights that can support decision-making processes. This makes data analytics the heartbeat of a modern organization.