How To Get Your First Job As a Data Scientist In 2023

Get your first job as a data scientist

As a fresher, it’s tough to get a data scientist job in the data science field. But if we follow a strategy to prepare to learn the required skill set for the data science field. 

We can easily get the first job as a data scientist.

As said before, the learning path won’t be so easy. We need to spend a hell lot of time, or, in a way, we should spend dedicated time learning all the required skill sets to land in the data science field.

With the high buzz on this field, the online is filled with data science concepts and certification courses.

This post is not about the concepts and different certification courses. It’s more about the strategic road map for freshers to crack the entry-level data science job.

To set the expectations for you, this article will be more useful for the freshers who are about to pass out of engineering colleges or universities.

How To Get Your First Job As a Data Scientist #datascientist #machinelearning

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In this article, we will also provide you a list of free resources to crack the data scientist job.All the resources we are providing in this article is entirely free. In a way, we are trying to explain how to become a data scientist at zero cost.

This post takes a bit of time to read and to understand the strategy clearly. We suggest you cross-check your correct level in the data science preparation journey all the time. 

As the article will take time to thoroughly understand the preparation road map, grab a cup of coffee, and start reading the article.

Before we go further, let me give you the table of contents for this article.

Six level strategy for getting entry-level data scientist job

Six level strategy for getting entry-level data scientist job

We will present you with a 6 level strategy for freshers to crack the data science job. The only prerequisite is a passion for the data science field. 

It doesn’t matter if you do not have any computer science background, or your mathematical and coding skills are low. Still, this strategy works for all.

But it’s quite often people lose motivation in the middle of the preparation. So in the 6 level strategy. We are also going to explain how to handle such a situation and get back to the learning track like a pro.

The 6 level strategy as follows

  1. The motivation for the Data  Science field
  2. Data science concepts preparation
  3. How to apply the concepts to the real-world problems
  4. Portfolio building & networking
  5. Smart ways to search for data scientist jobs
  6. What next

Let’s walk through each level in the 6 level strategy.

The motivation for the Data  Science field

data science field

To get started, we need a little motivation, but in the process, we lose motivation and give up on the thing or skill we started learning. 

This happens not only with data science learning, anything you learn. When the difficulty level skyrockets, our mind gives singles and gives tons of reasons to leave. 

By the time you release the mistake, you lose a lot of time, which never comes back. 

So we need high energy in the form of motivation while learning, which never gives false reasons to your mind to give up. Even the difficulty level rises.

To keep the motivation or passion towards the data science field, we always have to remember the various data science applications which make this field super hot. 

When you feel down with motivation, always remember the data science application and imagine you were part of building such an application. Here I want to quote one famous quote.

When you feel like giving up, remember why you started.

So Let’s see the applications of this field.

Data Science Applications

Data science applications

Data science applications

We have various data science applications, the above are some of the listed applications which give glory to this field. 

Let’s see each application.


Data science applications are helping in tackling many diseases, also reducing the time to get approval for new diseases vaccination. 

Some of the applications predict patient disease more accurately than expert doctors. 

Let's take the current world pandemic, AI & Data science models are helping a lot in the vaccination creation and also in tracking the disease. 

If you are interested you can read a great article on ScienceDirect. Below are some of the insights taken for the article published in ScienceDirect with the title Artificial Intelligence (AI) applications for the COVID-19 pandemic.

Applications of AI & Data Science in COVID-19 Pandemic

  • Early Detection
  • Monitoring the treatment
  • Tracing for individual contacts
  • Projection of cases and mortality
  • Vaccines development
  • Reducing the workload of healthcare workers
  • Disease Prevention

We know the data science models outperform with more data. In healthcare, to build such models, we are having a lot of data to feed for building deep learning or machine learning models. 

The knowledge learned by the various models is unpredictable compared with the real doctors. This is one of the main reasons for these models to accurately identify patient disease than doctors.


In the entertainment segment, the applications are beyond our imaginations. We limited the data science applications in entertainment as just Netflix or Youtube recommendations. 

But a variety of applications are trying to learn more about customers to understand what kind of videos we like and what kind of music bites. Using these customers’ information they are showing the advertisement in the same manner. 

Recently the cricket legend MS Dhoni announced his retirement. Have you guys identify any changes in the social media ads. You could see a lot of MS dhoni videos are popping your news feeds with ads linking to various product promotions. 

Social Networking

Our day starts with surfing social networking sites and ends with surfing too. 

People are more addicted to social networking sites these days. The key reason is all the social networking sites know what you like and what you don’t like, so it’s so easy for them to keep to stick with their platform. 

