If you are among those people who want to build a career in the field of Data Analytics and science then you are going to find this guide immensely helpful as it will take you through all the necessary details and particularly answer your queries on Data Analytics Entry-Level jobs. In this article, we are going to talk about 6 Data Analytics Entry Level Jobs To Kickstart Your Career. So keep reading to understand the different roles a Data Analyst has to play along with the requirements for certain job roles.
What is Data Analytics?
All companies are collecting data every day and some of them already have huge databases. This data serves no purpose until and unless someone can understand the data and make sense of the raw data. This is where Data Analytics comes into action.
Data Analytics is the process of analyzing raw data to draw meaningful conclusions and actionable insights to help better decision-making and also drive business growth.
It is the duty of a Data Analyst to gather all the available data, organize it, and then analyze it to give business intelligence. By drawing conclusions from the findings a data analyst will give suggestions or recommendations for the progress of the company or business.
When a company faces specific problems or challenges, a data analyst steps in and gives practical solutions to the current problems by analyzing the data trends. For example, a data analyst will help with identifying customer behaviors and patterns, the UX of certain tools, etc.
Data Analytics allows you to make informed choices based on what the data is telling you rather than making decisions on guesswork. When you are armed with insights based on data, you are able to understand the needs and preferences of your audiences, your competition, and your business as a whole much better and plan accordingly.
So if you find the field of Data Analytics exciting, and thinking about a career as a Data Analyst, then the 6 Data Analytics Entry-Level Jobs to kickstart your career will surely benefit you.
What Are the Different Types of Data Analysis?
There are Four main types of Data Analysis:
1. Descriptive Data Analytics
A simple and surface-level type of analysis is known as Descriptive Data Analytics. It is about analyzing past data. Data Aggregation and Data Mining are the two main techniques that are used in descriptive analytics.
The aggregation part involves the data analyst collecting all the available data and presenting it in a summarized format and the mining part is where the analyst will dig deep to discover patterns.
Then the findings are presented in simple language for a wide range of audiences to understand. The purpose of Descriptive Data Analytics is to determine and describe the “what”.
2. Diagnostic Analytics
Diagnostic Analytics involves exploring the question of “why”. While doing Diagnostic Analytics data analysts will try to locate any anomalies present within the data. For instance, if there was a sudden drop in sales in a particular month the analyst will try to figure out the cause.
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3. Predictive Analytics
Predictive Analytics is all about predicting what is likely to happen in the future just as the name suggests. Predictive analytics is important for business decisions as it eliminates guesswork. Even though predictive analysis can never be completely accurate but it does give data-driven insights about what the next steps of a company should be.
Predictive analysis is useful in all the areas of a business from deciding what products will be popular at a particular season to estimating the revenue increase or decrease for a given time period.
4. Prescriptive Analytics
Following Predictive Analytics, Prescriptive Analytics builds on the actions and decisions that should be taken. Prescriptive Analysis guides a business on how to take advantage of the situations that have been predicted with the help of Predictive analytics. Prescriptive analysis will guide the different actions that should be taken by a business.
Even though Prescriptive analysis is a more complex type of analysis, it can have a great impact on the company’s decision-making process and also on its revenue. All of these processes are to be implied when you will be working in data analytics entry-level jobs.
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What It is Actually Like to Work as a Data Analyst?
The role of a Data Analyst will vary from business to business. Below we have listed some of the typical day-to-day tasks that a Data Analyst has to do at data analytics entry-level jobs:
- Manage user satisfaction surveys and data visualization software.
- Work with businesses to develop requirements, define success metrics, manage and execute analytical projects, and evaluate results.
- Identify opportunities for improvement through monitoring of practices, processes, and systems.
- Translate important questions into concrete analytical tasks.
- Gather new data to answer client questions.
- Application of analytical techniques and tools to extract and present new insights to clients by using reports and interactive dashboards.
- Translate complex concepts and data into visualizations.
- Do team collaborations to find the best product solutions.
- Design, build, test, and maintain backend code.
- Set up data processes, define data quality criteria, and implement data quality processes.
- Ensure that the data being recorded is accurate by building data validation models and tools.
- Take part in teamwork to evaluate and analyze key data that will be used to shape future business strategies.
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6 Data Analytics Entry-level Jobs to Kickstart Your Career
1. Entry-Level Operations Analyst
Roles you need to perform as an Entry-Level Operations Analyst:
- As the name suggests you will be working on the operational aspects of a business.
- As an Entry-Level Operations Analyst, you will be analyzing the effectiveness of the different day-to-day business operations and measuring their effectiveness.
- Also, it will be part of your job to suggest changes for the betterment of different operational procedures and also to increase their effectiveness at the same time.
