How to Become a Data Scientist from Software Engineer in 2025?

As data is growing rapidly, the demand for data scientists is also increasing. This is the reason many people want to land in the data science field. So if you are a software professional and planning to switch from software engineer to data scientist, then you are in the right place. In this article, I am gonna share with you all the necessary information you should know and how to become a data scientist from a software engineer.

So, without further ado, let’s get started-

How to Become a Data Scientist from a Software Engineer?

As you are reading this article that means you are planning to switch from software engineer to data scientist. So the first piece of advice I would like to give you is to ask yourself-

Why do you want to be a data scientist?

You might be thinking why I am telling you to ask this question before switching your career…Right?. So let me explain…

The answer to this question will guide you towards your goal. If you want to change your career because you are really interested to solve complex problems and ready to learn something new every day, then I would say you should definitely switch from software engineer to data scientist.

But if you want to switch just because data science has a high salary and you are not comfortable with complex problems, then I would personally not suggest you switch your career. Because data science is not as easy as people think. So get your feet wet in data science only when you are really ready to learn and solve complicated problems.

But I’m not writing this to scare you away from starting this journey, I am just telling you the ground truth. So once you are clear with this question, you need to focus on 3 points in order to switch from software engineer to data scientist. And these 3 points are-

1. Understanding the Roles & Responsibilities and skills required for Data Scientist.
2. Knowing your abilities and knowledge as being a software engineer.
3. Filling the knowledge gap.

So, first let’s have a look at roles & responsibilities of a data scientist-

Roles & Responsibilities of Data Scientist-

Most of the time, data scientists work with business stakeholders to understand their needs and find out how to use data to accomplish those needs. Data Scientists outline the data modeling process, build algorithms and predictive models to extract the data, analyze the data, and present the insights with co-workers.

According to Indeed and Glassdoor (major job portals), these are the following roles & responsibilities of Data Scientist-

• Work with stakeholders to find out how to use business data for valuable business solutions.
• Collecting data through means such as analyzing business results or by setting up and managing new studies.
• Transferring data into a new format to make it more appropriate for analysis.
• Creating new, experimental frameworks to collect data.
• Create custom data models and algorithms.
• Use predictive models to enhance the customer experience, revenue generation, ad targeting, and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Preparing reports and presentations for business use.
• Correlating similar data to find actionable results.

Courtesy Indeed & GlassDoor

Now you knew the roles & responsibilities of a data scientist, now let’s see what skills are required to land into the data science field-

Skills Required for Data Scientists-

1. Programming Skills

In order to build a model, you should have knowledge of programming skills. So as a Data Scientists, you have to be comfortable in writing code in Python, R, SQL, and Java.

2. Statistics or Probability

Data science is all about extracting knowledge, making a prediction, algorithms, insights, etc. So for performing these operations, you must have a good knowledge of statistics.

3. Machine Learning

In order to build a model and enable a computer to automatically learn from data, you must have knowledge of machine learning algorithms like k- nearest neighbor algorithm, Random Forest, Naive Bayes, Regression, and more.

4. Multivariate Calculus and Linear Algebra

Knowledge of multivariate calculus is important because it helps you to build a machine learning model. These are some topics, which you should be familiar with in order to work in data science- Cost Function, Gradients, and derivatives, Sigmoid function, Step function, Plotting of functions, scalar-valued function, vector function, etc.

5. Data wrangling

Data wrangling is the process to clean the data and make it ready for analysis. The data which you collect is not ready for analysis, because it contains noise and this data is not in a proper format. So as a data scientist you should have knowledge of how to clean the data and make it ready for analysis.

6. Data Visualization

Data Visualization helps you to showcase your findings in a more precise way, so that end users can easily understand them. You can use a different type of visualization for your work like- Histogram, Pie chart, Bar chart, Scatter Plot, Time Series, Heat maps, and many more. There are different tools available for visualization work like-Tableau, Power BI, matplotlib, ggplot, etc.

7. Database Management

In data science, everything is close to the data. That’s why you should have knowledge of database management. A strong understanding of SQL is required.

8. BigData

In Data Science, you must have knowledge of how to deal with huge amounts of data and for that, you can learn the basics of Hadoop.

So these are technical skills that are required, but only technical skills are not enough, some soft skills are also required for data scientists. Because as a data scientist you have to communicate with business stakeholders and give a presentation of your findings. So some most important soft skills for data scientists are-

• Business intuition
• Analytical thinking
• Critical thinking
• Interpersonal skills

I hope now you have a solid understanding of data scientist responsibilities and skills. Now it’s time to know your potential as a software engineer and What are the common tasks and goals you and data scientists share?

