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আপনার গ্লোবাল ক্যারিয়ার প্রসপেক্ট ও আয় বাড়িয়ে নিন মাত্র ১ সপ্তাহেগ্রামীণফোন একাডেমিতে কোডার্সট্রাস্টের প্রফেশনাল সার্টিফিকেট কোর্সগুলো করে, সম্পূর্ণ বিনামূল্যে।

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Post-Graduate Diploma in Data Analytics

Your Gateway to Achieving Greatness in Data Analytics Careers!

The Post-Graduate Diploma in Data Analytics equips students with practical skills to collect, process, analyze, and interpret data for informed decision-making. Covering data visualization, statistical analysis, spreadsheet tools, database management, and business intelligence, the program prepares learners for careers such as Data Analyst, Business Intelligence Analyst, and Reporting Analyst. With a focus on practical projects and industry-relevant tools, the program also supports opportunities in freelancing, consulting, and entrepreneurship.

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Course Price

BDT 42,000

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  • 40 Credit Hours
  • 12 Months
  • 10 Subjects
  • Instructor-Led
  • On-Campus

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  • Industry-Relevant Insights and Tools
  • Develop skills for real career growth
  • Learn from experts and industry practitioners
  • Develop skills for real career growth
  • Learn from experts active in their field
  • Learn by working on real-world problems
  • Structured guidance ensuring continuous learning
The Post-Graduate Diploma in Data Analytics introduces students to the essential concepts, techniques, and tools used to transform raw data into meaningful insights. The program covers data collection, data cleaning, spreadsheet-based analysis, database fundamentals, statistical methods, data visualization, business intelligence, and analytical reporting.

Data Analytics is the process of examining data to identify patterns, understand trends, solve problems, and support evidence-based decisions. Organizations across industries use data analytics to improve performance, understand customer behavior, optimize operations, and identify new business opportunities.

Through practical exercises, case studies, and project-based learning, students develop the ability to work with datasets, interpret analytical findings, and communicate insights effectively. The program aims to build technical, analytical, and problem-solving skills that can be applied in business, finance, marketing, development organizations, research, and other professional fields.

Benefits of Earning a Diploma in Data Analytics:

A diploma in Data Analytics helps learners develop the skills required to interpret data and turn information into actionable business insights. Students gain practical experience in organizing datasets, identifying trends, creating reports, and presenting findings to support decision-making.

The program helps learners strengthen their analytical thinking, problem-solving, and communication skills while developing familiarity with commonly used data analysis and visualization tools. These competencies can support career opportunities in business operations, finance, marketing, research, information technology, and other data-driven sectors.

The diploma can also help graduates and working professionals enhance their existing qualifications, pursue career advancement, and explore opportunities in consulting, freelancing, and independent analytical projects.

The program emphasizes practical learning and industry-relevant competencies to help students prepare for evolving workforce demands.

Course Objective

  • Understand the fundamental concepts and applications of Data Analytics
  • Collect, organize, clean, and prepare data for analysis
  • Apply spreadsheet functions and analytical techniques to interpret datasets
  • Understand basic statistical concepts and their applications in data analysis
  • Use data visualization techniques to communicate findings clearly
  • Develop dashboards, charts, and analytical reports to support decision-making
  • Understand database concepts and retrieve relevant information from datasets
  • Identify trends, patterns, and relationships in data
  • Apply analytical thinking to practical business and organizational problems
  • Present data-driven insights and recommendations to relevant stakeholders

What you'll learn

The syllabus is designed to provide skill-based education to students and aspiring professionals who want to develop practical analytical capabilities and prepare for careers in data-driven environments.

By completing the program, students will be able to:
  • Understand the data analytics lifecycle, from data collection to interpretation and reporting
  • Organize, clean, validate, and prepare datasets for analysis
  • Use spreadsheet tools and formulas to perform data calculations and analysis
  • Apply basic statistical methods to identify patterns and trends
  • Understand database fundamentals and data retrieval concepts
  • Create charts, dashboards, and visual reports to communicate analytical findings
  • Interpret data to support business planning and operational decisions
  • Identify relevant performance indicators and evaluate organizational performance
  • Communicate insights through clear reports, presentations, and visualizations
  • Apply analytical and problem-solving skills to practical projects and case studies
  • Build a foundation for further learning in business intelligence and advanced analytics
  • Develop professional, managerial, and communication skills for data-related roles

Learning Path

Semester 01 - 20 Credits
Credits : 4

Course Overview and Context:

This course will provide essential knowledge on the basic understanding of the emerging data analytics field. The student will learn the data exploration methods, dimensional analysis, and some extent of data visualization. The theoretical knowledge and practical lessons learned from this course will equip the students to absorb their skill sin their respective industries significantly.

Course Outcomes:

  • Introduction to Data Analytics: Data overview; State of the practice in Analytics; Key roles for the new Big data ecosystem
  • Data Analytics Lifecycle: Overview of data analytic lifecycle; Various phases of data analytics life cycle; Global innovation network and analysis (GINA)
  • Data Mining Process: Definition; Data extraction as a process; Data mining models- CRISP-DM, SEMMA; Steps in the data mining process; Data mining process in a data warehouse, Applications of data mining; Data mining challenges
  • Review of Basic Data Analytics Methods: Descriptive analytics, Diagnostic analytics, Predictive analytics; Prescriptive analytics
  • Business Intelligence Trends and Big Data Trends: What is big data and why it is important; 5V’s of big data; Goals of big data; Big data architecture, analytics & techniques; Future trends of big data
Credits : 4

Course Overview and Context:

This course introduces students to computing science and programming. It includes fundamental concepts and terminologies of computing science, program design, and fundamental building blocks for programming in a high-level language. At the end of this course, the successful students will be able to: explain the process of program design and development; design a GUI by using objects (forms and controls) and managing properties of controls; design a program to solve a well-defined problem; and implement a program from a design using structured programming.

Course Outcomes:

  • Programming in general: Program development cycle; Programming tools – flowcharts, pseudo code
  • Programming in C#, NET: Forms, controls, properties, events; Numbers, variables, constants; Strings; Input and output using text boxes; Built-in functions – numeric functions, strings functions
  • Programming in Python: Basics, Data types and Control structures, Modularization and Classes
  • Methods: Value returning and void; Scope of variables; Value and reference parameters
  • Decisions: Relational and logical operators; Switch statements
  • Structure: Repetitions, Arrays, Classes, and Fundamental algorithms in C# and Python
  • Files: Reading and writing text files in C# and Python
Credits : 4

Course Overview and Context:

This course will provide knowledge regarding the advanced tools and techniques that are used for various data types. The concentration will be given to the application of suggestive tools in sophisticated software packages rather than enormous theories crunching. Students will learn the possible solutions for the situations of the respective problem for which the data was generated.

Course Outcomes:

  • Overview of Data Analytic Tools
  • Descriptive Measures of Data (Basic statistical measures): Types and measurement scales of data; Measures of central tendency; Measures of dispersion
  • Inferential Decision from Data (basic estimation and test of hypothesis): Basics of inferential statistics; Power of test and p-value; Confidence intervals and tests of means and proportions
  • Correlation and Regression: Simple and multiple correlations; Simple and Multivariate linear regression; Logistic Regression; Additional Regression Models
  • Classification: Overview; Decision Trees; Naïve Bayes; Diagnostics of Classifiers
  • Time Series Analysis: Decomposition of time series data; Plotting time series data; Box-Jenkins Methodology; ARMA models; ARIMA models
  • Text analysis: Text Analysis Steps; Collecting Raw Text; Representing Text; Term Frequency-Inverse Document Frequency (TFIDF); Categorizing Documents by Topics; Determining Sentiments
Credits : 4

Course Overview and Context:

This course will provide the student with knowledge of database concepts. Emphasis will be placed on database design, development, and querying using DBS installed on both LAN and cloud environments. Concepts covered include ER modeling, normalization, and database design theory. Hands-on activities will allow students to create, maintain and query various databases. An introduction and overview of stored procedures, triggers, and data warehousing will also be covered.

Course Outcomes:

  • Overview: Purpose of Database Systems; Database Languages; Database Design; Database Engine; Database and Application Architecture; History of Database Systems
  • Relational Models: Structure of Relational Databases; Database Schema; Relational Query Languages; The Relational Algebra; Introduction to SQL; Intermediate SQL; Advanced SQL
  • Database Design: Database Design Using E-R Model; Relational Database Design
  • Application Design and Development: Complex Data Types; Application Development
  • Big Data Analytics: Big Data; Data Analytics
  • Storage Management and Indexing: Physical Storage Systems; Data Storage Structures; Indexing
  • Query Processing and Optimization: Query Processing; Query Optimization
  • Transaction Management: Transactions; Concurrency Control; Recovery Systems
  • Parallel and Distributed Databases: Database-System Architecture; Parallel and Distributed Storage; Parallel and Distributed Query Processing; Parallel and Distributed Transaction Processing
  • Advanced Topics: Advanced Indexing Techniques; Block chain Databases
Credits : 4

Course Overview and Context:

This course will provide the student with knowledge of various data collection and data analysis software packages and their uses from database concepts. Emphasis will be placed on hands-on activities that will allow students to create, maintain and explore different queries and questions from various datasets.

Course Outcomes:

  • Overview: Purpose of Database Systems; Database Languages; Database Design; Database Engine; Database and Application Architecture; History of Database Systems
  • Relational Models: Structure of Relational Databases; Database Schema; Relational Query Languages; The Relational Algebra; Introduction to SQL; Intermediate SQL; Advanced SQL
  • Database Design: Database Design Using E-R Model; Relational Database Design
  • Application Design and Development: Complex Data Types; Application Development
  • Big Data Analytics: Big Data; Data Analytics
  • Storage Management and Indexing: Physical Storage Systems; Data Storage Structures; Indexing
  • Query Processing and Optimization: Query Processing; Query Optimization
  • Transaction Management: Transactions; Concurrency Control; Recovery Systems
  • Parallel and Distributed Databases: Database-System Architecture; Parallel and Distributed Storage; Parallel and Distributed Query Processing; Parallel and Distributed Transaction Processing
  • Advanced Topics: Advanced Indexing Techniques; Block chain Databases
Semester 02 - 20 Credits
Credits : 4

Course Overview and Context:

This course introduces data literacy required as a key twenty-first-century skill. Students will learn the nature of data across different domains and the concepts and skills of data visualization by understanding, questioning, and problematizing how data are generated, analyzed, and used. Students will be able to apply its concepts and skills to visualize their own data, interpret the findings, and examine the impacts of the data-driven decision.

Course Outcomes:

  • Introduction: Introduction to data visualization; Brief history of data visualization
  • Principles: Data graphics; Static graphics; Data visualization through their graphic presentation; Graph-theoretic graphs, High-dimensional data visualization; Multivariate data glyphs: principles and practice; Linked data views; Visualizing trees and forests
  • Methodologies: Linked micro-map plots; Grand tours, projection pursuit guided tours, and manual control; Multidimensional scaling; Multivariate visualization by density estimation; Structured sets of graphs; Regression by parts: fitting visually interpretable models with GUIDE; Smoothing techniques for visualization; Visualizing cluster analysis and finite mixture models; Visualizing contingency table; Mosaic plots and their variants; Matrix visualization; Web-based statistical graphics using XML technologies
  • Application: Reconstruction, visualization, and analysis of medical images; Exploratory graphics of a financial dataset, Graphical data representation in bankruptcy analysis, Visualization tools for insurance risk process
Credits : 4

Course Overview and Context:

This course will provide an understanding of data governance policy and ethical considerations while working as a data analyst. The students will be able to critically interpret the governance and regulatory frameworks associated with the capture, processing, and stewardship of data. They will also gain skills to critically interpret the roles and responsibilities of data security, privacy, and data protection. Learners will also build their capacity to analyze and evaluate the intersection of data and ethics in socio-technical environments.

