Preface: Who is a Data Analyst
From the story of successful businesses, we usually only get to read their strange stories. No one tells us what problems are holding us back, or what exactly our users and audience are interested in that we haven’t worked on. Or what are the strengths and weaknesses of our business? …
From the story of successful businesses, we often only get to read their strange stories. No one tells us what difficulties are holding us back, or what exactly our users and audience are interested in that we haven’t worked on. Or what are the strengths and weaknesses of our business? Sometimes we try to make diagnoses by checking and observing and usual methods, which are still not enough. The secret of big and successful companies in answering these questions and putting the arrow in the middle is nothing but data analysis! Stay with us until the end of the article and read about the data analyst:
- What is data analysis?
- What skills and duties does a data analyst have?
- How to become a successful data analyst?
- Tools needed by a data analyst
What is data analysis?
Data analysis is a field that takes logical conclusions and arguments from a large amount of data and information that is recorded every day in business and prepares coherent and understandable information for business decisions and strategies.
The data analyst is a translator who extracts the understanding of the situation and goals and strategies for the future and corrections for the current situation from the sum of partial and incomprehensible information.
In this way, analysts can recognize the competitive environment, and internal and external interests of the business and propose solutions to improve them.
How does data analysis happen?
Let’s give an example to get more familiar with the duties of a data analyst.
You may work with a large company that needs to analyze audience behavior to sell its products and know which strategies to work on in the future. The marketing team of this company enters into negotiations with you as a data analyst and says, for example, that we need to know what products the audience bought more during the quarantine period and how they made this purchase. Why products like A, which are not much different from B, have received many times the reception.
Or that a greenhouse wants to obtain information about the response of temperature and other factors on a plant. And you should find the answers to these questions with the methods you know.
What does a data analyst do?
Currently, the field of data analysis or Data analytics can be studied as a field for college and at the graduate level. In these stages, analysts get acquainted with concepts such as probability, data visualization, statistical modeling, analysis and forecasting, and business risk management and learn its methods.
The volume of our of a data analyst depends on the organization in which he works and the type of work. The data analyst may be able to provide a correct analysis of the data with the available tools. And it may be necessary for a large organization to code programs with languages like Python and analyze data using it.
What skills should a data analyst have?
The technical skills that a data analyst should have included the following:
- Proficiency in programming languages such as SQL, R, or Python
. Data analysts must be proficient in one language and work in several other languages. Data analysts use programming languages such as R and SAS for data collection, cleaning, statistical analysis, and visualization. - SQL Database Mastery SQL
A database is a relational database with structured data. Data is stored in tables, and a data analyst pulls information from various tables to perform analysis. The most common database query languages are SQL and its derivatives. Including PostgreSQL, T-SQL, and PL/SQL. - Proficiency in spreadsheet tools such as Microsoft Excel or Google Sheets
Data analysts must be proficient in Excel and understand advanced modeling and analysis techniques.
- Machine learning
Data analysts with machine learning skills are extremely valuable, although machine learning skills are not expected of typical data analyst jobs. - Proficiency in data visualization software such as Tableau or Qlik
- Adequate mathematical and statistical knowledge for the process of collecting, measuring, organizing, and analyzing data
Duties of data analyst:
- Designing and maintaining systems and databases
- Fix coding errors and other data-related problems
- Using patterns and trends to interpret aggregate data
- Preparation of reports for managers and leaders of the organization to identify potential opportunities, suggest improvements to existing situations, and formulate policies
Types of data analysis
The four types of data analytics build on each other to bring ever-increasing value to an organization.
Descriptive analysis
This type of analysis examines the events that happened in the past and concludes them. For example, it discovers and examines the trends of the organization on information such as monthly income, quarterly sales, annual website visits, etc.
Diagnostic analysis
In this type of analysis, they find the reason for the events by comparing descriptive data sets to identify dependencies and patterns. It helps an organization determine the cause of a positive or negative result.
Predictive analysis
This method helps the analyst determine possible outcomes by identifying trends in descriptive and diagnostic analysis. And it allows an organization to take proactive action, such as contacting a customer who is unlikely to renew a contract.
Prescriptive analysis
In this method, the analyst tries to perform business actions. While this type of analysis has significant value in being able to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.
A survey of more than 2,000 PwC business managers and consultants concluded that organizations do not consider descriptive analysis sufficient for informed, data-driven decision-making. Likewise, diagnostic and predictive analytics are increasingly important to organizations.
Tools needed by data analysts
There are several essential tools that data analysts use in their work:
- Google Analytics helps analysts understand customer data, including trends and areas of the customer experience that need improvement on landing pages or calls to action (CTAs).
- Analysts use Tableau to collect and analyze data. They can create and share dashboards with different team members and create visualizations
- The Jupyter Notebook system simplifies code testing for data analysts. Non-technical people prefer the simple design of client notebooks because of the markup feature.
- GitHub is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming.
- AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large data sets.
Where should I start to become a Data Analyst?
There are various tools that data analysts use day in and day out. Some data analysts use business intelligence software. Others may use programming languages and tools that have various statistical and visualization libraries, such as Python, R, Excel, and Tableau. Other skills include:
- Creative and analytical thinking
- connections
- Querying the database
- Data analysis
- Data cleaning
Is coding necessary to become a data analyst?
Some data analysts are proficient in programming languages, while others may use analytics software or Excel to analyze data and provide insights. Whether coding is required for a data analyst usually depends on the job or employer. Employers may or may not list programming as a required skill for data analysts in job listings. It is important to look at the job description and consider your background before applying.
Data Analysis vs. Data Science vs. Business Analysis
The difference in what a data analyst does as compared to a business analyst or a data scientist comes down to how the three roles use data.
- The data analyst serves as a gatekeeper for an organization’s data, so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master’s degree in analytics, computer modeling, science, or math.
- The business analyst serves in a strategic role focused on using the information that a data analyst uncovers to identify problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance.
- The data scientist takes the data visualizations created by data analysts a step further, sifting through the data to identify weaknesses, trends, or opportunities for an organization. This role also requires a background in math or computer science, along with some study or insight into human behavior, to help make informed predictions.
At startups and other small organizations, it is not uncommon for a data analyst to take on some predictive modeling or decision-making responsibilities that may otherwise be assigned to a data scientist.
Conclusion
- Data analysis means a process of cleaning, transforming, and modeling data to discover useful information for business decision-making
- Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
- Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
Depending on your organization and objectives, you may require a data analyst, a business analyst, or both to take your business to the next level. If you are uncertain about which role you need to fill, contact KSRA for helpful insights. We are a team of software engineers and data scientists with an extensive portfolio of successful cases. We are happy to share our expertise and help you make the right decision about optimizing your business and hiring the right person.
Skilled data analyst with more than 25 years of industry experience in collecting, organizing, interpreting, and disseminating various types of statistical figures. Energetic presenter and confident communicator with the ability to circulate information in a way that is clear, efficient, and beneficial for end-users. Creative in finding solutions to problems and determining modifications for optimal use of organizational data. Expert at providing realistic projections and establishing various scenarios to determine viable process strategies to utilize. Organized and timely in providing staff, departmental members, and executive management with reports on specific data findings and their impact on organizational growth and success.
As an Oracle Database developer, I generally use Oracle technologies to build and maintain databases. My Key tasks include launching database systems, modifying storage capacity based on company needs, ensuring database security, consulting with clients, and providing technical support. I have good qualifications like Oracle database expertise, programming skills, especially with Apex, time management, communication, and attention to detail. I have a Bachelor’s Degree in software engineering.
Executive-level IT strategist with over 20 years of experience in successful IT, product management, and business information systems initiatives. Senior liaison and progressive leader with the ability to plan broad-ranging initiatives while understanding impact and requirements from both business and technology perspectives. Strong employee advocate dedicated to aligning staff skills with product needs, and mentoring personnel in growing skills and career paths. Well-versed in a variety of design, development, and project management methodologies (i.e. SAFe Agile/Scrum, Lean Six Sigma, CMMI, UML, etc.), with a talent for guiding business process improvement through the adoption of industry best practices and standards.
