Data Science for Healthcare

Learn the world's one of the most in-demand profession along with data science in a quick and effective manner. Expert healthcare data scientists are waiting for mentoring you along the way.

Data Science for Healthcare

Introduction to Data Analytics

Data analysis is a multi-step process including examining, cleaning, exploring, and transforming data to discover useful information, and support decision-making. This part will give you an introduction to some basic tools (Jupyter Notebook, Google Colab.) and libraries (Numpy and Pandas) that you will use throughout the bootcamp.

Introduction to Python

Python is the favorite programming language among data scientists, and it is not a coincidence. Large communities and libraries, ease of use, open source, flexibility, and integration with other programming languages are just some of the advantages of Python. In this part, you will start from the basics of Python such as data structures and you will be able to run your independent analysis using it.


Unlock the potential of Python as you explore various data types and gain hands-on experience through practical examples and exercises. Whether you're a beginner or looking to expand your coding skills, this content is your gateway to becoming a confident Python programmer.

Data Exploration

Discover why data exploration is the heartbeat of every successful data science project. It goes beyond mere data collection and dives deep into uncovering the secrets hidden beneath the surface. From identifying outliers and missing values to identifying trends and making data-driven decisions, this dynamic process holds the key to transforming raw data into actionable insights.


As we embark on a transformative data journey, where data exploration reigns supreme. Witness firsthand how it fuels innovation, drives business growth, and empowers decision-makers to make informed choices.

Introduction to SQL

The relational database is a part of the day-to-day work of a data scientist. SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. This part will help you to understand basic queries, aggregations, joins, subqueries, and case statements. To do that you will use PostgreSQL.

Introduction to Statistics

Statistics is an essential part of data science. A good data scientist should combine, along with other hard and soft skills, coding, and statistical knowledge to come up with a reliable model.


This course covers the essentials of statistics, including the foundations, key concepts, and practical applications. Delve into the world of distributions and probabilities, gaining insights into various probability distributions and their real-world significance. With hands-on exercises and examples, you'll develop the skills to analyze and interpret data with confidence.

Experimental Design

As a data scientist, you will have to design many experiments. So, you can think of this part as a warm-up for your real challenges. Having learned the main statistical test such as the t-test and A/B test, you will be ready to run your data analytics project.

First Capstone

Up to this point, you learn many things and now it is time to present your skills. In the first capstone, you will work on a project that will be assigned to you by Leveragai. This project will be mainly based on data exploration, and it also helps you to sharpen your Python, statistics, and analytical skills.

First Mock Interview

To be able to prepare you for the real data science interview, we created two mock interviews during this program. The mock interview is held by an industry expert with a 1-on-1 session. You will be asked several questions about the topics you have covered so far. This is the first one and you must take and pass it to move forward. This first mock interview is designed to compare the level of knowledge you have and the one you are supposed to have. You will be asked coding, statistics, and design questions.

Introduction to Machine Learning

Here comes the part that we have prepared so far. This is the very first course in machine learning. In this part, you will learn the basic but most important concepts of machine learning that you will refer to along the way. These concepts include but are not limited to introduction to machine learning and supervised/unsupervised learning, train-test split, cross-validation, overfitting, and bias-variance trade-off.

Supervised Learning Models

Regression models are statistical models that are used to predict a numerical value. This part of the curriculum will give an in-depth understanding of regression-based modeling. You will first learn the theory of these models and see how you can apply it using Python.  Having talked about over and underfitting. Regularization will be discussed via the ridge, lasso, and elastic net regression.

Under supervised learning title, you will also learn classification models that are  types of machine learning models used in data science to predict the categorical class or label of an input data point. Having categorical classes in the data is not something rare, so it is better to get used to applying classification models. 

There are several types of machine learning models that can be applicable to classification and regression and, after discussing linear regression, you will be familiar with logistic regression, decision trees, random forests, and support vector machines, and boosting algorithms.

Second Capstone

Having completed the regression and classification models, now you know how to run a machine learning model. Combining your data exploration, Python, and machine learning modeling knowledge, you are all set to run your first machine learning modeling project as a second capstone. Leveragai will provide the project and you will apply your skill to tackle it.

Unsupervised Learning

What happens if data does not include any labels? It means you should run an unsupervised learning model. An unsupervised learning algorithm can be used for clustering, anomaly detection, and dimension reduction. In this part, you will learn how unsupervised learning models work. As always, you will learn the theory of KMeans, DBSCAN, and hierarchical clustering methods and apply them.

Data Science for Healthcare - 1

This is where we start exploring  the fascinating world of data science in healthcare. We kick off by providing an insightful introduction to the design of health studies, including a deep dive into cohort studies. We emphasize the significance of declassification in ensuring patient privacy and confidentiality, while also introducing you to MRNs (Medical Record Numbers) and DeID (De-identification) techniques that play a pivotal role in protecting sensitive information.


