Introduction
Today’s digital world creates enormous amounts of data from virtually every aspect of your life online shopping, social media, banking, and health care, among many others. However, data alone has no intrinsic value until it has been analyzed. This is where Data Analytics comes into play. The first step toward building a solid foundation for your future career in Data Analytics is to become familiar with its fundamental concepts. By reading this blog, you will learn about the key concepts, fundamental tools, and the many different applications of Data Analytics in a straightforward manner.
The Definition of Data Analytics
Data Analytics is a systematic method for collecting, cleaning, analyzing, and interpreting data in order to identify patterns, trends, and other insights that can be used to improve decision-making and performance throughout the organization as well as support future planning.
To put it simply, Data Analytics helps to answer questions like:
- Why did sales go up or down?
- What were the best-selling products/services?
- How could customer satisfaction be improved?
What are the primary concepts for being taught or learned in order to utilize the various tools available for Data Analytics?
1) Data Gathering – how Data can be collected from several different types of Sources (e.g. Databases, Websites, Applications, Surveys, Sensors).
2) Data Cleansing – Data tends to be DISORGANIZED (messy) once collected; Data Cleansing includes the following steps;
- Identifying and removing Duplicate Data
- Handling Missing Values
- Correcting Errors
Ensuring that Data is Cleansed should be a very important priority for any Data Analyst as any inaccuracy in the data being analyzed will result in an inaccurate output or conclusion.
3) Data Analysis – A Data Analyst will understand how to utilize Data Analytical Techniques and Querying to explore the data and find Patterns and Trends.
4) Data Visualization – The use; of Charts, Graphs, and Dashboards for providing Data Analysis to utilize for Data Visualization to be used for a Non-Technical Audience
5) Supporting Smart & Data-Driven Decisions with Data Analysis – The Primary Objective of Data Analytics is to Support Smart & Data-Driven Decisions.
Which Tools will be used in Data Analytics?
Students who want to learn about Data Analytics Basics will need to be introduced and learn to use the following tools:
1) Microsoft Excel – Useful in organizing Data, as well as using Formulas, Pivot Tables and for performing Basic Data Analysis.
2) SQL (Structured Query Language) – Helpful for Pulling and Manipulating Data stored in Databases through Querying.
3) Python – Used for Data Analysis with Python includes Data Processing, Automation as well as for Working with Large Data Sets.
4) Power BI & Tableau – Used for creating Interactive Dashboards and Visual Reports.
Webipher offers many opportunities to learn how to effectively use the above tools and build confidence while learning all aspects of the tools.
Data Analytics Use in the Everyday World
As stated above, the majority of business sectors use Data Analytics Basics in some capacity.
Examples of how Data Analytics is used:
- Business & Sales – Evaluate sales performance, track customer activity and determine revenue patterns.
- Marketing – Analyze success of marketing campaign(s), measure customer engagement and track conversion rates.
- Healthcare – Monitor patient records to improve patient treatment outcomes and assist in resource planning.
- Finance – Identify fraudulent activities (e.g., unauthorized purchases), determine the cost of doing business and predict financial risk.
- Education – Track student academic performance and improve student academic outcomes.
As shown here, applying Data Analytics across all industries can make it a viable, long-term career choice.
Why is it important to learn the Data Analytics process in its entirety?
Too often, beginners are concerned with mastering the tools of Data Analytics and do not dedicate enough time to understand how to create their own analytical thinking and problem-solving skills via repetitive use of Data Analytics and gaining exposure to the real-world application of what Data Analytics is used for.
Taking a structured approach to learning Data Analytics will enable aspiring Data Analysts to do the following:
- Build a solid foundation for their learning;
- Conceive ideas based on real-life situations; and
- Be able to carry themselves confidently in an interview situation.
Why Webipher Is the Right Place to Start Your Data Analytics Journey
Many students and new combinations are looking for Beginner-Friendly Course offerings designed for students, undergraduates or recent graduates, all of which can be found at Webipher. The focus of this course offering is to give a broad view of Data Analytics, along with a hands-on approach, to provide learners with an understanding of what it is like to work with this type of data in the business environment.
The course is supported with industry-standard tools, hands-on projects, recognised certifications and job placement assistance. In addition, students are provided with expert support throughout the Data Analytics learning process.
For students, graduates and/or working professionals, Webipher offers online Data Analytics courses that provide a strong foundation for building a long-term successful career.
Conclusion
Data Analytics is one of the most important skills necessary for Success. The ability to combine Data, Logic and Decision-Making is highly valuable in the marketplace today. Once individuals understand the basics of Data Analytics and this course provides students and/or graduates with practical and realistic examples to practice on, students can enter the field of Data Analytics with confidence.