The nature of the business that you are working for will greatly influence your job roles. For instance, if you are working for an e-commerce business, you will be looking after the warehouse and delivery operations. On the other hand, if you are working in a bank, customer acquisitions and underwriting processes will be your areas of concern.
Requirements for being an Entry-Level Operations Analyst:
- A strong ability to study business processes
- Analyzing and making sense of complex data sets
- Can make strategic business decisions with the help of insights from the available data
- Efficient in critical thinking
So an Entry-Level Operations Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
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2. Junior Quantitative Analyst
Roles you need to perform as a Junior Quantitative Analyst:
- This job demands number-crunching
- Provide valuable insights to boost the profits for the business
There is a huge demand for Quantitative Analysts in the Finance Industry. They are hired to architecture algorithms to help with the venture of capital firms, hedge funds, and investment banks, assessment of opportunities for investments, measure the risk factors, and detect frauds.
Requirements for being a Junior Quantitative Analyst:
- You have to be strong in Maths
- Good skills in Statistics are also a must
A Junior Quantitative Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
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3. Entry Level Healthcare Data Analyst
Roles you need to perform as an Entry Level Healthcare Data Analyst:
- There are different positions available in the Healthcare Sector for a Data Analyst.
- If you are working in Healthcare Research, you might have to take up the core role of a Healthcare Data Analyst. However these kinds of positions typically require a healthcare background.
- You can also look after the operations of hospitals and other healthcare service providers.
- There is also the opportunity to work with public health bodies to provide them with regulatory frameworks based on data and also perform data analysis for these bodies.
Requirements for being an Entry Level Healthcare Data Analyst:
- You need to have knowledge of image processing and image classification if you want to apply for job roles in the healthcare research sector.
- You should also be confident and comfortable in working with large volumes of data sets.
An Entry Level Healthcare Data Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
4. Junior Financial Analyst
Roles you need to perform as a Junior Financial Analyst:
- When seeking a job as a Junior Financial Analyst you will find most of the jobs in banks, hedge funds, or insurance companies.
- There are also a lot of vacancies for this job role in venture capital firms.
Requirements for being a Junior Financial Analyst:
- This is a number crunching role therefore strong skills in maths are a must as you will be working with quantitative data.
- Foundational concepts of the financial sector.
- The knowledge of Python language.
A Junior Financial Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
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5. Junior Business Intelligence Analyst
Roles you need to perform as a Junior Business Intelligence Analyst:
- You will be working on projects analyzing the data for market trends, products, and data.
- Understanding the industry and the requirements of the company you are working for quickly.
- You will also have to be good at digging up all the business data and bringing it together for a deeper analysis.
Requirements for being a Junior Business Intelligence Analyst:
- Basic skills in data analysis are most of the time sufficient for this type of role.
- Sourcing data and building data storage structures are also important skills for being a Junior Business Intelligence Analyst.
- Some roles might require knowledge of special business intelligence tools like Domo or Rapid Insight.
A Junior Business Intelligence Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
6. Entry Level Manufacturing Analyst
Roles you need to perform as an Entry Level Manufacturing Analyst:
- You will have to gather data from the manufacturing units do a thorough analysis of all the gathered data and generate insights to guide business decisions.
- You will have to work with sensors and other devices used to generate data insights.
- You will also be in charge of identifying other key data sources on the factory floor.
Requirements for being an Entry Level Manufacturing Analyst:
- You have to understand the functioning of the factories.
- Spending time on the factory floor to gain real-time experience.
- Knowledge of basic data analytics tools and programming languages such as Python and R for the analysis of the available data.
An Entry Level Manufacturing Analyst’s job is one of the 6 Data Analytics Entry-Level Jobs to kickstart your career.
Data Analytics Skills That Will Help You to Get Hired in Data Analytics Entry-level Jobs
Data Visualization- It is the process of presenting data in the form of graphics or other illustrations. The purpose of Data Visualization is to explain data-driven insights in a simpler form for a better understanding.
Data Visualization is an extremely necessary skill and according to a survey by LinkedIn Learning, it was reported to be the number one skill.
Data Cleaning- It is a critical step and often takes up a significant time of a data analyst’s working hours. When a properly cleaned dataset is available even simple algorithms can give remarkable insights. Uncleaned data will mislead conclusions and therefore data cleaning skills are necessary.
MATLAB– MATLAB is a programming language that allows analysts to save the time they usually spend pre-processing data and this facilitates quick data cleaning, organization, visualization and businesses interested in big data have begun implementing MATLAB because of it. MATLAB can be executed in any machine learning model built in its environment across multiple platforms. The knowledge of MATLAB isn’t a necessary skill but keeping in mind its usefulness, having at least a working understanding of the knowledge improves your chances of getting hired.