Various data science applications in social networking sites the people you know. 

It’s called recommendation, and every social networking site has its own recommendation engine to recommend people you like or content pages and also suggests products you would like to purchase in the form of ads. 

Business growth

Every organization nowadays is moving towards data science fields to know their customer better. This helps organizations to provide more personalized products to users. 

Just imagine a lot of data science and decision science techniques are applied to the T-shirt you are wearing now.

Irrespective of the field, every business organization is seeking skilled people to grow the data science team in their organization. This gives opportunities for us if we are proficient with the minimum required data science skill.

AI & Robotics

When it comes to Robotics, I don’t need to say anything about data science's importance in this field. 

Even the ten years kid knows the importance of data science or artificial intelligence in building robots that looks and function like humans.

Like health care, a lot of research is happening in the Robotics field as well. At present, we have many robots that significantly do the work humans can do. This ranged from cooking, waiter jobs in restaurants, As soldiers in the defense filed, and lot more.

Space Research

In the space segment, the data science application is helping in creating the situations for the Rockets and also in predicting the likelihood of various components used to build rockets. 

In short, the intention is to remember the diversity of the field and the scope of this field before you give up and remember the applications of this field to get back to the learning track. 

We have seen various data science applications, now let’s discuss what the growth of the data science field is. We will address will this field sustain longer or it will saturate to early.

Growth of Data Science Field

The other thing to consider when we are stepping into any new field is the growth of that filed. If you go 2 decades back we used to have many job roles that did not even exist now. 

With the increase in technology, those jobs have vanished. They lose their significance. So When we are selecting any new domain or new field, we need to know the growth of the field, and we need to know will this field sustain for long or will it get to the saturation stage. 

According to the global level research report, the need for skilled data scientists is increasing continuously over the last 5 to 6 years. This continuous growth will continue for the next 10 years without any doubt. 

Now let’s discuss various roles available in the data science field. 

Possible Roles in Data Science

Even though data scientist jobs are so fancy in the data science field, we still are having many other job roles that fall under the data science field.

Let’s see different job roles that fall under this field and let’s understand what skill set we need to get the job in those categories. 

So you can focus on those areas rather than blindly learning all the stuff related data science, which leads you nowhere.

Data Science Possible Roles

Data Science Possible Roles

When you decide to get a job in the data science field, You don’t need to limit yourself to a data scientist job role. We are having various job roles in this field. 

  • Data Scientist
  • Data Engineer
  • Business Analyst
  • Data Analyst
  • Visualizer
  • Researcher

Let’s discuss the key skillsets for these roles.

Data Scientist

A Data Scientist role is to build various machine learning or deep learning models to solve complex business problems. 

Before building these models, data scientists spend a lot of time preprocessing the data to create the fruitful data for modeling. 

They also spend time with the domain experts to know more about the problem. If you join any big organization, the data scientist team will get all the required data from different sources, and this team will mainly focus on building accurate models.

To get the job in this area, you need to be good at different machine learning algorithms and deep learning algorithms along with good command with various databases. 

You need good experience with data preprocessing frameworks like pandas. We need to know the mathematical foundations to understand the various algorithms. You need to be very good at coding.

Data Engineer

The data engineer role is to collect the data from different sources and make it available to the data scientist team in a more structured form. 

This role requires very good coding skills like data structure, and you need to be very strong in MYSQL.

This role demands you to change the data from one source or table to another source. Sometimes you need to perform different data transformations on top of the data collected from various data sources.

For this role, you need to learn different Big data technologies like Hadoop, Spark, Kafka, different data streaming services and microservice, .etc.

Business Analyst

The business analyst job is to get quick insights from the data. They work in Poc projects to analyze minor data to check the feasibility of the solution.

In some organizations, these business analysts will take care of the data modeling too. They mainly interact with clients and stakeholders to collect business requirements.

Data Analyst

The data analyst role is a lower level for the data scientist role. They work a lot on the data preprocessing stage, in many organizations, the junior data scientist role is considered as a data analyst.

Visualizer/ Tableau Team

We have a saying

A picture worth a million words. 

In this field, we need to show to the available data in a story structure with great visuals to clients.

To visualize the data in a better way, we need visualizers. Who represents the data in a much reasonable way to address many business questions without building any machine learning models.

Also, the build models need to explain with proper storyboards about how the build model impacts the business. 

To get this role, you need to be very good at different data visualization tools like Power BI, tableau, and you also need to be good at the database queries. As to visualize the data, we need to pull the data from different tables and sources.


