Data Science Program

Program Overview:

The comprehensive three-month data science and advanced analytics program at Indepth Research Institute (IRES) is designed to equip participants with the essential knowledge and skills required to excel in the dynamic field of data science. Through a carefully crafted curriculum spanning data curation, advanced programming, and sophisticated analytics techniques, participants will gain hands-on experience and practical insights into the latest technologies and industry best practices. From understanding the data lifecycle to mastering machine learning algorithms and deploying data-driven solutions in the cloud, this program offers a holistic learning experience tailored to meet the demands of today's data-driven industries.

Duration: 3 Months

Mode of Delivery: Virtual


Who Should Attend?

  • Aspiring data scientists seeking to build foundational knowledge and skills.
  • Data analysts looking to enhance their data manipulation and analysis capabilities.
  • Software developers interested in transitioning into data science roles.
  • Business professionals aiming to leverage data for informed decision-making.
  • Researchers and academics interested in applying data science techniques to their work.
  • Anyone interested in learning about the latest tools and techniques in data science and analytics.

Program Objectives:

  • Understand data curation's significance for data science, ensuring quality, reusability, and reproducibility.
  • Acquire techniques for collecting relevant data from various sources, including APIs and web scraping.
  • Master data cleaning and preprocessing using Python libraries like pandas and NumPy.
  • Gain proficiency in data integration, transformation, merging, consolidating, and filtering using SQL and/or Python.
  • Learn best practices for data preservation, storage, documentation, and sharing, adhering to FAIR data principles.
  • Enhance Python proficiency with advanced libraries like NumPy, pandas, matplotlib, and seaborn for efficient data manipulation and analysis.
  • Develop skills in object-oriented programming principles and design patterns for building robust data science applications.

Learning Outcomes:

By the end of the program, participants will be able to:

  • Acquire, clean, integrate, and preserve data effectively, adhering to best practices in data curation processes.
  • Demonstrate proficiency in advanced programming languages and tools such as Python, SQL, and Apache Spark for efficient data manipulation and analysis.
  • Utilize various data storage solutions, including relational and NoSQL databases, and cloud computing platforms for scalable data storage and processing.
  • Apply statistical analysis techniques and machine learning algorithms to extract meaningful insights and patterns from complex datasets.
  • Develop advanced data visualization techniques using libraries such as Plotly, Bokeh, and Tableau to communicate insights effectively to stakeholders.
  • Design and implement robust data pipelines and APIs for seamless data integration and communication between different systems in real-world scenarios.

Program Pre-requisites

Participants are expected to have:

  • Basic understanding of programming fundamentals, including variables, loops, conditionals, and functions.
  • Familiarity with at least one programming language such as Python, Java, or JavaScript.
  • Basic knowledge of data structures and algorithms.
  • Understanding of fundamental concepts in mathematics, including statistics and linear algebra.
  • Familiarity with command-line interfaces and text editors for coding purposes.
  • Eagerness to learn and engage actively in hands-on exercises and assignments throughout the program.


  • Excel
  • Python/R
  • Microsoft Azure
  • Hadoop Ecosystem /Apache Spark 
  • SQL
  • GitHub/Kaggle

Module 1: Data Curation

Data curation module will involve learning the systematic management and maintenance of data to ensure its accuracy, accessibility, and relevance over time. Key activities in this course module will include data collection, cleaning, documentation, organization, storage, and integration from multiple sources. The primary goals will be to enhance data quality, facilitate data reuse, and support compliance with legal and regulatory standards. Challenges in data curation include managing large volumes of diverse data, ensuring data quality, and protecting data privacy. Effective data curation relies on standardized practices, automation, stakeholder engagement, and regular updates to maintain data as a valuable resource for research and decision-making.

Module 2: Advanced Programming

Advanced programming module will involve learning the development and optimization of software to perform complex data analysis and machine learning tasks. Key activities in this course module will include mastering programming languages, implementing object-oriented programming principles, designing algorithms, and utilizing parallel and distributed computing techniques. The primary goals will be to enhance coding efficiency, facilitate scalable application development, and support robust data processing. Challenges in advanced programming include managing computational resources, ensuring code quality, and optimizing performance. Effective advanced programming relies on best practices, code review, stakeholder collaboration, and continuous learning to maintain software as a reliable tool for data science and advanced analytics.

Module 3: Advanced Analytics

Advanced analytics module will involve learning sophisticated techniques and methodologies to analyze complex data and extract actionable insights. Key activities in this course module will include statistical analysis, machine learning, natural language processing, and big data analytics. The primary goals will be to enhance data-driven decision-making, uncover hidden patterns, and predict future trends. Challenges in advanced analytics include handling large and diverse datasets, ensuring model accuracy, and interpreting results effectively. Effective advanced analytics relies on robust statistical methods, advanced algorithms, interdisciplinary collaboration, and continuous model evaluation to maintain analytics as a powerful tool for business intelligence and strategic planning.

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Program Experience

Program Delivery

Delivered via video lectures.

Real-World Examples

Delivered through a combination of video and live online lectures.

Applications to Data Sets

Learn through individual assignments and feedback.

Debrief of Learnings

Delivered through a combination of recorded and live video lectures.


Upon successful completion of the program, you will earn a certificate of completion from Indepth Research Institute.

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