Learn AI and Data Science with a Low-Code Approach

The first module in the program for applied Data Science begins with the foundations, which covers Python and Statistics foundations.

• Python Foundations - Libraries: Pandas, NumPy, Arrays and Matrix handling, Visualization, Exploratory Data Analysis (EDA)
  Pandas is a commonly used library in Python, which is used to analyse and manipulate data.
  NumPy is a package in the Python library, where you can use this package for scientific computing to work with arrays.
  An array is a data structure that stores various elements or items at contiguous memory locations.
  A matrix is a two dimensional (2D) array where data (elements/items) is stored in the format of rows and columns.
  Visualization is the process to represent data and information in a graphical form.
  Exploratory Data Analysis (EDA) enables you to uncover patterns and insights frequently with visual methods within some data.
• Statistics Foundations: Basic/Descriptive Statistics, Distributions (Binomial, Poisson, etc.), Bayes, Inferential Statistics
  Descriptive Statistics is a method that helps you study data analysis using multiple data sets by describing and summarizing them. For example, the data set can either be a collection of the population in a neighbourhood or the marks a sample of 100 students achieved.
  A Distribution is a statistical function used to report all the probable values that a random variable takes within a certain range.
  Bayes Theorem is a mathematical formula that is named after Thomas Bayes. This theorem helps you determine conditional probability.
  Inferential Statistics is a method that lets you explore basic concepts on using data for estimation and assess theories with the help of Python.

The next module in this applied Data Science course will teach you all the essentials about data analysis and visualization.

• Exploratory Data Analysis, Visualization (PCA and t-SNE) for visualization and batch correction
  This chapter will lecture you on all the essential topics about EDA and visualization.
• Introduction to Unsupervised Learning: Clustering includes- Hierarchical, K-Means, DBSCAN, Gaussian Mixture
  Unsupervised learning is a technique that helps you analyze and cluster unlabelled data sets. Clustering is a technique that clusters or groups data.
  In this chapter, you will learn more about unsupervised learning and clustering techniques, like Hierarchical, K-Means, DBSCAN, and Gaussian Mixture.
• Networks: Examples (data as network versus network to represent dependence among variables), determine important nodes and edges in a network, clustering in a network
  In this chapter, you will learn about networks and various examples of a network, like data as a network versus network to represent dependence among variables, determine important nodes and edges in a network, and clustering in a network.

The next module in this MIT Professional Education Applied Data Science Program will teach you about Machine Learning, which covers supervised learning and model evaluation. Machine Learning is an application of Artificial Intelligence, which studies computer algorithms and improves automatically through experience and data usage.

• Introduction to Supervised Learning - Regression
  Supervised learning is a technique that helps you analyze and cluster labelled data sets. Regression is a statistical technique in machine learning that manages the relationship between dependent and independent variables with the help of one or more independent variables.
• Introduction to Supervised Learning - Classification​​​​​​​
  Classification, as the name implies, is a procedure to classify/categorize a data set into various categories. This can be performed on both structured and unstructured data.
• Model Evaluation - Cross Validation and Bootstrapping
  Model Evaluation is a technique used for machine learning models, which estimates the accuracy of these models on future data. This chapter will prepare you for evaluating machine learning models using model evaluation techniques, like Cross Validation and Bootstrapping.

The next module in the program for applied Data Science teaches you about decision trees, random forests, and time series analysis.

• Decision Trees
  A Decision Tree is a popular supervised machine learning algorithm, which is used for both classification and regression problems. It is a hierarchical structure in which the internal nodes denote the dataset features, branches indicate the decision rules, and each leaf node represents the result.
• Random Forest
  Random Forest is another popular supervised machine learning algorithm. As the name implies, it consists of multiple decision trees on the various subsets of a given dataset. Then, it calculates the average for strengthening the predictive accuracy of a dataset.
• Time Series (Introduction)
  Time-Series Analysis consists of methods to analyze data on time-series, which later extracts meaningful statistics and other information. Time-Series forecasting is a method to predict future values by taking the help of previously observed values.

The next module in this applied Data Science course is Deep Learning. Deep Learning is an application of Machine Learning and Artificial Intelligence.

• Intro to Neural Networks
  Neural networks are inspired by the human brain, which is used to extract deep/high-level information from the raw input, like images, objects, etc. This chapter introduces you to artificial neural networks in deep learning.
• Convolutional Neural Networks
  Convolutional Neural Networks (CNN) are used for image processing, segmentation, classification, and several other applications. This chapter helps you learn all the essential concepts about CNN.
• Transformers
  Transformers are a recent, very successful neural network architecture that applies to language, graphs, and images. You will learn the basics of this architecture and see how it can be applied to different types of data.

The next module in this MIT Professional Education Applied Data Science Program will teach you about implementing recommendation systems.

• Intro to Recommendation Systems
  As the name implies, recommendation systems help you predict the future preference of some products, which later recommend you the best-suited items to customers.
  This chapter will teach you how to use a recommendation system so that you can choose the best products for customers.
• Matrix
  In this chapter, you will learn about the matrix used in recommendation systems.
• Tensor, NN for Recommendation Systems
  In this chapter, you will learn how to implement Tensor and NN for recommendation systems.​​​​​​​

The last module is capstone project, you will implement a hands-on capstone projects to master Data Science.

• Week 10: Milestone 1
  In week 10, you will implement the foundations of your capstone project related to data science.
• Week 11: Final Submission
  In week 11, you will work toward submitting the capstone project related to data science
• Week 12: Synthesis + Presentation
  In week 12, you will be reviewed on the projects implemented with synthesis and presentation.

The module covers :

• Overview of ChatGPT and OpenAI
• Timeline of NLP and Generative AI
• Frameworks for understanding ChatGPT and Generative AI
• Implications for work, business and education
• Output modalities and limitations
• Business roles to leverage ChatGPT
• Prompt engineering for fine-tuning outputs
• Practical demonstration and bonus section on RLHF

The module covers :

• Mathematical Fundamentals for Generative AI
• VAEs: First Generative Neural Networks
• GANs: Photorealistic Image Generation
• Conditional GANs and Stable Diffusion: Control & Improvement in Image Generation
• Transformer Models: Generative AI for Natural Language
• ChatGPT: Conversational Generative AI
• Hands-on ChatGPT Prototype Creation
• Next Steps for Further Learning and understanding