The data science domain offers many jobs in the R&d too. Many big organizations will have dedicated research teams. Who will build new modeling frameworks, optimizing the model building time for the current using modeling frameworks. 

To get this job, we need to hold a master’s or Ph.D. degree in mathematics or statistics or in Artificial intelligence.  

By now, we know about the field, and we also know the different roles we can target.

It’s up to you to know which role you wanna go for. First, select the job role you want to pursue and learn the required skill for that role. Don’t spend time learning all the data science skills.

Now let’s learn the concepts we need to learn to get the job in the data science field as a data scientist.

Data Science Concepts Preparation

How to prepare for data scientist job

How to prepare for data scientist job

When it comes to learning the new skill, the common question we would have in mind is

How much we need to spend to learn various data science skills?

Are you having the same question in your mind?

Don’t worry, if we are smart enough, we can learn all the topics, and we can acquire data science knowledge for free

In the last section of this article, we have given all the free resources to become data scientists.

Before that, let’s have a look at the concepts and tools you need to learn and master.

Data science topics

Data science topics

At a high level, you need to learn these four categories.

Coding & database

To get the data scientist job, you need to have a decent amount of coding skills. You need to be very good at MySQL or NoSQL. 

In the entry-level job for other engineering branches, fresher no need to focus much on the data structure and computer science algorithms, but coding is the most important thing to learn. 

As a personal suggestion, I would recommend spending more time coding. For every problem, you solve, try to check how you can optimize this code better. This helps in the long run to make you a better coder.

Preferred Coding languages for data science field

In the data science field, a cold war is happening in selecting the best programming language. Some people say Python is the best for the data science field. Whereas some people say, R is the best for the data science field.

Both these languages have their own advantages and drawbacks too. However, we have other programming languages like Scala, Julia, Octav, etc.

If you have enough time, we would suggest you learn both Python and R programming languages. In the end, it’s not about the programming language you selected. It’s all about the best model you build.

Again, If you are still in confusion about the programming language you want to select, pick any one of the below.

  • If you are starting fresh, start with python.
  • You already know python, go with python.
  • You know R, go with R.
  • If you know both, that’s great.

With this, we are clear about the programming language. Now let’s discuss databases.

When it comes to the database, you should be very strong in MySQL or NoSQL. 

If you learn MySQL properly, it will help you in cloud related platform databases also. For example, Google cloud will have a big query database. Likewise, AWS has Athena. Both databases run with MySQL queries.

In NoSQL, we are having MongoDB, Cassandra, etc.

Statistics concepts

Many people give a low-level look and spend very less time learning statistical concepts. But don't forget for all the machine learning and deep learning, the key building blocks are the statistical methods.

In statistics, we have to focus on the below topics mainly.

  • Algebra Concepts
  • Probability Concepts
  • Descriptive statistics
  • Inferential statistics

Machine learning concepts

Once you have completed learning the statistical concepts, you can start learning machine learning concepts.

You can focus on learning the machine learning concepts in the below order.

As a fresher, you can target to learn the supervised learning algorithms. Whatever algorithm you learn, try to learn everything about the algorithm.

You need to learn mathematical concepts. Why we have selected optimization functions, why not other functions, where the selected algorithm will fail, etc..

Deep Learning concepts

Once you get a good knowledge of the machine learning algorithms in both theoretical and practical, you can start learning the deep learning algorithm's structures. 

Deep learning algorithms use different neural network structures that mimic the behavior of the human brain. Like machine learning algorithms, deep learning is also divided into two categories, such as supervised and unsupervised algorithms.

Deep Learning Supervised Algorithms

Deep Learning Unsupervised algorithms

  • Self-Organizing Maps
  • Boltzmann Machines
  • Autoencoders

These are the basic structures of the Neural Networks. You can implement most of the Natural Language Processing and Computer Vision tasks using Deep Learning Supervised Algorithms. 

CNN architectures used for Computer Vision tasks and RNN used for Natural Language Processing tasks. If you're a beginner in the journey of deep learning, just focus on these algorithms first.

How to apply the concepts to the real-world problems

Solving data science challenges online

Once we learned all the required data science concepts and learned the required tools. The next big step is to apply the knowledge to solve the real-world problem. 

Because knowing concepts is different, applying the concept to solve the problem is different. Solving problems makes you a completely different person.


How to build the models to solve real-world problems then?

Don’t worry, I will give you the list of ways we test your knowledge.

The more you practice, the more you learn. It’s that simple.

To practice the problems you can focus on the below 3 categories.