Course Outcomes:

  • Data Governance: Data quality and provenance; Data management-Roles and responsibilities. Management of data policies, processes, and procedures; Data integrity & security; Risk management; Models and tools for data governance
  • Privacy and Data Protection: The right to privacy – constitutional and statutory protections, privacy, and the European Convention on Human Rights and EU Charter of Fundamental Rights; Common law protection; Data Protection Regulation Scope, processing of personal data, legitimate bases; Principles of data protection; Sensitive data; Issues of consent; Rights, supervision and enforcement; Data Protection in practice includes international transfers, surveillance, cloud computing, and auditing; The current reform of the area
  • Ethical Issues Pertaining to Data: Ethics and Computing – examining moral problems when using the Internet – spam, censorship and free speech, the anonymity offered by the Internet; Ethical issues that arising from the increasing use and pervasiveness of information technology and socio-technical systems; Health technology; Pervasive monitoring and tracking; Image, video and sound capture; Identity; Perpetuity of data storage; Trans nationality; Copyright; IOT
Credits : 4

Course Overview and Context:

In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, explore data using statistics and visualizations, transform data for further modeling, model data using machine learning algorithms to predict data patterns, and evaluate these model-based predictions.

Course Outcomes:

  • Supervised learning Vs. Unsupervised learning
  • Maximum likelihood
  • Naive Bayes
  • Logistic regression
  • Gradient descent
  • Regularization
  • Information theory
  • Decision trees
  • Representation learning / PCA
  • Clustering
  • Latent variable models
  • Neural networks
Credits : 4

Course Overview and Context:

This course will connect students’ education in the program and their work experiences to their post-graduation goals, whether that is obtaining a new job, earning a promotion, and/or freelancing. The goal is for students to focus on professional development topics so they feel confident in marketing their skills after graduation and have concrete materials that will make them stand out to employers and/or clients.

Course Outcomes:

  • Career Development Theories; Conducting Career Research.
  • Networking; Networking Tools; E-Portfolios.
  • Conducting a Targeted Job Search; Resumes and Cover Letters.
  • Interviewing; Effective job presentation.
  • Decision Making; Best Practices in the Workplace.
  • Setting Business Goals; Working with Clients.
  • Client Documents and Forms – Proposals, Bids, Contracts, Policies, and Procedures; Creating Forms (ex. Needs Assessments).
  • Project Organization; Requests for Proposals.
Credits : 4

Students will get an opportunity to conduct innovative research for Data Analytics in an industry of their choice. This course will provide students with the required breadth to jumpstart their careers in the Data Analytics field. During the first quarter of the Second semester, students will be asked to select a specialized topic according to his/her interest and submit a thesis/experimental project report during the semester final examination. The work will be supervised by an internal supervisor and the submitted thesis/report will be evaluated by an expert in a similar research field.

Career Opportunity

  • Data Analyst
  • Junior Data Analyst
  • Business Intelligence Analyst
  • Reporting Analyst
  • Business Analyst
  • Research Data Assistant
  • Marketing Data Analyst
  • Operations Analyst
  • Financial Data Analyst
  • MIS Executive
  • Data Visualization Analyst
  • Database Reporting Assistant
  • Performance Analyst
  • Freelance Data Analyst
  • Data Analytics Consultant
  • Entrepreneur
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Frequently Asked Questions

Data Analytics is the process of examining and interpreting data to identify patterns, measure performance, and support informed decisions. It helps organizations understand customers, improve efficiency, identify opportunities, and solve business problems.
The program emphasizes practical data handling, spreadsheet analysis, statistical concepts, visualization, reporting, and project-based learning. These skills help students understand datasets and communicate useful findings in professional settings.
Graduates may pursue opportunities in data analysis, business intelligence, reporting, research support, marketing analytics, operations, and management information systems, depending on their skills and experience.
Project work allows students to apply analytical concepts to practical problems, organize and interpret data, develop reports, and present findings. An internship, where available, can provide additional exposure to professional working environments.
Data Analytics can be relevant to graduates from business, social sciences, economics, science, information technology, and other disciplines. However, applicants must meet the official academic eligibility criteria specified by National University and CodersTrust College for the program.

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