Identify What You Already Know as a Software Engineer

As a software engineer, you have following skills-

• Knowledge of programming languages (Python, Java, C, C#, C++, and JavaScript).
• Familiar with Software Development Life Cycle (Data gathering, Requirement analysis, coding, testing, and deployment).
• Strong knowledge of Database Management System- RDBMS, SQL, NoSQL, etc.
• Data Structure & Algorithms.
• Basic understanding of developer tools like Git, GitHub, Azure, etc.
• Familiar with cloud technology- AWS, VMWARE, etc.
• Analytical & Problem solving skills.
• Communication skills.

Some of the skills match with Data scientist’s skills like programming knowledge, SDLC knowledge, Database knowledge, knowledge of cloud technologies, analytical and communication skills. So this is a plus point for you as a software engineer.

Now you know what is required for data scientists and what you already know as a software engineer. Now it’s time to take action and fill the knowledge gaps.

Start Learning the Skills that You Don’t have-

As a software engineer, you are not much aware of-

1. Machine learning,
2. Statistics,
3. Probability,
4. Multivariate calculus,
5. Linear algebra,
6. Data visualization,
7. Data ETL (Extract, transform, and load) methods to build continuous data pipelines
8. Big Data- Hadoop
9. Data science tools

So you have to learn these skills in order to fill the knowledge gaps and start your data science journey. Now you might be thinking about what to learn first and from where to learn?… Right…? No worries… In the next section, I am gonna discuss what you should learn first and from where to learn-

NOTE- I am assuming as a software engineer you already know Python, SQL, and cloud technology.

Step 1- Brush Up Your Math Skill

In data science, knowledge of multivariate calculus and linear algebra is required. There are lots of resources available online to learn math, so you can take the help of online courses. I have listed some popular resources to learn math for data science-

Resources to learn Math for Data Science-

• Khan Academy– This is a classic school-like tutorial. It helps you to refresh the math concepts and also has a few exercises.
• Data Science Math Skills (Duke University)– This course is specially dedicated to those who want to learn Math for the Data Science field. In this course, you will learn all the math required for Data Science. You can check all the course details here.
• Mathematics for Data Science Specialization (Coursera)– This is a Specialization Program dedicated to Data Science Math. In this program, you will learn Discrete Mathematics relevant to Data Analysis, Calculus, a linear algebra that is used in data analysis, and probability theory and statistics. You can check the course details here.
• Introduction to Calculus (The University of Sydney)- This full course is dedicated to Calculus. This course will teach you key ideas and historical motivation for calculus. You can check the details of this course here.

You can check this detailed article to get some more best math courses for data science- Best Math Courses for Machine Learning- Find the Best One!

Step 2- Learn Statistics & Probability

Statistics knowledge includes statistical tests, distributions, and maximum likelihood estimators. All are essential in data science. Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem. Now let’s see the resources from where you can learn statistics-

Resources to learn Statistics-

• Statistical Inference (Johns Hopkins University)– If you are thinking to gain statistics knowledge, then this course is best for you. After completing this course, you will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data. You can check the course details here.
• Basic Statistics (University of Amsterdam)– This is another course specially dedicated to Statistics. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. You can check the details of this course here.
• Statistics with R Specialization (Duke University)-This specialization program will give you more in-depth on Statistics with the help of R. In this program, you will learn how to analyze and visualize data in R and create reproducible data analysis reports, and much more. You can check the details of this course here.
• Statistics with Python Specialization (University of Michigan)- This specialization program is especially dedicated to statistics. In this program, you will learn the beginning and intermediate concepts of statistical analysis using the Python programming language. You can check the details of this course here.

For more statistics courses, you can check this article- Best Course on Statistics for Data Science to Master in Statistics

Step 3-Learn Machine Learning

Once you gain math and statistics knowledge, get your feet wet in machine learning. Some important machine learning algorithms are principal component analysis, neural networks, support vector machines, decision tree, logistic regression, and k-means clustering. You can learn machine learning from these resources-

Resources to Learn Machine Learning-

• Machine Learning (Andrew Ng)- This is no doubt one of the Best Online Course for Machine Learning. This course is created by Andrew Ng the Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. For more details about this course, you can check here.
•  Machine Learning with Python (IBM)- This is another Machine Learning course for Beginners. This course starts with the basics of Machine Learning. Python is used in this course to implement Machine Learning algorithms. For more details about this course, you can check here.
• Get started with Machine Learning (Codecademy)– This course starts with the basics of machine learning. After completing the basics of machine learning, you will work on 3 different projects- Handwriting Recognition, Sports Vector Machine, and Breast Cancer Classifier.
• Machine Learning A-Z™: Hands-On Python & R In Data Science -Udemy– This course not only teaches you the theory related to Machine Learning but also provide the implementation of each Machine Learning Algorithms. The best part of this course is that you will find implementation in Both Languages Python and R. 
• Deep Learning Specialization (deeplearning.ai)– This Deep Learning Specialization is an advanced course series for those who want to learn Deep Learning and Neural Network. Python and TensorFlow are used in this specialization program for Neural Network. This is the best follow up to Andrew Ng’s Machine Learning Course.