Delving further, we explore the rich landscape of health data types and their distinctive features, encompassing the realm of genomics data. Our content sheds light on observational studies, elucidating their applications and methodologies for association analysis and causal inference. Additionally, we equip you with fundamental statistics knowledge tailored specifically for healthcare, enabling you to derive meaningful insights and make informed decisions.


Moreover, we unravel the intricacies of survival analysis, presenting both parametric and non-parametric models for examining time-to-event data in healthcare scenarios. By immersing yourself in this content, you will gain a holistic understanding of data science in healthcare, empowering you to extract actionable intelligence and contribute to the advancement of medical research and patient care.

Data Science for Healthcare - 2

In the second part of data science for healthcare, we begin with biostatistical inference, providing you with the tools to make confident decisions and draw meaningful conclusions from healthcare data.


Next, we delve into the fascinating world of Natural Language Processing (NLP) in healthcare, exploring its applications and techniques. Discover the power of Named Entity Recognition (NER) as we delve into its role in extracting critical information from healthcare texts. We also delve into text classification, uncovering its potential to automate tasks and enhance healthcare workflows. Additionally, we dive deep into the intriguing realm of embedding selection and discuss its significance in optimizing NLP models for healthcare applications.


Finally, we shine a spotlight on the transformative impact of data science in medical imaging. 

Data Science for Healthcare - 3

We embark with the dynamic field of drug discovery, equipping you with the knowledge to leverage cutting-edge technologies such as Keras and ChemMol models. 


In addition to drug discovery, we shed light on the vital aspect of disease prevention. Explore disease surveillance techniques that leverage data science to monitor and analyze patterns, enabling early detection and proactive interventions. Furthermore, we guide you through the creation of ODE.


Finally, we unravel the realm of clinical matching, where data science plays a pivotal role in optimizing patient outcomes. Discover how advanced algorithms and machine learning techniques can facilitate precise and efficient patient-clinical trial matching, enhancing the process of medical research and improving patient access to innovative treatments.

Final Capstone

An important part of a data scientist’s job is to bring research questions and in this third and final capstone, you will see how a real research question can be emerged and be tackled. So, you will first work on the research question that you want to address, check if data is available, and, upon approval of your mentor, you will start working on it.

Second Mock Interview

In this second mock interview, you will be tested on what you have learned so far with a 1-on-1 session. An industry expert mentor will ask you several questions and gauge your level of knowledge and provide you feedback so that you better understand if you are ready for the real data science interviews.


Congrats! Now, you will be paired with a company to accomplish a data science or analytics project. During this internship, you will equip yourself not only with theoretical skills but also with real-life challenges. This internship will take at least three months.


Once you have finalized all the steps given above, you deserve to graduate. A certification will be awarded to you by Leveragai.

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Data Science for Healthcare

Best Offer

$4900 $3900 / one time
  • Unlimited access to all materials
  • Move forward on your own schedule
  • Get your hand dirty with finance focused industry projects
  • Learn from industry experts with 1-on-1 sessions
  • Unleash the power of data science in healthcare
  • Get your certification

The Importance of Familiarity with Healthcare Domain in Data Science



Data scientists are highly skilled professionals who possess a diverse set of skills and knowledge in fields such as statistics, programming, and machine learning. Their expertise allows them to provide valuable services across various industries. However, when it comes to the healthcare field, data scientists often find themselves facing a significant challenge – a lack of domain knowledge. In this article, we will explore why having a deep understanding of the healthcare domain is crucial for data scientists, how they can acquire this knowledge, and the impact it has on their work.

The Need for Domain Knowledge

Project Ideation and Data Gathering

One of the primary challenges data scientists face in the healthcare field is coming up with project ideas without sufficient domain knowledge. Understanding the healthcare domain is essential for identifying areas where data science can be applied effectively. For example, if the goal is to build a model to predict gastrointestinal bleeds, data scientists need to know what variables are relevant to this outcome. Without domain knowledge, they may struggle to gather the right data and design an accurate predictive model.

Data Cleaning and Exploration

Domain knowledge is also crucial for data cleaning and exploration. Understanding how healthcare data is captured, whether through manual entry or machines, helps data scientists spot potential issues and ensure the quality of the data. Additionally, knowing what variables are likely to be related to a specific health outcome can guide data exploration and speed up the feature engineering process.

Feature Engineering and Model Building

Feature engineering, the process of creating meaningful features from raw data, is often the most challenging part of building a machine learning model. In healthcare, understanding clinical variables and their relationship to health outcomes is essential for effective feature engineering. Data scientists need to know which variables are significant and which time horizons are relevant. This understanding not only speeds up the feature engineering process but also improves the quality and interpretability of the model.

Quality Assurance and Interpretation of Results

A strong understanding of the healthcare domain is crucial for quality assurance and interpretation of analytical work. Data scientists need to be able to determine whether the results of their analyses and modeling work make sense in the context of healthcare. Knowing which results are important, which are trivial, and which are actionable helps in the presentation and communication of results to stakeholders.