  • Solve problems over online platforms
  • Participating in webinars/ YouTube live coding channels
  • Working on open source / own project 

Solve problems over online platforms

Unlike other software technologies like web development, android app development, you can’t create code and see the visualizing and perform the required changes while building models in data science. 

The machine learning model building is completely different, maybe which is one reason for the data science field popularity.

No need to worry. We have various platforms to solve problems near to the real world. In the next sections of this article, we will show you multiple platforms to apply data science skills along with the challenging levels.

You can register in the below platform.


For applying various algorithms and data preprocessing techniques Kaggle is the most popular platform to try out. After Kaggle acquired by google, Kaggle went to the next level in providing everything the data science aspirants need to have.

In Kaggle, you will get the problem statement and the required datasets to build any model you want. You are open to use any tool. The intention is to come up with the best model results. 

In the dataset section, Kaggle will provide both train and test datasets. Once you build the model, you have to submit the model predictions of the test data in the portal. 

This shows your rank on all the participants who participate in that problem. This rank is on the global level. So if you get a decent rank, you can showcase this. Kaggle also gives you the badges too. These are badges levels.

  • Gold medal
    • To get the gold medal, you need to rank in the top 10%
  • Silver medal
    • To get the silver medal, you need to rank in the top 20%
  • Bronze  medal
    • To get the bronze medal, you need to rank in the top 40%

In Kaggle, you can become an expert in different categories. If you become an expert in one category, you are called a Kaggle expert. If you become an expert in multiple categories, you are called a 2X expert, 3X expert, etc. 

Once the challenge is open for all the participants in the Kaggle platform, it will be open for months. Some problems will have years of time to come with the best model.


The other popular platform is machinehack. This is similar to Kaggle but comparatively, the difficulty low. The number of people who are participating in the computation also low. So it’s an ideal place for the beginner to get a low rank to motivate themselves.

Generally, once the data science or machine learning challenge is open for all, you will get 3 to 4 days of 1 week’s time to build the model and rank on the leaderboard. 

Analytics Vidhya hackathons

In this platform, the problems will be a bit lower level, the time you will get to solve also moderate. You can also learn various data science concepts on their blog page.


In HackerEarth various companies will keep the various data science, deep learning, and machine learning problems to solve. If you stand in the top rank, you will get a job too. 

These problems will have a bit of high complexity. Problems posting on this platform are  not so regular, So also please keep an eye on the platform. In HackerEarth, you can also learn data structure and SQL stuff too.

Suggestion for beginners

While solving these problems at the beginning level, it’s very hard and difficult to solve, So first try to check the winner’s solutions. Always learn from the winner code. For that, you can check the GitHub profiles. In Kaggle, you can find various notebooks to learn. 

Below is the graph for the difficulty level.

Data science online competition complexity

Data science online competition complexity

We have seen various platforms to practice building models. Now let’s see some popular YouTube channels to follow and webinars to participate. 

Participating in webinars/ youtube live shows

To learn how to solve various machine learning problems, you can follow popular youtube channels. Where people will explain how they solved the problem. Some time various organization contends webinars to show how they used various algorithms to solve real world problems 

In the free resources section of this article, we will give you a list of such channels to follow.

Working on a challenging project

Now comes the more challenging part. We applied our data science knowledge on various platforms but didn’t show those projects on our resume. These are only for learning purposes.

However, still, you can showcase the leaderboard ranks in your resume. We will talk more about this in the smart job search section.

So, once you are comfortable with the workflow of solving machine learning or deep learning problems, now it’s time to work on a challenging project. 

You can search for an open-source project if you are not getting any ideas, you can read research papers and try to improve the accuracy mentioned in the paper by fine-tuning the model with your approach.

In various platforms, you can meet light minded and talented people, and you can form a group and work on a single project.

Don’t limit yourself with accuracy or any model evaluation metrics. Try to create a complete end-to-end pipeline line for the model, from collecting the data from different sources to deploying the model in a cloud or production environment. 

Example: Suppose you are building an accurate image classifier, then from collecting images to creating a simple web application to showcase how the classifier performing the prediction on a given image is a complete end-to-end pipeline.

You can include machine learning, deep learning, or natural language processing technique to solve a challenging project. 

Example: To use both deep learning and natural language technique you can build a deep learning and natural language processing which writes a short story from the image.

In this specific project, you will extract the features from the image then convert then into text using the natural language processing techniques.

To summarize everything you can refer to the below image.

Data science online platforms links

Data science online platforms links

Now let’s discuss how to build the data science portfolio and how we need to do the networking.

