What is the Career Track of A Data Analyst? A Comprehensive Guide

It is common for some learners to wonder about the Career Track of A Data Analyst. Many students complete relevant courses and do not have an idea where their professional journey leads. This point sheds light on the lack of awareness of the opportunities at hand for a data analyst. This article will help you learn the hierarchy in the data analytics field and its basics. Take your time to consume the content and make the best out of the data analytics stream.

Data Analytics

A stream that requires data professionals to collect data from different sources and analyze it to draw useful insights to help the management in making business improvement-related decisions is called data analytics. The data analysts perform analysis with the employment of various tools and techniques. 

Data Analytics Types

The majority of companies implement four kinds of data analytics, and they are. 

Descriptive Analytics:

Descriptive analytics is considered the easiest form of data analytics by the data professionals. This is also a foundation on which the advanced versions are built. If you are a data analyst, you will use raw data to pull trends to describe the organization’s past or current situation. To put it short, you will describe what is happening in a company using descriptive analytics. This kind of analytics involves the use of data visualization to present trends in the form of graphs, maps, and charts.

Diagnostic Analytics:

Have you heard about the term diagnosis? It is a word used to find the root causes of a disease in the medical field. We use the word “diagnostic analytics” for the same in the data stream. However, here we try to find out why patterns and trends took place in a company. We focus on understanding the relationship between variables. The main purpose of diagnostic analytics is to identify the root causes of issues in an organization. 

Predictive Analytics:

The name of the data analytics type is self-explanatory. This kind of data analytics is used for predicting future events, and trends, and answering questions like “What will happen next?”.  Data analysts carry out predictive analytics to figure out strategies that can help a company thrive in the future or more like prepare it for unforeseen situations. 

Prescriptive Analytics:

You might have seen a doctor’s prescription at least once in your lifetime. It is a paper with the names of medicines that are useful in curing the illness of a patient. Similarly, prescriptive analytics is a form of analytics that concentrates on one point, and it is “What can/should be done to improve the performance of a company?” 

To make it simple for you, it is the decision-making phase in the company. Machine-learning algorithms are considered to find trends and patterns from large volumes of data and recommend the next course of action to fix the issues or optimally utilize the current opportunities. The algorithms that have “if” and “else” commands will filter the data. If a dataset meets the criteria, the algorithm will proceed to recommend the next step for handling the situation. Machine-learning algorithms are more than the “if” and “else” statements. A data analyst who will be trained in algorithms will also be exposed to mathematical equations.

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Career Track of A Data Analyst

Let us look at how you will progress in data analytics and the list of responsibilities you have to perform for each role. The below-mentioned Career Track of A Data Analyst may vary from person to person and as per their hard work and skillset.  

Entry-Level Data Analyst: 

If you are an entry-level data analyst, you will perform tasks that are usually performed by your seniors.  Your trainers. . i.e., your seniors will teach you to carry out day-to-day responsibilities like them, but your tasks will be simpler and weigh less. 

Responsibilities

You will be expected to finish tasks that are similar to the activities performed by your trainers. However, you will be given less responsibility. Some of the common tasks are.

• Data collection: You must collect data on your own. Conducting surveys and purchasing data collection will be your job.
• Data cleaning: Your next task would be to filter the data to ensure it is free from duplicates and errors. 
• Data modeling: Add a format or give a structure to the collected and cleaned data.
• Data analysis: Whatever your position may be, if you are assigned the activity to analyze data, you are supposed to find trends and patterns to interpret data that can be a starting point to draw insights to assist a business in achieving its objectives. 
• Data visualization: Drawing insights is not the final responsibility of a data analyst. They must present their findings to the stakeholders in an understandable way; by using data visualization techniques.

Junior Data Analyst 

If you are a Junior Data Analyst, you will have the following responsibilities to deliver. Make sure you gain essential skills to efficiently perform them. 

Responsibilities

• Manage and merge many Excel data files. Ensure that there are no errors or data corrupted in the process of merging files.
• Carry out detailed data analysis using a programming language, preferably SQL in data sources like single customer view. 
• Perform data analysis to manipulate the data in Excel and send it to clients.
• Assist your team and data-management linked departments in managing data including providing data inputs.
• Collect, track, and trend forecast data to allow the development of production or manufacturing plans for Med Device business operations. 
• Work closely with all supply chain partners and materials management teams that deal with the management of finished goods supply and pipeline.
• Submit demand and supply volume input and analysis to departments that are related to capacity utilization and customer service.
• Demand and supply data manipulation, entry, reporting, and analysis.
• Maintain good connections with your team members and relevant internal departments to make sure there is no miscommunication of data.
• Present data outputs to management and partners when needed.