Acquiring Domain Knowledge

Reading Literature and Attending Conferences

One way for data scientists to gain domain knowledge is through reading relevant literature and attending conferences. However, finding resources that are accessible to those without a clinical background can be challenging. Nevertheless, making an effort to seek out and read research papers, industry reports, and attending conferences can provide valuable insights into the healthcare domain.

Establishing Relationships with Clinicians

Building strong relationships with clinicians is another effective way for data scientists to gain domain knowledge. Clinicians can provide valuable guidance and insights into the healthcare field, helping data scientists understand the context of their data and the challenges they may encounter. By collaborating with clinicians, data scientists can gain firsthand experience by visiting clinics, observing procedures, and interacting with healthcare professionals. However, the easiest and efficient way is to learn more about use cases in healthcare.

Gaining Familiarity in Healthcare Use Cases in Data Science

Use cases play a pivotal role in the realm of healthcare within the realm of data science, serving as invaluable compasses that guide researchers, practitioners, and data analysts through the intricate landscape of medical data. The healthcare industry is awash with a deluge of diverse and complex data, ranging from electronic health records (EHRs) and medical images to genomics and patient-generated data from wearable devices. In this data-rich environment, use cases serve as potent tools, illuminating the path towards deriving meaningful insights and tangible benefits.

These use cases provide a structured framework for data scientists to harness the power of data analytics, machine learning, and artificial intelligence to unravel critical insights that can drive evidence-based decision-making, predictive modeling, disease diagnosis, treatment optimization, and patient outcomes improvement. Whether it’s predicting disease outbreaks, personalizing treatment plans, or optimizing hospital operations, use cases provide a concrete context to apply data science methodologies and algorithms effectively.

Furthermore, the healthcare domain is characterized by its inherent complexity, ethical considerations, and regulatory constraints. Use cases act as bridges, connecting data scientists with medical experts and stakeholders, ensuring that the solutions developed are not only technically sound but also ethically and legally compliant. Through these use cases, data scientists gain a deep understanding of the clinical processes, workflows, and challenges, enabling them to translate raw data into actionable insights that have a real-world impact on patient care and population health.

Importantly, use cases in healthcare data science facilitate interdisciplinary collaboration, fostering partnerships between data scientists, clinicians, researchers, and administrators. This collaboration enriches the analytical process by infusing domain expertise and clinical context into data-driven solutions. For instance, in radiology, data scientists collaborating with radiologists can develop algorithms that enhance medical imaging analysis, leading to more accurate diagnoses and reduced interpretation time.

Leveraging Healthcare-Based Data Science Bootcamps and Mentorship Programs

For data scientists seeking to enter the healthcare domain, leveraging healthcare-based data science bootcamps and mentorship programs can be immensely beneficial. These programs offer structured learning opportunities and mentorship from experienced professionals in the healthcare field. One such program is the Leveragai Healthcare Data Science Bootcamp, which provides a 1-on-1 mentored learning experience for data scientists looking to gain expertise in healthcare data science projects. You can visit to learn more.


In conclusion, familiarity with the healthcare domain is vital for data scientists working in the healthcare field. Understanding the intricacies of healthcare data, clinical variables, and health outcomes is essential for project ideation, data gathering, data cleaning, feature engineering, and result interpretation. Data scientists can acquire domain knowledge by reading literature, attending presentations, establishing relationships with clinicians, and leveraging healthcare-based data science bootcamps and mentorship programs. By gaining a deep understanding of the healthcare domain, data scientists can become self-sufficient in essential feature selection and design, ultimately contributing to improved healthcare outcomes through their data science projects.

Frequently Asked Questions (FAQ)

The Leveragai Data Science for Healthcare Bootcamp is an immersive and domain-specific program that focuses on equipping participants with the skills and knowledge required to excel in data science within the healthcare industry.

The bootcamp is a 5-month intensive program designed to provide a comprehensive understanding of data science concepts as applied to healthcare.


The bootcamp covers a wide range of domains within healthcare, including survival analysis, medical diagnostics, genomics, drug discovery, NLP in healthcare, and more.

This bootcamp is tailored specifically for the healthcare industry, combining data science expertise with healthcare domain knowledge. It offers a focused and practical curriculum to address real-world healthcare challenges.

There are no prerequisites for the Bootcamp. So, the Bootcamp is suitable for everyone interested in leveraging data science within the healthcare domain.

You can apply for the bootcamp by visiting this page on Leveragai’s website. Click on “ADD TO CART” and follow the steps.

Throughout the bootcamp, you will have access to experienced mentors on a weekly basis. Regular mentoring sessions and interactive discussions will enhance your learning experience. Also, Leveragai organize regular meetup and hosts industry professionals to discuss latest improvement in data science for healthcare.

After successful completion of the bootcamp, you will be well-equipped to pursue roles such as healthcare data analyst, medical researcher, or data scientist within healthcare organizations.

Yes, we offer a satisfaction guarantee. If you are not satisfied with the bootcamp within two weeks, you can request a refund. Please refer to our refund policy for more details.