Master AI and Data Science, Accelerate Your Career

This covers the prerequisites needed to begin the online Data Science and Business Analytics program and includes the basics of programming with Python.

Elevate your analytical skills with the Business Statistics module. Harness the power of Python to assess the reliability of business estimates through confidence intervals and hypothesis testing. Make informed decisions by analyzing data distributions, ensuring precision in resource allocation and strategic commitments.

• Inferential Statistics Foundations

Delve into the core of statistical analysis. Gain a comprehensive understanding of probability distributions, essential for making statistically-sound, data-driven decisions. Master the fundamentals to draw conclusions about populations based on samples.

• Estimation and Hypothesis Testing

Uncover the intricacies of estimation, determining population parameters from sample data, and master the art of hypothesis testing—a framework for drawing meaningful conclusions. Delve into essential concepts like the Central Limit Theorem and Estimation Theory, providing a solid foundation for robust statistical analysis in decision-making.

• Common Statistical Tests

Gain proficiency in hypothesis tests, essential for validating claims about population parameters in Data Science. This module introduces the most commonly used statistical tests, equipping you to choose the right test for business claims based on contextual nuances. Explore practical implementations in Python through real-world business examples, ensuring a comprehensive understanding of statistical testing in the Data Science realm.

Uncover the power of linear models in deciphering relationships between variables and continuous outcomes. Validate models, draw statistical inferences, and gain invaluable business insights into the key factors shaping decision-making.

• ​​​Intro to Supervised Learning - Linear Regression
  
  

Gain insights into Machine Learning, a subset of Artificial Intelligence, dedicated to pattern recognition and predictive analysis without explicit programming. This module specifically delves into the fundamentals of learning from data, the mechanics of the Linear Regression algorithm, and practical aspects of building and evaluating regression models using Python.

• Linear Regression Assumptions and Statistical Inference
  
  

Explore the critical facets of Linear Regression with our module on Assumptions and Statistical Inference. Gain insights into the essential assumptions that validate the model statistically. This module guides participants through understanding, checking, and ensuring the satisfaction of these assumptions. Learn how to address violations and draw meaningful statistical inferences from the model's output, ensuring a robust and reliable application of Linear Regression in data analysis.

Master classification models to discern relationships between variables and categorical outcomes, extracting vital business insights by identifying key decision-making factors.

• Logistic Regression
  
  This module covers the theoretical foundations of Logistic Regression, performance assessment, and the extraction of meaningful statistical inferences. Participants will grasp the intricacies of model interpretation, evaluate classification model performance, and discover the impact of threshold adjustments in Logistic Regression for enhanced predictive accuracy. Explore applications spanning medicine, finance, and manufacturing, ensuring a robust understanding and application of Logistic Regression in diverse fields.

• Decision Tree
  
  Explore the power of Decision Trees in our module, uncovering their role as supervised ML algorithms for hierarchical decision-making in both classification and regression scenarios. Delve into the intricacies of modeling complex, non-linear data with Decision Trees. This module elucidates the process of building a Decision Tree, introduces various pruning techniques to enhance performance, and provides insights into different impurity measures crucial for decision-making. Acquire a comprehensive understanding of the Decision Tree algorithm, empowering you to navigate its construction and optimization effectively.

In this course, you will learn how to combine the decisions from multiple models using ensemble techniques to improve model performance and make better predictions, and employ feature engineering techniques and hyperparameter tuning to arrive at generalized, robust models to optimize associated business costs

• Bagging and Random Forest

Random forest is a popular ensemble learning technique that comprises several decision trees, each using a subset of the data to understand patterns. The outputs of each tree are then aggregated to provide predictive performance. This module will explore how to train a random forest model to solve complex business problems.

(Introduction to Ensemble Techniques, Introduction to Bagging, Sampling with Replacement, Introduction to Random Forest)

• Boosting

Boosting models are robust ensemble models that comprise several sub-models, each of which is developed sequentially to improve upon the errors made by the previous one. This module will cover essential boosting algorithms like AdaBoost and XGBoost that are widely used in the industry for accurate and robust predictions.

(Introduction to Boosting, Boosting Algorithms (Adaboost, Gradient Boost, XGBoost), Stacking)

• Model Tuning

Model tuning is a crucial step in developing ML models and focuses on improving the performance of a model using different techniques like feature engineering, imbalance handling, regularization, and hyperparameter tuning to tweak the data and the model. This module covers the different techniques to tune the performance of an ML model to make it robust and generalized. (Feature Engineering, Cross-validation, Oversampling and Undersampling, Model Tuning and Performance, Hyperparameter Tuning, Grid Search, Random Search, Regularization)

In this course, you will learn to use clustering algorithms to group data points based on their similarity, find hidden patterns or intrinsic structures in the data, and understand the importance of and how to perform dimensionality reduction.

• K-means Clustering

K-means clustering is a popular unsupervised ML algorithm that is used for identifying patterns in unlabeled data and grouping it. This module dives into the workings of the algorithm and the important points to keep in mind when implementing it in practical scenarios.

(Introduction to Clustering, Types of Clustering, K-means Clustering, Importance of Scaling, Silhouette Score, Visual Analysis of Clustering)

• Hierarchical Clustering and PCA

Hierarchical clustering organizes data into a tree-like structure of nested clusters, while dimensionality reduction techniques are used to transform data into a lower-dimensional space while retaining the most important information in it. This module covers the business applications of hierarchical clustering and how to reduce the dimension of data using PCA to aid in the visualization and feature selection of multivariate datasets.

(Hierarchical Clustering, Cophenetic Correlation, Introduction to Dimensionality Reduction, Principal Component Analysis)

In this course, you will get an overview of Generative AI, understand the difference between generative and discriminative AI, design, implement, and evaluate tailored prompts for specific tasks to achieve desired outcomes, and integrate open-source models and prompt engineering to solve business problems using generative AI.

• Introduction to Generative AI

Generative AI is a subset of AI that leverages ML models to learn the underlying patterns and structures in large volumes of training data and use that understanding to create new data such as images, text, videos, and more. This module provides a comprehensive overview of what generative AI models are, how they evolved, and how to apply them effectively to various business challenges.

(Supervised vs Unsupervised Machine Learning,  Generative AI vs Discriminative AI, Brief timeline of Generative AI, Overview of Generative Models, Generative AI Business Applications)

• Introduction to Prompt Engineering

Prompt engineering refers to the process of designing and refining prompts, which are instructions provided to generative AI models, to guide the models in generating specific, accurate, and relevant outputs. This module provides an overview of prompts and covers common practices to effectively devise prompts to solve problems using generative AI models.

(Introduction to Prompts, The Need for Prompt Engineering, Different Types of Prompts (Conditional, Few-shot, Chain-of-thought, Returning Structured Output), Limitations of Prompt Engineering)

This course will help you gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights.

• Querying Data with SQL

SQL is a widely used querying language for efficiently managing and manipulating relational databases. This module provides an essential foundation for understanding and working with relational databases. Participants will explore the principles of database management and Structured Query Language (SQL), and learn how to fetch, filter, and aggregate data using SQL queries, enabling them to extract valuable insights from large datasets efficiently.

(Introduction to Databases and SQL, Fetching data, Filtering data, Aggregating data)

• Advanced Querying

SQL offers a wide range of numeric, string, and date functions, gaining proficiency in leveraging these functions to perform advanced calculations, string manipulations, and date operations. SQL joins are used to combine data from multiple tables effectively and window functions enable performing complex analytical tasks such as ranking, partitioning, and aggregating data within specified windows. This module provides a comprehensive exploration of the various functions and joins available within SQL for data manipulation and analysis, enabling them to summarize and analyze large datasets effectively.

(In-built functions (Numeric, Datetime, Strings), Joins, Window functions)

• Enhancing Query Proficiency

Subqueries allow one to nest queries within other queries, enabling more complex and flexible data manipulation. This module will equip participants with advanced techniques for filtering data based on conditional expressions or calculating derived values to extract and manipulate data dynamically.

(Subqueries, Order of query execution)

Gain an understanding of the evolution of Data Science over time, their application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data driven solution.

Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build Data Science applications

Read, explore and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards

Learn how to describe components of a time series data and analyze them using special techniques and methods for time series forecasting.

In this course, you will learn the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.

Understand the role of predictive modeling in influencing customer behavior and how businesses leverage analytics in marketing and retail applications to make data-driven decisions

Develop a deep appreciation of credit and market risk and understand how banks and other financial institutions use predictive analytics for modeling their risk

Understand and appreciate the most widely used tools of web analytics which form the basis for rational and sound online business decisions, and learn how to analyze social media data, including posts, content, and marketing campaigns, to create effective online marketing strategies.

Get exposed to the discipline of supply chain management and its stakeholders, understand the role of logistics in businesses and supply chains, and learn methods of forecasting prices, demand, and indexes

This course is designed to provide you with a comprehensive understanding of Deep Learning, specifically Artificial Neural Networks. These networks consist of multiple hierarchical levels and serve as fundamental building blocks for knowledge discovery, application, and prediction from data. Through this course, you will gain expertise in effectively applying Artificial Neural Networks to real-world scenarios.

• Pre-work for Deep Learning, Artificial Neurons, Tensorflow, and Keras
• Introduction to Artificial Neural Networks
• Building Blocks of Artificial Neural Networks.

Gain expertise in leveraging Convolutional Neural Networks (CNNs) to empower computer systems with visual perception and comprehension. This program equips you with the skills to effectively process and utilize image data for business applications.

• Pre-work for Computer Vision
• Introduction to CNN - Working with Images
• Transfer Learning.

This course will explore the fascinating application of Neural Networks in enabling computers to comprehend human language. Specifically, you will learn how to analyze text data and determine its underlying sentiment.

• Pre-work: Natural Language Processing
• Vectorization and Sentiment Analysis
• Sequential Natural Language Processing using Deep Learning.