Certification in Post Graduate Programme in Data Science Online Live

In today’s data-driven landscape, businesses face the challenge of sifting through vast amounts of data to make timely, informed decisions. Traditional methods often fail to provide the necessary insights, leading to missed opportunities and inefficiencies. Data science addresses these issues by turning complex data into actionable intelligence, empowering organisations to make strategic decisions, optimise operations and drive innovation.

LearninGT by Grant Thornton Bharat is pleased to offer a Post Graduate Programme in Data Science. The programme equips individuals with technical knowledge and skills in data management, including generative AI, cloud computing, big data analytics, and Microsoft Power BI, to uncover insights and manage data in the most effective way.

Who should attend

Programme deliverables

• Excel – Basic and advanced
  • Functions and formulas
  • Data formatting and validation
  • PivotTables and PivotCharts
  • Data analysis with Excel
  • Excel macros and automation
  • Data tables and what-if analysis
  • Advanced data visualisation
• SQL – Basic and advanced
  • Introduction to SQL
  • SQL basics
  • Advanced SQL queries
  • Data manipulation in SQL
  • Working with multiple tables and views
  • Data modelling and normalisation
  • Stored procedures and functions
  • Data import, export and working with database
  • SQL for data cleaning and analysis
  • SQL best practices
  • SQL security
  • Stored procedures and triggers
  • Case Study: Retail database management
• Data analytics using R
  • Introduction to data analytics, R and R programming
  • Data visualisation: Graphs and charts
  • Descriptive statistics
  • Linear and logistic regression
  • Multinomial linear regression
  • Multinomial logistic regression
  • Introduction to AI, ML and deep learning
  • Neural networks
  • Support vector machines (SVM)
  • Decision tree and random forest
  • K-nearest neighbours
  • Clustering and principal component analysis (PCA)
• Data visualisation with Power BI or Tableau
  • Introduction to Power BI and Tableau
  • Principles of effective data visualisation
  • Interactive and dynamic visualisations
  • Storytelling with data
  • Case Study: Sales performance of a company
• Programming and statistics using Python
  • Introduction to Python
  • Data types and variables
  • Control flow and loops
  • Functions and modules
  • File handling and exception handling
  • Object-oriented programming
  • NumPy and Pandas for data manipulation
  • Data visualisation with Matplotlib and Seaborn
  • Descriptive statistics: Measures of central tendency (mean, median, mode), measures of dispersion (variance and standard deviation)
  • Probability distributions: Common distributions (normal, binomial and Poisson), probability calculations
  • Inferential statistics: Sampling, confidence intervals, hypothesis testing (t-tests and z-tests) and p-values
  • Introduction to Bayesian statistics: Basic concepts and examples
  • Case study: Exploratory data analysis with Python
  • Case study: Analysing Black Friday datasets to understand consumer behaviour
  • Case study: Bank marketing data analysis to understand the effectiveness of campaigns
• Machine learning (ML)
  • Overview and types of ML-supervised and unsupervised learning
  • Linear regression
  • Logistic regression
  • Decision trees and random forest algorithms
  • Ensemble methods
  • SVM
  • Naïve Bayes
  • Feature, importance and selection
  • Model evaluation and selection
  • A/B testing and experimentation
  • Case study: Fraud detection in credit card transactions
  • Case study: Predicting housing prices
• Advanced ML and data mining
  • Time series analysis
  • Association rule mining
  • Clustering techniques
  • Dimensionality reduction
  • Anomaly detection
  • Boosting and PCA
  • Case study: Market basket analysis in retail
  • Case study: Time series forecasting for stock prices
• Artificial intelligence (AI)
  • Introduction to AI and its branches
  • Natural language processing (NLP)-text analytics
  • Deep learning with TensorFlow and Keras
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Case study: Image recognition using CNN
• Generative AI
  • Overview of generative AI
  • Introduction to generative models, generative adversarial networks (GANs) and variational autoencoders (VAEs)
  • Advanced topics in generative AI
  • Case study: Image generation using GANs
  • Case study: Anomaly detection in time series data using VAEs
  • Case study: Text generation with GPT-3/4
  • Case study: Generative models in healthcare
• Big data analytics on cloud
  • Big data characteristics and challenges
  • Importance of cloud computing for scalable data analytics
  • Overview of the Hadoop ecosystem and MapReduce basics
  • Deploying Hadoop on cloud platforms (e.g., AWS EMR and Azure HDInsight)
  • Introduction to Apache Spark and its advantages
  • Working with Spark DataFrames and resilient distributed datasets (RDDs)
  • Real-time data processing using Spark streaming
  • Implementing ML algorithms using Spark MLlib
  • Introduction to NoSQL databases and their role in big data applications
  • Case study: Analysing large-scale social media data
  • Case study: Real-time fraud detection with big data

Programme fees: INR 1,25,000 plus 18% GST

Schedule