Portfolio Building & Networking

How to build data science Portfolio

How to build data science Portfolio

Portfolio building is the key step to get the data science job. If you are not focusing on portfolio building, It’s similar to hitting the target in the darkroom.

Unlike other fields, a resume is not enough to get the job. You also need to have a stunning online profile.

Let’s discuss that.

Learn all the required data science concepts and been ideal won’t help in getting opportunities in this field. To get job opportunities from N different ways you need to create a stunning portfolio.

The advantage of having good data science portfolio will help you in getting job opportunities from the unbillable sources. This we will discuss more in the next section. For now, let’s learn how to create a dominating portfolio. 

To create a portfolio, we have various ways but you can start with the bellow once.

  • Sharing your projects online
  • Writing Articles
  • Connecting online/offline connection

Sharing your projects online

The best way to create an online presence is by sharing your project over online. Don’t limit yourself by keeping your project code in Github. Always share those links in social media networks too. You can try out all the below-mentioned platforms.

Once you completed the project kept all the project-related codes in Github. Don’t forget the write the readme file. In the readme file, you need to mention everything about the project.

You can include the below question and provide the answers to these questions in the readme file.

  • What the problem statement
  • Solution motivation
  • About the input data sources
  • How to run the project
  • How updated the code block
  • Including the new pipeline/ features in the current project pipeline
  • Research paper read
  • Solution architecture graph/image
  • Motivation/reference GitHub links
  • The accuracy details
  • Further improvement details

If you cover the above your readme file looks more promising and authentic. Don’t limit to these include other platforms that you feel important to showcase.

Writing Articles

To showcase your profile you can write articles too.

You can write about machine learning, deep learning, or natural language concepts or algorithms you are strong at. You can also write about how you have solved various data science projects. If you solved any online hackathons problems, you could write about that too.

For writing articles you can consider the below platforms.

If you are interested in guest posting in our blog, you can check out the join us page.

Connecting online/offline connection

When it comes to the connection, I always remember one quote, not sure who said this.

Your network (connection) is your net worth.

For getting the quality connection, checkout LinkedIn connections, you can use the LinkedIn filters to connect with the people you would like to connect.

Use LinkedIn as the main source for connection, Don’t sent requests on Facebook.

Smart ways to search for data scientist jobs

Smart data scientist job search

Smart data scientist job search

Now comes the key and the last level for getting the data scientist jobs. The Smart job search

The topics we will be discussing in this section will be helpful for all kinds of job searches. 

In this section, we will be discussing below.

  • Resume Preparation
  • Selecting Companies
  • Salary Expectation

Resume Preparation

The first step is to create the resume template filled with all your details. While creating the resume format keep mind freshers and experienced candidates should be different.

Freshers can follow this order in the resume.

  • Eduction details
  • Skillset
  • Certifications
  • Awards/Publications
  • Projects (with links)

Experience candidates can follow this order in the resume.

  • Company wise experience
  • Skillset
  • Eduction details
  • Certifications
  • Awards/Publications
  • Projects (with links)

Don’t throw away the same resume to all the companies. We should have the resume template based on the job description we need to update the resume. 

Suppose the job description needs an NLP skill set, you can change the order of the projects and keep the NLP project on the top, in the same we will include all the NLP skill sets into the skillset section.

Selecting Companies

While starting the career, focus on startups, but don’t blindly go for every startup. Before deciding to apply for the job, do your research on the company. 

Remember also that you don’t have to restrict yourself to companies within your own city, or even country. Now that remote work is commonplace, many startups are using services like Remote to recruit talent from all over the world, so you may consider remote overseas-based roles as well.

Based on your interest, you can create the below kind of sheet where you keep all the information about the company.

Company Name

Company URL

Experience Level Required



Company X

0 to 1 year

skill1, skill2

When you ready, you can start sending your resume to the collected emails.

Salary Expectation

While starting the career, you can expect a low salary, but focus more on learning. Also, do your research on the salary part before you are conveying your salary expectations. It should be too low.

What next

What next

Once you got the job, Always update your skill sets. As you are doing the job now, you can afford the various online course on Udemy, Coursera, Edx or Udacity.

Always help other aspirants, Share your learning through articles or post. If you are not interested in maintaining your own blog, you can join or write for other blogs.

Free resource to become a data scientist (Zero cost)

Online Courses


Free Books

Free Servers

Coding Resources

Database Learning Resources

Machine Learning | Deep Learning | Natural Language Processing Resources

Research Papers

Kaggle Popular Notebooks

Github Resources

Youtube Channels

Popular Youtube University Channels

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