Topics include Python fundamentals such as variables

data structures

loops

and functions. Students will also dive into essential data science libraries like NumPy and Pandas to manipulate and analyze data efficiently. Exploratory Data Analysis (EDA) techniques

including visualization methods like histograms

scatter plots

heatmaps

and outlier detection

will be covered to develop actionable insights.

Students will learn inferential statistics foundations

including logistic regression and classification model evaluation. Common statistical tests such as one-mean tests

equality of means

chi-square tests

and one-way ANOVA will be introduced. Additionally

students will explore Decision Trees

learning about impurity measures

splitting criteria

pruning techniques

and regression trees to enhance predictive modeling capabilities.

Introduction to Supervised Learning - Linear Regression: Understand how to learn from data using simple and multiple linear regression while evaluating model performance

Linear Regression Assumptions and Statistical Inference: Explore the differences between statisticians and ML practitioners

analyze linear regression assumptions

and derive statistical inferences.

Logistic Regression: Learn logistic regression fundamentals

interpretation techniques

threshold adjustments

and model evaluation

Decision Trees: Understand decision trees

impurity measures

splitting criteria

pruning methods

and regression tree applications.

Bagging and Random Forest: Improve predictive accuracy using bagging techniques and random forest models. Boosting: Explore boosting algorithms like Adaboost

Gradient Boost

and XGBoost to refine model performance. Model Tuning: Master feature engineering

cross-validation

oversampling/undersampling

hyperparameter tuning

and regularization for model optimization.

K-means Clustering: Understand clustering techniques

scaling importance

silhouette scores

and visual analysis of clustering. Hierarchical Clustering and PCA: Learn hierarchical clustering

cophenetic correlation

and dimensionality reduction through Principal Component Analysis. Introduction to Generative AI: Explore the fundamentals of Generative AI

its distinction from discriminative models

and its real-world applications. Introduction to Prompt Engineering: Gain expertise in crafting and optimizing prompts using various techniques like conditional

few-shot

and chain-of-thought prompting.

Supervised vs Unsupervised Machine Learning: Understand the key differences and applications of supervised and unsupervised learning. Generative AI vs Discriminative AI: Learn how generative models create new data while discriminative models classify existing data. Brief Timeline of Generative AI: Explore the evolution and advancements in Generative AI over time. Overview of Generative Models: Examine various generative models and their working principles. Generative AI Business Applications: Discover real-world business applications of Generative AI across industries.

Querying Data with SQL: Learn database fundamentals

data fetching

filtering

and aggregation techniques. Advanced Querying: Explore in-built functions (numeric

datetime

strings)

joins

and window functions for efficient data manipulation. Enhancing Query Proficiency: Master subqueries and understand the order of query execution for optimized performance.

Introduction to Neural Networks: Understand deep learning history

multi-layer perceptrons

activation functions

neural network training

and backpropagation. Image Processing: Gain insights into computer vision

image representation

edge detection

kernels

padding

strides

pooling

and array flattening. Convolutional Neural Networks: Compare ANN and CNN

explore CNN architectures

apply transfer learning

and study common CNN models.

Introduction to Natural Language Processing: Learn NLP history

applications

text cleaning

vectorization

and sentiment analysis. Word Embeddings: Understand word embeddings

Word2Vec

CBOW

Skip-gram

Negative Sampling

GloVe

and their applications. Attention Mechanism and Transformers: Dive into transformer models

key components

different architectures

and their real-world applications

Storytelling with Data: Understand dimensions

measures

data types

calculations

filtering

and various visualizations. Creating Interactive Dashboards: Master parameters

level of detail calculations

sets

blends

and interactive dashboard creation with storyboarding.

Introduction to Data Science: Gain an overview of data science concepts and methodologies. Python Pre-Work: Learn Python fundamentals to prepare for data science applications. Time Series Forecasting: Understand time series data and apply forecasting techniques. Model Deployment: Learn how to deploy machine learning models effectively in real-world scenarios. Marketing and Retail Analytics: Explore data-driven strategies for marketing and retail decision-making. Financial and Risk Analytics: Apply analytical techniques to financial data for risk management and decision-making.

Course 1: Introduction to Data Science

Course 2: Python Pre-Work

Course 3: Time Series Forecasting

Course 4: Model Deployment

Course 5: Marketing and Retail Analytics

Course 6: Financial and Risk Analytics

Course 7: Web and Social Media Analytics

Course 8: Supply Chain and Logistics Analytics.

This post-graduate certificate program on Data Science and Business Analytics helps you enhance your professional profile through resume building and mock interviews

boosting your confidence to excel in job interviews.

Upon successful completion of the Data Science and Business Analytics course and the associated project

learners will receive a certificate of achievement

showcasing their proficiency in utilizing data science and business analytics tools and techniques.