Data Analyst

The next position after Junior Data Analyst in the Career Track of a Data Analyst is the data analyst role. A professional who organizes data related to market research, sales numbers, linguistics, logistics, and other aspects is called a data analyst. You will utilize your technical expertise in the subject to transform information into high-quality and accurate data as an analyst. You will then be assigned the task to analyze, design, and present data to assist businesses, organizations, and individuals in making better decisions. 

Responsibilities

• Use automation tools for the extraction of data from secondary and primary sources. 
• Remove corrupted data and fix coding errors along with other related problems.
• Develop and maintain databases and systems; reorganize data in a usable format.
• Perform analysis to check the meaning and quality of data.
• Filter data by reviewing performance indicators and reviewing reports to find and rectify code problems.
• Use statistical tools for identifying, analyzing, and interpreting patterns and trends from complex datasets for diagnosis and prediction.
• Assign a numerical value to applicable business functions to make the assessment of performance and compare it with history when required. 
• Analyze local, global, and national trends that can impact the industry and organization. 
• Prepare and send reports to the management containing patterns, predictions, and trends.
• Work with engineers, management heads, and programmers to identify growth opportunities, recommend system modification, and create data governance strategies.  
• Prepare financial analysis reports to help the stakeholders understand the steps involved in data analysis and make them self-sufficient to make decisions based on the various trends and facts. 

Senior Data Analyst

A senior data analyst is usually occupied with data analytics projects. They act as mentors for data analysts and suggest analytics best practices. They are invited to meetings where the same-level attendees discuss strategies for the improvement of business performance through the extraction and conversion of data for decision-making. 

Responsibilities 

• Collate, clean, and analyze data for specialized or key analytics projects.
• Identify data insights and trends to solve immediate and significant business problems.
• Code programs to capture and organize data as per the relevance.
• Manage the activities of data analysts and find optimization opportunities. 
• Directly collaborate with external and internal clients to fulfill analytics demands.

Data Analytics Manager:

As the name suggests, a senior-level professional who manages various teams related to data analytics is called a data analytics manager. They often lead a big data project and can carry out the below responsibilities. 

Responsibilities

• Research and create effective methods to gather data.
• Analyze information.
• Recommend solutions for a business problem.
• Motivates data specialists to efficiently complete projects.
• Communicate with managers from various departments in a company to create strategies and achieve goals.
• Manage project schedules and forecasts for upcoming developments and execute technological improvements of databases of a company.

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Data Scientist

You can become a data scientist as part of a career transition or a parallel path. Though many people assume that data analysts and data scientists have the same responsibilities, it is not true. The work of both professionals varies. You will understand the same after reading about the responsibilities of a data scientist. 

Responsibilities

• Management: Believe it or not a data scientist has a managerial role. If you are a data scientist, you will support the development of the base of technical and futuristic abilities within the analytics and data field. The purpose behind the same is to help different continuing and planned data analytics projects. 
• Analytics: You will have a scientific role in planning, implementing, and assessing high-level statistical strategies and models to apply to complex issues in the business. You will be expected to develop statistical and econometric models for a variety of problems including classification, projections, pattern analysis, clustering, simulations, and sampling.
• Strategy: Your key responsibility will be to understand the business management and consumer trends to fix complex business problems. 
• Collaboration: You may need to collaborate with superior/expert data scientists to communicate findings and obstacles to stakeholders with the goal of promoting decision-making and improving business performance. 
• Knowledge: You also have to become a leader and explore different tools and technologies to create innovative insights for the business. One of your roles is to take the initiative to assess and utilize the latest and enhanced data science techniques for the business. 

Data Analytics Director

The Data Analytics Director is known to supervise the analysis, reporting, and collection of data within an organization. They have many years of experience in the data analytics industry. They have the power to influence decision-making in the company. They regularly communicate with non-technical and technical staff to establish collaboration between departments to support non-technical employees in applying analytics to the decisions and activities of an organization. All the actions of the analytics department will be reported to the directors which is they are held accountable for any mistake. 

Responsibilities

If you are a data analytics director, you have to ensure that the analytics team meets its goals by working efficiently through following the industry standards. You must manage the assigned team both as a group and individually. You may have to interact with the team members on a one-on-one basis to discuss their professional development and progress. Some of your key responsibilities after reaching the data analytics director are listed below.

