PGP in Big Data & AI for Business & Management
Empower your analytical skills and make strategies with FORE and grow your analysis in Data Machine Learning.
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Program Overview
Data Proficiency
Graduates will show that they are capable of gathering, processing, and evaluating sizable datasets using the right tools and methods.
Application of Analytics
Graduates will be able to use analytical and statistical techniques to glean valuable insights from large amounts of data to assist managers and corporate leaders in making strategic decisions.
Technology Utilization
Graduates will be able to manage, visualize, and analyze big data with ease using advanced data analytics tools and technologies like Python, R, SQL, Hadoop, and Spark.
Problem Solving
Graduates will be able to solve problems by recognizing business obstacles, creating hypotheses based on data, and using analytical methods to come up with workable solutions.
Data Visualization
Graduates will be skilled at applying the right visualization tools and techniques to interpret and communicate insights to stakeholders in complicated data sets.
Ethical Data Handling
Graduates will exhibit knowledge of big data analytics for business and management, including ethical issues on data security, privacy, and integrity.
Alumni Profiles & their Projects
Sheik M Imran
Bhaveshkumar Thaker
Tapas Mohanty
Devendra Yadav
Ananda Deshmukh
Amukhta Gouru
Course Curriculum
- Python: Data structures in Python, Pandas, and Numpy.
- Data exploration, data summarization and transformation using pandas and numpy.
- Data Visualization: Data Visualization using Matplotlib, Seaborn and Plotly express. Developing relationships between mix of categorical and numerical features and plotting distributions
- Data Mining: Measures of Proximity; Cluster Analysis; Evaluation of Clusters: Cluster validation and Clustering Tendency; Curse of Dimensionality
- Techniques of Dimensionality Reduction: PCA, Random Projections and SVD (Singular Value Decomposition)
- Classification Analysis: Decision tree Induction & Regression Trees
- Random Forest algorithm
- Gradient Boosting Technique for Machine Learning
- Light GBM: Light Gradient Boosting Machine
- Extreme Gradient Boosting (XGBoost)
- Evaluating Classification: ROC, AUC, Precision, Recall, Specificity, Sensitivity; kappa metric; Overfitting; Bias-variance trade-off; L1 & L2 regularization
- Neural Networks
- Interpreting Machine Learning Models using Partial Dependence Plots and LIME
- Linux and Hadoop shell commands
- Introduction to Hadoop and its ecosystem
Hadoop file storage formats - Hadoop streaming
- Spark: Machine Learning, Structured Streaming, Deep Learning, Building data pipelines with Hadoop, kafka and NoSQL databases, Spark Delta Lake, and Using Spark NLP
- Apache Kafka: Building Data pipelines; transforming streaming data; Simple experiments with Apache Flink—Streaming analytics
- Introduction to NoSQL Databases and CAP theorem; Comparison with RDBMS
- Redis in-memory data structure store
- MongoDB Document Database
- Hbase column family database on hadoop
- TIG Stack: telegraf, InfluxDB and Grafana for collecting, storing and visualizing Time Series or IOT Data/metrics on a Dashboard
- Gephi Open Graph Visualization Platform
- Neo4j Graph Database
- Neural Networks
- Autoencoders and anomaly detection
- Deep Learning with Convolution Neural Network
- Using very Deep Convolution networks and Data Augmentation
- Transfer Learning-I
- Transfer Learning-II
- Natural Language Processing-I
- Natural Language Processing-II
- Recurrent Neural Networks
- General Architecture of Transformers
- Zero-shot classification and Few-shot learning
- Streamlit for developing LLM webApps
- Ollama and anythingLLM installation
- Embedding, vector databases and similarity search
- Prompt Engineering
- Developing LLM applications using langchain
- Biased LLMs and Ethics
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Program Director
Prof. Ashok Harnal
Professor Ashok Harnal, with 31 years of work experience, holds a B.Tech from IIT Delhi and an M.Phil from Punjab University, Chandigarh. He has been teaching and experimenting with Big Data technology since around the last twelve years. During his stay in the Min of Defence, he has led country-wide projects like Raksha Bhoomi for land records and establishing Disaster Management organizations at Delhi and Pune. He has published two books (both by Tata McGrawHill ): One on How to program games on computers and the IInd on Linux Administration and Applications.
Faculty & Mentors
Prof. Amarnath Mitra
Associate Professor
Prof Shilpi Jain
Professor of Business Administration in the Information Technology & Big Data Analytics
Admission Process
01. Complete the inquiry form
Once you submit the Query Form, a counsellor will contact you to discuss your eligibility.
02. Get Called and Put on a Shortlist
Our admissions committee will examine your profile. You will receive an email verifying your program admission as soon as you meet the requirements.
03. Reserve a seat and start the preparatory session
To join the program, pay for your seat in advance. Start your Big Data & AI For Business & Management journey with your Prep course!
Program Fee
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Frequently Ask Questions
Large and complicated datasets that are difficult for conventional data processing software to manage are referred to as "big data." Big Data is used in management and business to improve operations, obtain a competitive edge, make data-driven decisions, and extract insightful information.
Data sources, data storage systems, data processing frameworks, analytics tools, and visualization platforms are usually part of a big data ecosystem. Hadoop, Spark, NoSQL databases, and data warehouses are examples of common components.
Analyzing massive datasets for patterns, trends, and correlations is known as data analytics. Data analysis helps firms find opportunities, reduce risks, and make well-informed decisions.
Data quality problems, privacy and security worries, a shortage of qualified staff, complicated integration, scalability problems, and regulatory compliance are a few potential obstacles. It will need careful planning, talent and technology investments, and a data-driven culture to overcome these obstacles.
Data quality problems, privacy and security worries, a shortage of qualified staff, complicated integration, scalability problems, and regulatory compliance are a few potential obstacles. It will need careful planning, talent and technology investments, and a data-driven culture to overcome these obstacles.
Customer targeting and segmentation, supply chain optimization, fraud detection, sentiment analysis, targeted marketing, risk management, and increases in operational efficiency are a few examples.
Expertise in programming languages (such as Python, R, and SQL), statistical analysis, machine learning, data visualization, and business specific knowledge are essential.