Tuesday, May 30 2023

You have probably heard about the word “Data” and you are wondering what this means, the English Dictionary defines Data as facts and statistics that are collected for reference or analytical process. Data can be in a coded form or other forms, but Data simply means a piece of information. So, what do you do with a piece of information? You collect more data to make information more comprehensive. Data is collected, Reported, measured, and then Analyzed using different analyzing tools such as graphs, images, etc.

Now that we have been exposed to what Data means, let us jump straight into Data analysis.

Data Analysis

What is Data analysis?

Data analysis be the process of cleaning, modeling, and transforming data for research or problem-solving purposes. Data analysis basically involves the extraction of useful information from datasets to affect an individual or company in the process of decision-making. Let us relate this to our day-to-day life, sometimes before we make decisions, we analyze our past or our future before we make any decision. That is exactly what Data analysis means.

Why Data analysis?

Developing or developed businesses use Data analysis to facilitates growth. Only a less visionary business makes business decisions without the use of Data analysis.

Types of Data Analysis.

There are various types of Data Analysis and they exist based on the type of business and technology.

(1) Text Analysis

(2) Statistical Analysis

(3) Diagnostic Analysis

(4) Predictive Analysis

(5) Prescriptive Analysis

(1) Text Analysis: Text Analysis can also be referred to as Data Mining. This is a Data Analysis method that involves the use of Database or Data mining tools to discover a pattern in large data sets. This Analysis method also transforms raw data into information used in Businesses. In short, Text Analysis helps to extract and examine data by creating some sort of pattern to fully interpret the data.

(2) Statistical Analysis: This used past data in its analysis. It is in the form of “What happened?“. Statistical Analysis involves the collection, interpretation, presentation, and remodeling of sample data or sets of data. Statistical analysis can be categorized into two, which includes Inferential Analysis and Descriptive Analysis.

(3) Diagnostic Analysis: This analysis method finds causes, that is, “why did it happen?“. This Analysis method is used to identify behavior patterns of data. For example, if you are facing a certain issue in your business, you can use this analysis method to find past problems that are closely related to the present one. And if there is any, the solution that was used for the former could be prescribed for the present one.

(4) Predictive Analysis: This Analysis guesses what would happen by using previous data. That is, “What is likely to happen”. The predictive analysis makes predictions about the future based on using previous data. It is not always accurate though. But it could if much detailed information is provided.

(5) Prescriptive Analysis: This analysis method is probably my favorite because it combines the insight from past analysis to determine the right action to take when it comes to problems solving or decision-making processes.

Data Analysis

Phases of Data Analysis

Data Analysis doesn’t just happen, it takes steps. Let us consider the phases.

(1) Data requirements Gathering.

(2) Data Collection.

(3) Data Cleaning

(4) Data Analysis

(5) Data Interpretation

(6) Data Visualization.

1. Data Requirement Gathering: This phase involves finding the purpose of doing the analysis. Like, why and what do I need it for? There must be a clear aim of what you need it for. Then you decide the type of data analysis that want to do.

2. Data Collection: This phase involves the collection of data based on requirements. As a data analyst, your job is to collect data and properly organized for Analysis. In the process of collecting data, remember to keep a log with a collection of data and the source of the data.

3. Data Cleaning: I personally call this filtering. This is because not all the Data will be useful for your analysis. So as a Data Analyst, you must conduct a proper vetting on the data you have collected.

4. Data Analysis: This process will follow you’ve collected, cleaned, and process the data. Data analysis tools may be employed in this phase to help you understand, interpret, and then derived conclusions based on the requirements.

5. Data Interpretation: You interpret already analyzed data. In this phase, you’re spoiled with choices because you get to choose the best method that suits you to interpret the data. It could be in the form of a table, Graph, or simply in words.

6. Data Visualization: This phase is common in our day-to-day life. They appear in the form of Charts, Graphs, etc. Data Visualization is usually used to discover new or unknown facts and trends.

Who is a Data Analyst and what do they do?

Data Analyst

To simply put it, A data analyst is charged with analyzing, collecting, processing, and remodeling of datasets. They find out how data can be used to answer questions, solve problems, and help in the process of decision making. And with the increasing surge towards Technological intertwine, data analysis has evolved greatly. This is due to the development of relational database and data analysis tools.

The basic job of a data analyst in any business venture include,

(1) Estimation of market shares

(2) Figuring out when to hire or when to reduce the workforce.

(3) To establish prices for new products or new materials for the market. Etc.

Skills Data Analysts must possess.

1. Creative and Analytical Thinking: As a data analyst, curiosity and creativity are an important attribute that you a good data analyst must possess. You must be able to critically think through problems and offer groundbreaking solutions. This facilitates the growth of the business or company.

2. Effective Communication: Data analysts must be able to have clear communication with people using different data interpretation tools that would be able to convey their findings. As they often say, Communication is the key to success.

3. Data Visualization: A good data analyst should know how to interpret his analyzed data. This means a good analyst must know the type of graphs to use, which type of charts to use, etc.

4. SQL Databases: SQL means Structured Query Language and they are a relational database with structured data. Data in these databases are stored in tables to make it possible for data analysts to pull information off from these tables to perform analysis.

5. Database Querying Languages: There are various types of querying languages out there, but the commonest is the SQL. This querying language also has varieties and they include, PostgreSQL, T-SQL, PL/SQL. Others include XML, JavaScript, Python, SAS, Hadoop.

6. Data Mining and Cleaning: A data analyst must filter the data, that is data must be vetted thoroughly and must be properly arranged and stored.

7. Advanced Microsoft Excel: A good data analyst should know how to use Excel and understand advanced modeling and analytics techniques.

8. Programming Language: A data analyst must know at least one programming language. For example, a good data analyst should have knowledge about programming languages such as R and SAS which can be used in data gathering, data cleaning, and data visualization.

Common Tools/Software used by Data Analyst

Programs used by Data Analyst

1. Python: Python is an open-source tool and is an object-oriented language that is easy to read, write, and maintain. It is very easy to read because it is a bit like JavaScript, and it can also handle text data well.

2. SAS: Sas is a programming language for data manipulation and analytics. Sas is a top analysis tool because it is easily accessible, and it can analyze data from any sources.

3. Microsoft Excel: This is a very common computer tool for analysis purposes. Excel presents data in tables thereby help in the filtering of data.

4. RapidMiner: This is a powerful integrated data platform that was developed by the same company that performs data mining, predictive analytics, text analytics, and visual analytics without any programming.

5. MYSQL workbench: This is a tool for data modeling, SQL development, backup, etc. It gives instant access to a database schema, etc. through the use of Object browser. This tool is used to design, model, manage databases, and optimize SQL queries and utilize a suite of tools to improve the performance of MySQL applications.

6. KNIME: KNIME was developed in 2004 and has grown into a leading open-source tool, integrated analytics tool, and reporting tools that allow Data analyst to analyze and model data using visual programming. Through its modular data pipelining concept, it has integrated the process of data mining.

Programs used by Data Analyst

7. R Programming: This is arguably the biggest and the most widely used analytical tool that can be used for statistics and data modeling. What it does is manipulate data and then present it in different ways. R is jam-packed with 11,556 packages and allows you to browse the packages by categories.

8. Google Analytics (GA): This tool helps data analysts to understand customer data, this includes trends and areas that need improvement in relation to Customers on landing pages.

9. AWS S3: This tool is a cloud storage system that can be used by analysts to retrieve and store large data.

Other example includes Tableau Public, Apache Spark, QlikView, Splunk.

How to become a Data Analyst

The growth in the numbers of companies has necessitated the need for qualified data analysts. In fact, data analysts are in great demand. According to BLS (Bureau of Labor Statistics), the employment of computer and information research scientists is expected to grow 11 percent from 2014-2024, and this doesn’t exclude Data analysts.

If peradventure you like solving problems and you love numbers, data analysis might just be a good fit for you. You can attain this level by obtaining a university degree in data science and data analysis, and by learning important analytical skills, etc. So, let us pick them one by one.

(1) Earn a bachelor’s degree in information technology or computer science: Most data analyst jobs require a bachelor’s degree. That means you will have to earn a degree in subjects such as Statistics, Mathematics, economics, and computer science.

(2) You should consider gaining some data analysis experience: As they say, the experience is the best teacher. Most firms only hire data analyst with experience, either little or vast experience, experience is experience. Gaining more experience adds to one’s skills. A good way to gain more experience is by interning while in school.

(3) Consider a master’s degree or certificate program: The more you advance in your academics, the greater your chance of having more job opportunities. Not only this, but it will also furthermore increase your level in the field of data analysis. You should consider applying for Data science in your master’s degree.

(4) Learn necessary skills: This includes Master College-Level Algebra, Statistics, Coding, and other programming languages such as Python, SQL, R, JavaScript, also familiarize yourself with Microsoft excel and learn machine languages.

Data Analysts Salary

Data Analysts Salary

An average salary for a Data Analyst is $57.261 per year according to Payscale.com. Although a lot of factors would affect this, this includes education level, level of experience, expertise in the field, etc.


Do you think you know what a cyber security analyst does? 10 things that they do a daily basis.


Python Programming: Everything You Need to Know

Check Also


Don’t Miss

Cloudburst Technologies Raises $3 million in Seed Funding

Julia Bischoff

Cloudburst Technologies, a monitoring and threat intelligence provider, announced that they raised $3 million in their seed investment round. Investments were led by Strategic Cyber Ventures with participation from Coinbase Ventures and Bloccelerate. Cloudburst Technologies specifically provides tools aimed at tracking and combating fraud in the cryptocurrency market. Investigators can do so without relying on […]

Two people collaborating over a computer by Desola Lanre-Ologun

Cyber Deals: Cloudburst, HUB Security, and Soucepass

Julia Bischoff

Cybersecurity venture funding activity and mergers and acquisitions: This week’s most sizable deals included AI powered security service providers and computing platforms. Funding Threat solutions provider for cryptocurrency Cloudburst Technologies raised $3 million in the seed funding round led by Strategic Cyber Ventures in order to help build on their current technology. HUB Security, a […]

Cyber Executive Moves: Vimeo, IntelePeer, National Cybersecurity Center, and More

Julia Bischoff

Former Tesla CISO joins Vimeo, NCC welcomes new board members, and Greenway Health hires new CISO. Video sharing platform Vimeo welcomes Mark Carter as their first chief information security officer. Carter has a variety of experience in the industry including serving as chief information security officer at Tesla.    Greenway Health, a health services software […]

NetBox Labs Raises $20 Million in Series A To Drive Open Source Transformation of Networking Industry


NetBox Labs, an open source network management and automation startup, announced it has raised $20 million in Series A funding. Flybridge Capital is leading the investment with participation from GGV Capital, Grafana Labs CEO Raj Dutt, Mango Capital, Salesforce Ventures, Two Sigma Ventures, IBM, the Founder Collective, and Entrée Capital. As part of the round, […]

Fivecast Completes $20M Series A Raise With New US and Existing Australian Venture Investors


US-based cybersecurity venture capital firm Ten Eleven leads funding, joining existing backers in CSIRO’s Main Sequence and South Australian Venture Capital Fund Australian open-source intelligence software company, Fivecast, has closed its Series A funding round with almost $20 million (AU$30m) raised to fuel its expansion and service contracts in key markets, including Five Eyes nations, […]

Push Security Raises $15M and Launches New Visibility and Employee-Powered Tools to Help Enterprises Scale SaaS Security


LONDON–(BUSINESS WIRE)–Push Security, a SaaS security company, has raised $15M in Series A funding. GV (Google Ventures) led the funding with participation from Decibel and notable angels, including Dug Song, co-founder and former CEO at Duo Security, and Tray.io co-founder and CEO Rich Waldron. GV General Partner Karim Faris and Jon Oberheide, co-founder and former […]