7 Entry-Level Data Science Jobs to Go For – [2023 Update]
Tags: datascience, dataanalytics, datascientist, dataanalyst
Published: 2023-02-20 08:46:30


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Data science is a topic that is continually expanding and changing, and sometimes it seems like a new use for data science is discovered every day. Data scientists are in high demand across industries, including banking and healthcare, making it a worthwhile (and lucrative) field to work in.

Although it's obvious that the subject of data science is expanding, getting started in it isn't as simple. Becoming a data scientist is more complicated than becoming a doctor or lawyer, two professions with entry-level six-figure salaries.

This is the reason we wrote this manual. We'll detail some of the most typical entry-level data science positions below. We'll also go through the fundamentals of what it takes to be a successful data scientist.

Is It Difficult to Get an Entry-Level Job in Data Science?

If you have the necessary skills, getting an entry-level data science job is not difficult. With the growth of data science, there are numerous options for those with little to no experience to enter the profession. Learnbay’s [data science course with placement](https://www.learnbay.co/data-science-course-training-in-bangalore) will help you become job-ready and land your dream data science job in top MNCs.

7 Data Science Jobs for Entry Level:

Many opportunities are available for those who want to begin their career in data science. Here is a list of seven entry-level positions to help you get started.

Data Scientist Intern:

A data scientist intern is a new hire who works alongside more seasoned data scientists to learn the ropes of the field.

Interns in the field of data science typically work on statistical analysis or data preparation. They might support the creation of brand-new algorithms, machine learning models, or even visualizations that demonstrate how data is used. Data scientist interns occasionally have the chance to work on analytics projects that are particular to the company where they are interning.

Intern data scientists should have previous experience using various programs, including Excel. Also, they must be familiar with programming languages like Python, R, or SAS.

Junior Data Scientist:

A junior data scientist is learning to gather, examine, and present data so that others may use it. They are fresh to the discipline of data science.

Analyzing, reporting, and conveying the analysis findings are tasks that a junior data scientist performs in the same manner as a senior data scientist. A junior data scientist is less likely to be in charge of projects, which is the difference. Also, they are less likely to have experience with sophisticated machine-learning models or massive datasets.

You'll require fundamental statistics and computer science knowledge to succeed as a junior data scientist. Also, you'll need to know how to use SQL databases and write Python code. Depending on the organization you work for, you can also require expertise in business analytics.

Junior Data Engineer:

A young data engineer seeking practical experience recently completed a data science program. They could be an intern, employed part- or full-time.

The position requires a thorough knowledge of data technology, including data collection, storage, and analysis. Unlike senior engineers, junior data engineers often work on smaller open-source projects and are given less authority.

A junior data engineer must understand how data is stored, processed, and displayed. You should be well-versed in the fundamentals of Python and SQL and have a solid foundation in machine learning, statistics, and mathematics.

Data Analyst:

The foundations of data analysis are well understood by a junior data analyst who is still learning to use such skills in a professional setting.

In order to learn how to interpret and apply various tools, junior data analysts frequently collaborate with senior analysts and analytics managers. They manage and analyze vast amounts of data. Also, they help other workers prioritize projects and clean up data so they can be finished quickly.

You must be meticulous, have excellent communication skills, enjoy working in a team setting, and successfully manage team projects if you want to get employed as a junior data analyst. Also, you should be familiar with fundamental statistical and probabilistic concepts and several data science programming languages, such as R, Python, SAS/SPSS, or SQL. Check out the job-ready [data analytics course](https://www.learnbay.co/data-analytics-course-training-in-bangalore) offered by Learnbay in partnership with IBM.

Junior Data Modeler:

A company's database structure is created and maintained by junior data modelers, who are entry-level data modelers.

A junior data modeler may also be in charge of other duties, including building triggers and indexes and creating tables, columns, and relationships between tables.

You should have a foundational understanding of relational databases, SQL, and query authoring to be employed as a junior data modeler. Also, you should be able to operate on various platforms, including SQL and Microsoft Excel.

Database administrator Trainee:

A junior database administrator has a limited scope of administrative duties but assists in managing a database-driven website or application.

A junior database administrator assists in running a database on a daily basis. They build new databases and tables, keep an eye on performance, and troubleshoot when their databases experience problems.

Younger database administrators must be able to create queries in several computer languages, such as Python or SQL. Additionally, they must be able to administer their databases using programs like SQL Management Studio or Toad.

Junior Machine Learning Analyst:

Junior machine learning analysts have a solid background in math and computer science, but they are still honing their data analysis skills.

Before going on to more complex subjects like neural networks and deep learning, a junior machine learning analyst will first become familiar with the many types of machine learning, including supervised and unsupervised machine learning. They will also study methods like linear regression and k-means clustering. A junior machine learning analyst will also expect to comprehend the value of data analytics and how it affects business choices.

You must have a solid understanding of statistics and probability to be hired as a junior machine learning analyst. You'll need to be able to interpret data and explain why your analysis is significant. Having a firm grasp of linear regression's limits is also crucial.

What Qualifications Are Needed for Entry-Level Data Science Positions?

You need a wide range of skills to get an entry-level data science job. Here are a few of the most significant.

Technical Skills:
You'll need some technical abilities if you're looking for work in data science. While some data science organizations employ those without prior skills, most prefer candidates who can program and handle data. The following are a few of the most crucial technical skills:

The capacity to create straightforward Python, Java, or C++ programs
Understanding of machine learning techniques, including clustering, regression, and classification
Excellent grasp of statistics and probability theory
understanding of MySQL, Postgresql, and MongoDB database systems

Soft Skills:
You must be able to explain your findings to non-technical stakeholders if you want to succeed as a data scientist. You'll need soft skills, such as the capacity to communicate effectively and present, to do this.

What Qualifications Are Needed for a Data Scientist Position?

You can create models using machine learning methods, train algorithms using labeled training sets, analyze the results, and find patterns in the data at hand as an entry-level employee for a company that incorporates data science into its business strategy.

To become a data scientist, you need all the necessary abilities and expertise. There are no formal educational requirements. You should be familiar with SQL databases like MySQL and PostgreSQL and computer languages like Python or R.

Because they tend to be rapid learners who are self-directed learners, many organizations hire entry-level individuals without degrees. You can get recruited without having a degree in data science or a [data science certification course](https://www.learnbay.co/data-science-certification-courses) if you have significant experience dealing with data sets but no formal schooling.