Post-Graduate Program in Data Analytics using AI
This course is designed to prepare young graduates for the role of Data Scientists, who can leverage the power of Artificial Intelligence (AI) to analyze and derive insights from large and complex data sets. The course covers the theoretical and practical aspects of Data Science, including the data lifecycle, data collection, data preprocessing, data exploration, data visualization, data modeling, data evaluation, data communication, data ethics, and data governance.
The course is delivered by certified global experts in Data Science and AI, who have rich experience and expertise in the field. The course adopts a blended learning approach, which combines online lectures, interactive sessions, case studies, assignments, projects, quizzes, and exams. The course also provides hands-on experience with various Data Science and AI tools and platforms through lab sessions and simulations.
The course duration is six months, followed by a brief industry internship that will help the students to apply their learning to real-world problems and gain exposure to the industry and commerce sectors. The course also prepares the students for various global certifications in Data Science and AI that will enhance their employability and career prospects.
By the end of this course, the students will be able to:
- Understand the fundamental concepts and principles of Data Science and AI and their importance in the digital era.
- Identify and collect relevant and reliable data from various sources and formats using appropriate methods and tools.
- Preprocess and clean the data to remove noise, outliers, missing values, duplicates, and inconsistencies using various techniques and tools.
- Explore and visualize the data to discover patterns, trends, outliers, correlations, and distributions using various techniques and tools.
- Model and analyze the data using various AI techniques, such as machine learning, deep learning, natural language processing, computer vision, etc., using various tools and platforms.
- Evaluate and validate the data models using various metrics and methods to measure their performance, accuracy, robustness, scalability, etc.
- Communicate and present the data insights using various formats and channels to different audiences and stakeholders using effective storytelling skills.
- Apply ethical principles and frameworks to ensure the responsible and fair use of data and AI in various domains and contexts.
- Implement data governance policies and practices to ensure the security, privacy, quality, integrity, and compliance of data and AI systems.
Upon successful completion of this course the students will be able to:
The course pedagogy consists of:
Online lectures: The lectures are delivered online by the certified global experts in Data Science and AI. The lectures cover the theoretical concepts and principles of Data Science and AI and provide examples and illustrations from various domains and scenarios. The lectures are recorded and made available for later viewing and revision by the students.
Interactive sessions: The interactive sessions are conducted online by the instructors
and facilitators. The interactive sessions provide an opportunity for the students to ask questions, clarify doubts, discuss topics, share experiences, and exchange feedback with the instructors and peers. The interactive sessions also include guest lectures by industry practitioners and experts who share their insights and perspectives on Data Science and AI issues and trends.
Case studies: The case studies are based on real-world problems and situations related to Data Science and AI. The case studies help the students to apply their learning to practical contexts and analyze the challenges and solutions involved in Data Science and AI. The case studies also enhance the students’ critical thinking, problem-solving, decision-making, and communication skills.
Assignments: The assignments are given to assess the students’ understanding and application of the concepts and techniques learned in the course. The assignments require the students to perform various tasks and activities related to Data Science and AI, such as data collection, data reprocessing, data exploration, data visualization, data modeling, data evaluation, data communication, data ethics, data governance, etc. The assignments are graded and feedback is provided by the instructors.
Projects: The projects are designed to provide hands-on experience and exposure to the students in Data Science and AI. The projects require the students to work in groups and develop a Data Science or AI solution for a given problem or scenario. The projects involve various stages, such as problem identification,
requirement analysis, design, implementation, testing, evaluation, and presentation. The projects are evaluated and feedback is provided by the instructors and peers.
Quizzes: The quizzes are conducted online to test the students’ knowledge and comprehension of the topics covered in the course. The quizzes consist of multiple-choice questions, short-answer questions, and fill-in-the-blanks questions. The quizzes are timed and scored automatically by the system.
Exams: The exams are conducted online to measure the students’ achievement of the course objectives and outcomes. The exams consist of descriptive questions, case-based questions, and application-based questions. The exams are proctored and graded by the instructors.
who can apply
why join us?
Running successfully since 2010
Panel of expert trainers
Trained more than 12k students
Conducted more than 300 workshops India & Globally
DTA is also into Digital Marketing Workshops & Consulting
Located in multiple regions of Mumbai including Thane, Dadar, Vashi, Andheri, & Fort
Institutes which enhances your Mindset, Skillset & Toolset in turn trainee excels
Very keen focus on excelling students’ practical skills rather than theory indeed which is required.
The course framework consists of the following modules:
Introduction to Data Science and AI
- Overview of Data Science and AI
- Essential Components of Data Science and AI
- Data Science and AI Domains and Applications
- Data Science and AI Challenges and Opportunities
Data Collection and Storage
- Types and Sources of Data
- Methods and Tools for Data Collection
- Formats and Structures of Data
- Methods and Tools for Data Storage
Data Preprocessing and Cleaning
Need and Importance of Data Preprocessing and Cleaning
Techniques and Tools for Data Preprocessing and Cleaning
Handling Noise, Outliers, Missing Values, Duplicates, and Inconsistencies in Data
Quality and Usability of Data after Preprocessing and Cleaning
Data Exploration and Visualization
- Need and Importance of Data Exploration and Visualization
- Techniques and Tools for Data Exploration and Visualization
- Discovering Patterns, Trends, Outliers, Correlations, and Distributions in Data
- Communicating Information and Insights from Data using Visualization
Data Modeling and Analysis using AI
- Need and Importance of Data Modeling and Analysis using AI
- Techniques and Tools for Data Modeling and Analysis using AI
- Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, etc. for Data Modeling and Analysis
- Solving Complex Problems and Generating Predictions using AI Models
Data Evaluation and Validation
- Need and Importance of Data Evaluation and Validation
- Metrics and Methods for Data Evaluation and Validation
- Performance, Accuracy, Robustness, Scalability, etc. of AI Models
- Improving and Optimizing AI Models using Evaluation and Validation Results
Data Communication and Presentation
- Need and Importance of Data Communication and Presentation
- Formats and Channels for Data Communication and Presentation
- Audiences and Stakeholders for Data Communication and Presentation
- Storytelling Skills for Effective Data Communication and Presentation
Ethical Issues in Data Science and AI
- Need and Importance of Ethical Issues in Data Science and AI
- Principles and Frameworks for Ethical Issues in Data Science and AI³
- Responsible and Fair Use of Data and AI in Various Domains²
- Ethical Issues in Big Data, Machine Learning Algorithms, Privacy, etc.¹
Data Governance in Data Science and AI
- Need and Importance of Data Governance in