Step 4- Learn Data Visualization Tools

As a Data Scientist, you have to showcase your findings in a visual form, so that stakeholders can understand them properly. This is an important step for a Data Scientist. That’s why the knowledge of Data Visualization is important. And for that, you should be familiar with data visualization tools like ggplot, matplotlib, Seaborn, and D3.js.

You should have knowledge of any 1 Reporting tool like Tableau or power bi. You can learn Data Visualization from these resources. I would suggest you learn Tableau.

Resources to learn Data Visualization-

• Data Visualization with Python (IBM)- This course introduces a range of data visualization techniques like line graphs, pie charts, bar charts, and specialized visualizations like Waffle and Folium. In this course, you will learn various data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
• Data Visualization with Tableau Specialization (University of California, Davis)- In this specialization program, you will learn Data Visualization with Tableau. Tableau is the most powerful, secure, and flexible end-to-end analytics platform for your data. This specialization program is dedicated to newcomers to data visualization with no prior experience using Tableau. 

For more data visualization courses, you can check out this article- Best Data Visualization Courses Online- You Need to Know!

Step 5- Learn Big Data & Hadoop

As a data scientist, you have to deal with large amounts of data that’s why you should have knowledge of big data and Hadoop. For learning Hadoop, you can take the help of YouTube videos or online courses.

Resources for learning Big Data & Hadoop-

• Big Data & Hadoop Full Course– This course is by Edureka and available on YouTube. This course is good to get an idea of Big Data and Hadoop.
• Big Data Specialization (University of California San Diego)– In this course, you will learn through the basics of using Hadoop with MapReduce, Spark, Pig, and Hive.
• Apache Spark with Scala – Hands On with Big Data!– Udemy– This course will teach you the hottest technology in big data: Apache Spark using the Scala programming language.

For more Big Data Courses, you can check this article- 8 Best Online Courses on Big Data Analytics You Need to Know

Step 6- Start Practicing with Real-World Projects

It’s time to start working on some Real-World projects. Projects are most important in order to get a job as a Data Scientist. The more projects you will do, the more in-depth understanding of Data you will grasp. Projects will also provide more privilege to your Resume.

If you are not clear on where to start with Data Science Projects, you can go on Kaggle and choose any dataset. Once you have a dataset, you will get an idea of what you can do with it. You can choose projects on Datacamp.

Do this for a couple of projects, and then look up common problems on the internet and try to solve them by acquiring relevant datasets.

You can also take a part in Kaggle Competitions and try to get a rank between 1-100. If you get a rank between 1-100 and put it on your resume, then there is a high chance that you will get a call from companies.

These are some Kaggle Competitions, in which you should participate-

1. Titanic: Machine Learning from Disaster– Start with this competition. This Competition is good for the beginner.
2. Predict Future Sales– This challenge serves as a final project for the “How to win a data science competition” Coursera course.
3. House Prices: Advanced Regression Techniques– This is another beginner-friendly competition for those who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. 
4. Digit Recognizer– This competition is for those who have knowledge of R or Python and machine learning basic, but new to computer vision.

The list is long, for more Kaggle Competitions, you can check here.

Step 7- Build a Strong Resume

Finally, you are ready to apply for a Data Scientist Job. Now, it’s time to make a strong Resume. Your Resume is the first impression for any recruiters. No matter how skilled you are, but if your resume is not attractive, sorry you will not get an interview call. That’s why you shouldn’t ignore your Resume.

Nowadays, most companies are using Applicant Tracking System. So try to make a clean and simple resume so that computers can read it easily. Make two copies of your resume, one for ATS and one that looks great and you can give them to someone in person.

List all of your projects in your resume, better is to have URLs so that recruiters can click on them to view online. At least put your 3 best projects in your resume.

Read this article, if you want to create a strong resume for a Data Science job- Data Science Resume to Get Hired.

That’s all!. If you follow these steps and gain these required skills, then no one can stop you to land in Data Science Field.

Now it’s time to wrap up!

Conclusion

I hope you got an answer to the question in this article- How to Become a Data Scientist from Software Engineer?. I tried to provide all the necessary information you should know to switch from a software engineer to a data scientist. If you still have any doubt, feel free to ask me in the comment section. I am here to help you.

But if you found this article helpful, share with others.

All the Best for your Career!

Happy Learning!

Thank YOU!

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden