Data Science and AI Programme

• Introduction to Python

• Running Jupyter Notebooks

• How to Use a Jupyter Notebook

• Basic Data Types

• Comparison and Logical Operators

• Lists and Indexing

• Advanced Indexing

• Updating Data in a List

• Introduction to Tuples

• Introduction to Dictionaries in Python

• Functions and Arguments

• Methods

• Writing User-Defined Functions

• Conditionals: If Statements

• Conditionals: While Loops

• For Loops

• Looping through a Dictionary

• Packages

• NumPy Arrays

• 2D NumPy Arrays

• Looping over NumPy Arrays

• Pandas Creating Data Frames

• Pandas Slicing and Filtering Data Frames

• NumPy and Pandas Statistical Tools

• Functions

• Global Scope vs Local Scope

• Nested Functions

• Default and Flexible Arguments

• Handling Errors and Exceptions

• Writing Lambda Functions

• Importing and Exporting Data

• Series

• Data Frames

• Common Functionality

• Indexing and Selecting Data

• Editing Data Frames: Setting Columns

• Editing Data Frames: Transforming Columns

• Editing Data Frames: Setting Data with loc

• Combining Data Frames

• Reshaping Data Frames

• Grouping and Aggregating Data

• Introduction to Matplotlib

• Simple Line Plots

• Bar Plots

• Scatter Plots

• Histograms

• Customising Graphs

• Line of Best Fit

• Box Plots

• Pair Plots

• Time Series Plots

• Introduction to 3D Plotting

• Exporting Figures

• Probability and Statistics

• Probability vs Statistics in Python

• Sampling Using Python

• Random Variables and Probability Distribution Functions

• Random Variables and Probability Distribution Functions in Python

• Probability Mass and Probability Density Functions

• Uniform Distribution

• Bernoulli and Binomial Distributions

• Normal Distribution

• Exponential, Poisson and T Distributions

• Confidence Intervals

• Hypothesis Testing

• Hypothesis Testing: Confidence Intervals for Difference in Means

• The Data Cleaning Process

• Inspecting the data

• Strategies for data cleaning

• Dealing with missing or duplicate data

• Introduction to Exploratory Data Analysis

• Descriptives, Frequencies and Averages

• Correlation

• Visualising and Plotting Data in Exploratory Analysis

• Data Preprocessing

• Introduction to Linear Algebra

• Matrices and Vectors

• Matrix Addition and Subtraction

• Dot Product and Cross Product

• Matrix Multiplication and Division

• Matrix Transposition

• Matrix Determinant and Inverse

• Cumulative Distribution Function

• Span and Linear Independence

• Eigenvalues and Eigenvectors

• Singular Value Decomposition

• Principal Component Analysis

• Maximum Likelihood Estimation

• What is machine learning?

• Types of machine learning (supervised, unsupervisedand reinforcement learning)

• Applications of machine learning in various industries

• Deep Learning

• Intro to NLP and Time Series Analysis

• Linear regression

• Logistic regression

• Decision trees and random forests

• Support vector machines

• Naive Bayes classifiers

• What is a Random Forest?

• How Random Forests build multiple decision trees

• Handling missing data and outliers

• Using scikit-learn to create a Random Forest classifier

• Hyperparameter tuning, including the number of trees and maximum depth

• Feature selection and importance

• K-fold cross-validation for assessing Random Forest performance

• Evaluation metrics like accuracy, precision, recall, F1-score, and ROC curves

• Basic principles and intuition behind KNN

• How KNN makes predictions based on nearest neighbours

• Different distance metrics like Euclidean, Manhattan, and others

• Using scikit-learn to create a KNN classifier

• Choosing the value of 'k' and its impact on the model

• Evaluating KNN performance with cross-validation

• Selecting the right evaluation metrics for classification problems

• What is unsupervised learning, and how is it different from supervised learning?

• K-Means Clustering

• Hierarchical Clustering

• Density-Based Clustering (DBSCAN)

• Evaluating clustering quality (e.g.Silhouette Score)

• Dimensionality reduction

• Anomaly detection

• Association rule mining

• Recommendation systems

• Metrics for assessing the quality of unsupervised learning results

• Discuss practical applications in fields like finance, healthcare,marketing etc

• Hands-on Implementation: Participants will engage in practical implementation of basic generative models using Python libraries.

• Step-by-Step Tutorials: Follow detailed tutorials to create generative models for image and text generation.

• Practical Exercises: Reinforce understanding through hands-on exercises and real-world applications.

• Introduction to Applications: Discover practical applications of generative AI in data science.

• Data Augmentation: Explore how generative models enhance datasets for machine learning tasks.

• Case Studies: Understand the impact of data augmentation on model performance in image classification and text analysis through real-world examples.

• Synthetic Datasets: Learn to create synthetic datasets using generative models.

• Applications: Generate synthetic data for filling in missing data and balancing imbalanced datasets.

• Real-World Examples: See how synthetic data improves model training and evaluation through practical examples.

The scope of the VIT Data Science and AI course in India is vast, with growing demand for experts in data science, AI, and machine learning across industries. This data science certification course equips you with practical skills in Python for data science, analytics, and AI applications, opening doors to diverse career opportunities in technology, finance, healthcare, and more.

This 16-week online data science and AI course by VIT is delivered through recorded sessions, offering flexibility for working professionals to learn at their own pace. The programme includes data science online classes, practical projects, and Python-based machine learning training, making it ideal for career-focused learners.

This data science online programme is ideal for professionals who want to gain or enhance their skills in Python, data science, machine learning, and Generative AI. Whether you're an early-career professional or a mid-level manager, this course offers valuable insights and practical applications in data science and AI. If you still have questions on whether this programme is a good fit for you, please email support.india@emeritus.org, and a dedicated programme advisor will follow-up with you very shortly. 

This data science programme covers Python, Pandas, Matplotlib, Seaborn, NumPy, and Jupyter Notebook.