R- R is one of the most widely used programming languages in Data Analytics. This language is very appealing to businesses as it can handle large sets of data as well as complex datasets. Keeping in mind the popularity and functionality of R, learning this programming language should be on the high-priority list if you want to land a job in data analytics.
Python- Python is a high-level, general-purpose programming language, and learning this language should be the top priority for would-be analysts. In the current AI-influenced professional landscape, the importance of Python is increasing each day.
SQL and NoSQL- In modern analytics, SQL is regarded as the standard means for querying and handling data in relational databases. SQL has a foothold in companies everywhere as well as has a high demand among companies because of its functionality and maintained effectiveness and its popularity shows no sign of coming down any soon.
You should be learning SQL if not for its function, then to increase your job prospects. You should familiarize yourself with branded versions of SQL such as MySQL which offers opportunities for gaining a greater understanding of relational database management systems.
NoSQL databases should also be given equal importance. As per the name, NoSQL systems don’t organize their data sets along SQL’s relational lines. NoSQL frameworks can effectively structure their information in any way when the provided method isn’t relational.
Machine Learning- Having a general understanding of Machine Learning and its related tools and concepts will give you an edge over other fellow job seekers. Even though all data analysts may not be working on machine learning projects but will definitely help you in getting hired.
Linear Algebra and Calculus- If you want to be a Data Analyst, you have to have strong mathematical skills. Linear Algebra and Calculus are the two specific fields the importance of which is non-negotiable in data analysis. In machine and deep learning linear algebra is applied whereas to build objectives, cost, and lost functions calculus is used. Most of the time you won’t be needing a very strong theoretical foothold for real-life applications.
Microsoft Excel- Microsoft Excel is a well-used tool among businesses as it is very good at automating certain features and commands for better data analysis. Excel has its own programming language which is VBA. Excel uses VBA to create macros or pre-recorded commands. When VBA is deployed correctly, it can save human analysts a lot of time on frequently performed and repetitive projects such as accounting, payroll, or project management.
Key Soft Skills:
Only technical skills can make you a successful Data Analyst. There are many soft skills that are required for a successful career.
Critical Thinking- You have to not only look at data but also make sense of it and explain its applications beyond numbers. You have to have the skill of critical thinking to think analytically about the data identify the patterns form actionable insights and give valuable information for better business decisions.
Communication- You must be capable of explaining your findings to others if you want to be a good Data Analyst. To put it in simple words you have to become “bilingual” that is having the capability to address highly technical points to trained peers as well as provide clear and easy-to-understand explanations to the business owners or the decision takers. If you are unable to do so, you have to still build your skill set as a data analyst.
Teamwork- You should be a good team player.
Domain knowledge- Good knowledge of the domain is also necessary.
All of the above-discussed technical and non-technical skills will help you to land your first data analytics job.
❖ What is the role of an Entry Level Data Analyst?
In large data analytics projects, an entry-level data analyst looks after the smaller sections of the large project. They primarily play the role of collecting and analyzing complex data sets. Their key focus is to generate useful insights to help the company make better decisions and improve its strategies.
❖ How hard it is to get hired for Data Analytics Entry-Level jobs?
No, it is not hard to get hired for data analytics entry-level jobs if you have the right skills and the correct mindset. Companies also hire people who have attended Data Analytics Bootcamps but have to clear the company interviews. When it comes to getting a job companies primarily focus on the knowledge and skills of a candidate rather than a college degree. You should build a good and attractive Data Analyst portfolio and also mention your own real-world projects to increase your chances of getting hired.
❖ Is Coding necessary for a Data Analyst?
It is not necessary for all Data Analysts to know the language of coding. Some Data Analysts use mathematical analysis whereas some may prefer Microsoft Excel as their primary analytics tool. But the knowledge of Coding will give you an upper hand at your workplace and will make you a multi-tasker.
❖ How are Data Analytics and Data Science different?
Even though the terms Data Analytics and Data Science are used interchangeably, they are two very different fields that lead to two different career paths. The key difference between these two fields is how they treat the available data and make the deductions to reach a final outcome.
A Data Analyst is called in when the organization has already identified its problems and challenges and the analyst takes over the job to identify complex datasets and give suggestions to the organization on means to overcome the challenges by means of data-driven strategic decision-making.
On the other hand, a Data Scientist will work on what type of questions a business should be asking or can ask in the future. Their jobs include: designing new processes for data modeling, writing algorithms, devising predictive models, and also running custom analyses.
❖ How stressful Data Analytics entry-level jobs are?
As a job Data Analytics alone is not at all stressful and can turn out to be quite rewarding for those who enjoy this field. But if you feel overwhelmed by your workload you can always have a conversation with your manager. Data Analytics could be your dream career but to be successful in this field, you need to have the required skills. With consistent hard work and patience, you can surely reach your goals.