• Create analytics reports to help managers in making decisions that are related to business performance and strategy. 
• Use data analysis methods like pattern recognition, predictive data modeling, and statistical testing. 
• Use non-technical language to share the outcomes of analytical studies with managers and related employees.
• Make recommendations to analysts on processes and strategies to improve the efficiency of the business.
• Analyze data for trends and patterns that reveal meaningful insights for the improvement of business operations. 
• Develop innovative methods of utilizing business data for informed decision-making.
• Monitor patterns and trends within the industry to find developments in data modeling and data analytics.
• Recommend new ideas and products based on customer trends and customer data. 

Skills to Become a Data Analyst

You will be successful in achieving the above-mentioned Career Track of A Data Analyst. But you need to acquire a set of skills that will let you get started as an entry-level analyst and they are.

SQL:

SQL stands for Structured Query Language. It is a language used in the data analytics field. If you want to question a set of data, then learn SQL. Having the skill to use SQL will assist you in collecting specific data and updating data. One of the main reasons for a rise in demand for SQL is the limited kind of database processing feature of Excel. You can process a large volume of data that was missed by spreadsheets. This is why many employers search for the keyword “SQL” in a candidate’s resume. 

Spreadsheets:

Though large volumes of data cannot be processed by using spreadsheets. It is still important to start learning Excel because it has features that can present the data in the form of reports and visual elements. Though spreadsheets were introduced way earlier, they are easy to use and great at handling data. This is why many organizations continue to use spreadsheets for a variety of database functions. 

Domain Knowledge:

A domain is a field in which you choose to work. Knowing the nature of operations in the field is important to make a career decision. You cannot enter a stream without knowledge and try your luck. It is one of the craziest approaches to earning bread and butter and it is not advisable unless you are rich and are free from responsibilities. When you know the concepts in data analytics, you can climb step by step and get on the Career Track of A Data Analyst. You must have knowledge about the basics of data analytics to succeed in the industry.

Machine-learning:

There is no rule that data analysts must be perfect in executing machine-learning algorithms, but it is good to have the skill to use machine-learning in data analytics. The algorithms in machine learning are developed to identify trends in big data sets. The algorithms when executed led to accurate data processing. In simple words, if you acquire machine learning skills, you will outsmart the rest. Why not get on the Career Track of a Data Analyst early?

Data Visualization:

It is not enough to have the ability to draw insights from a dataset. You must also be able to present the findings in an appealing way. If your presentation of data is not understandable or is too complex to comprehend, your efforts may go in vain as the one with good visualization skills may take credit for your hard work. The ability to present data in the form of charts, graphs, and other visual elements will help you reduce the burden of over-explaining findings and allow the audience to grasp the data at a faster pace. 

Research:

Data analysis is not just analyzing the given data. It also requires you to research and gather important data from relevant sources. It means you have to acquire research skills to collect data as quickly as possible and complete the analysis within the deadline. This is an essential faculty because when you are new to the industry and are not familiar with data visualization techniques, you can use your research ability and present findings in a better way. You can also use the skill to find out common kinds of stakeholders and ways to present data to them. If you want to smoothly travel the Career Track of a Data Analyst, acquire research skills. 

Conclusion

Many learners enter data analytics without understanding the scope of the field. When you know the Career Track of a Data Analyst, you can make career choices accordingly. For example, if you want to become a data analytics director, you can gain additional skills such as leadership and project management skills besides the basic faculties. On the other hand, if you are only focused on becoming a data scientist, you can avail of data science certification programs apart from completion of the data analytics course. 

If You Have the Following Skills, You Can Climb the Hierarchy of the Data Analytics Department in a Company with Ease.

• SQL
• Spreadsheets
• Domain knowledge
• Machine-learning
• Data Visualization
• Research
• Leadership skills
• project management skills

The Career Track of a Data Analyst can be as follows.

• Entry-Level Data Analyst
• Junior Data Analyst 
• Data Analyst
• Senior Data Analyst
• Data Analytics Manager
• Data Scientist
• Data Analytics Director

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FAQs

Q. What is the Career Track of A Data Analyst?

The journey of a data analyst can start from an entry-level data analyst and reach the highest designation which is Data Analytics Director. But to reach that position, you need to cross all the intermediate levels and acquire skills of a senior official which may include leadership and project management skills. 

Q. What skills will I need to start working as an entry-level data analyst?

You can become an entry-level data analyst if you complete a course and acquire the following list of skills. 

• SQL
• Spreadsheets
• Domain knowledge
• Machine-learning
• Data Visualization
• Research

That being said, the criteria for hiring a data analyst may differ from company to company. It is best for you to go through the job description and accordingly apply for the position. 

Q. What are the common kinds of data analytics used in companies?

Most companies usually choose one type of data analytics or a combination of a few from the below. 

• Descriptive analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics