MBA in Data Science

About This Course

MBA in Data Science

The MBA in Data Science is designed to equip scholars with the chops and knowledge to dissect and manage large sets of complex data. This program provides hands-on experience in data visualization, machine literacy, and statistical modeling.

5 in 1 Course

• Training
• Projects
• Placement Support
• Certification
• Assignments

Industry Projects

Get hands-on experience in capstone industry projects with an MBA in Data Science. 

Takeaways of Your Investment

• 192 hours of intensive training
• Industry-acclaimed Data Science Course Certification
• Free 1-year subscription to Kodakco LMS
• Monthly Masterclass sessions
• The updated industry-oriented study material
• Recorded videos of the sessions
• 100% placement assistance, internship opportunities, and project support exclusively entitled to the professionals
• Add-on supplements provided to effectively deliver projects (Logo Software, E-Books, Question Making Software, Project Guides/Workbooks, Mobile App, etc)
• Get certification from any of our partners - Dunster Business School, Switzerland or College de Paris

Module 1: SQL

Learn about SQL overview and manipulation

• SQL Overview
• SQL Manipulation
• JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
• String Functions
• Mathematical Functions
• Date-Time Functions
• Hunting Tips
• Case Study

Module 2: Power BI

Learn about Business Intelligence (BI) Concepts and many more

• Business Intelligence (BI) Concepts
• Microsoft Power BI (MSPBI) Introduction
• Connecting Power BI with Different Data Sources
• Power Query for Data Transformation
• Data Modelling in Power BI
• Reports in Power BI
• Reports & Visualization Types in Power BI
• Dashboards in Power BI
• Data Refresh in Power BI
• End to End Data Modelling & Visualization
• Case Study

Module 3: Python Programming

Learn about Python basics and programming fundamentals

• Python Basics
• Python Programming Fundamentals
• Python Data Structures
• Working with Data in Python
• Working with NumPy Arrays
• Case Study

Module 4: R Programming

Learn about R basics and programming fundamentals

• R Basics
• R Programming Fundamentals
• Data Structures in R
• Working with Data in R
• Handling Data in R
• Case Study

Module 5: CRISP ML(Q)

Learn concepts of Project Management using CRISP ML(Q)

• Project Management Methodology
• Case Study

Module 6: Data Types and Data Processing

Learn about data cleaning techniques

• Nominal, Ordinal,Interval, Ratio, Data Cleaning Techniques
• Case Study
• Project
• Dataset

Module 7: Statistics

Learn about descriptive and inferential testing

• Descriptive Testing
• Inferential Testing
• Hypothesis Testing
• Case Study

Module 8: EDA

Learn about business moments and graphical representation

• Business moments
• Graphical representation
• Feature Engineering
• Case Study
• Project
• Dataset

Module 9: Mathematical Foundation

Learn about data optimization, derivatives, linear algebra

• Optimization
• Derivatives
• Linear Algebra
• Matrix Operations
• Case Study
• Project

Module 10: Clustering

Learn about Hierarchical and K-means Clustering

• Hierarchical Clustering
• K Means Clustering
• Case Study
• Project
• Dataset

Module 11: Dimension reduction

Learn about PCA and SVD in data science

• PCA
• SVD
• Case Study
• Project
• Dataset

Module 12: Association Rules

Learn about market basket analysis and association rules intuition

• Market Basket Analysis
• Association Rules Intuition
• Association Rules Applications
• Association Rules Terminology Association Rules Performance Measures
• Case Study
• Project

Module 13: Recommendation Engine

Learn about recommendation engine

• Intro to personalized strategy
• Similarity measures
• user-based collaborative filtering
• item-to-item collaborative filtering recommendation engine vulnerabilities
• Case Study
• Project
• Dataset

Module 14: Text Mining and NL

Learn about Text Mining Importance and BOW

• Text Mining Importance
• BOW
• Terminology and Preprocessing
• Textual Data cleaning
• DTM and TDM
• Corpus level
• Positive and negative word clouds
• Social media web scraping
• Case Study
• Project
• Dataset

Module 15: Naive Bayes

You will learn about the Probability concepts, Naive Bayes, etc.

• Probability, Joint probability, conditional probability, Naive Bayes formula, Use case
• Case Study
• Project
• Dataset

Module 16: KNN

Learn about the KNN.

• Nearest Neighbour Classifier, 1- Nearest Neighbour classifier, K- Nearest Neighbour Classifier, Controlling complexity in KNN, Euclidean Distance
• Case Study
• Project
• Dataset

Module 17: Decision Tree

Learn about decision tree, building a decision tree, and algorithms

• What is a Decision Tree
• Building a Decision Tree
• Greedy Algorithm
• Building the best Decision Tree
• Attribute selection- Information gain
• Case Study
• Project
• Dataset

Module 18: Ensemble Techniques

Learn about Ensemble Techniques in this module

• Ensemble Primer
• Voting, Stacking
• Bagging, and Random Forest
• Boosting Models
• Case Study
• Project
• Dataset

Module 19: Confidence Interval

Learn about confidence interval and normal distribution

• Intro to Normal Distribution
• Probability Calculation for normally distributed data
• Normal QQ plot
• Central Limit Theorem
• Confidence Interval
• Case Study
• Project
• Dataset

Module 20: Hypothesis Testing

Learn about Hypothesis testing and flowcharts

• Hypothesis Testing
• Flowchart- Y is continuous
• 2 sample T-Test
• One Way ANOVA
• Flowchart- Y is discrete
• 2 proportion Test
• Chi-Square Test
• Case Study
• Project
• Dataset

Module 21: Regression Techniques

Learn about simple linear and multiple linear regression

• Simple Linear
• Multiple Linear
• Logistic Regression
• Multinomial Regression
• Ordinal Regression
• Advance Regression
• Case Study
• Project
• Dataset

Module 22: SVM

Learn about SVM Hyperplanes and Kernel Tricks

• SVM Hyperplanes
• Best fit Hyperplane
• Kernel Tricks
• Multiclass Classification using SVM
• Case Study
• Project
• Dataset

Module 23: Survival Analytics

Learn about survival analytics, its applications, and its function.

• Intro to Survival Analytics
• Applications
• Time to event
• Censoring
• Kaplan Meier Survival Function
• Case Study
• Project

Module 24: Forecasting

Learn about Forecasting, times series, errors in forecasting, methods of forecasting, etc.

• TimeSeries vs Cross-Sectional Data
• Time Series Dataset
• Forecasting Strategy
• Time Series Components
• Time Series Visualizations
• Time Series Partition
• Forecasting Methods
• Forecasting Errors
• Seasonal Index
• Case Study
• Project

Module 25: ANN

Learn about ANN, Perceptron functions, Error surface, Activation function, etc.

• Neural Network Primer
• Perceptron and Multi-Layered Perceptron Algorithm
• Activation Function
• Error Surface
• Gradient Descent Algorithm
• Case Study
• Project
• Dataset

Module 26: CNN

Learn about CNN

• Image Net Challenge
• Parameters Explosion and MLP
• Convolutional Networks
• Convolutional Layers and Filters
• Pooling Layer
• Practical Issues
• Adversaries
• Case Study
• Project

Module 27: RNN

Learn about Traditional Language Models and Recurrent Neural Networks

• Traditional Language Models
• Wny not MLP
• Recurrent Neural Networks
• RNN types
• CNN+RNN
• Bidirectional RNN
• Deep Bidirectional RNN
• RNN vs LSTM
• Deep RNN vs Deep LSTM
• Case Study
• Project

Elective 1: Deep Learning

Module 1: Deep Learning Architectures

• Logistic regression
• Neural Networks - CNN, RNN, LSTM
• Backpropagation, Deep networks
• Regularization, Dropout, Batch Normalization

Module 2: Deep Learning for Natural Language Processing

• Introduction
• Distributed word representations
• Language Modeling, Convolutional neural networks for Text
• GRUs, LSTMs for Language Modeling, Attention and Applications, GPT, BERTs and Variants
• Recurrent Neural Networks (Unidirectional and Bidirectional), Machine Translation, POS, Sentence Classification, Text Generation

Module 3: Deep Learning for Speech and Audio Processing

• Audio representations for deep learning
• Speech Recognition
• End-to-end deep networks
• Detection Models

Module 4: Deep Learning for Computer Vision

• Popular CNN architectures
• Transfer learning, autoencoders
• Object detection, image segmentation
• RNN and LSTM for image captioning/video

Elective 2: Artificial Intelligence

Module 1: Neural Networks

• Introduction to Neural Networks and Deep Learning
• Activation Functions
• Feedforward Neural Network
• Backpropagation and Gradient Descent
• Learning Rate Setting and Tuning
• Introduction to Keras
• Fully Connected Layer-Forward and Backward Pass
• Softmax and Cross-Entropy Loss
• Introduction to babysitting the learning Process12
• Data Preprocessing
• Data Augmentation
• Weight Initialization
• Regularization-Batch Normalization
• Regularization-Dropout
• Working with Google Collab

Module 2: Computer Vision

• Working with Images
• Introduction to convolutions2D convolutions for Images
• Convolution-Forward and Backward
• Transposed Convolution and Fully Connected Layer as a Convolution
• Pooling: Max Pooling and other Poolings
• CNN Architectures
• ALexNet, LeNet,ZFNet,VGGNet, GoogleNet, ResNet
• GPU Vs CPU
• Transfer Learning
• Semantic Segmentation using UNet
• Inception and Mobile Net Models
• Object Detection with region proposals, YOLO, and SSD
• Bounding Box Regressor
• Siamese Network for Metric Learning

Module 3: Natural Language Processing

• Introduction to NLP
• Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF
• N-gram models and channel models
• Word Embedding
• Dense Encoding
• Word-2-Vec Vectors and Glove Vectors
• POS Tagging
• Named Entity recognition
• Sequential models
• Introduction to RNN, LSTM
• LSTM Forward Pass
• LSTM Backprop through time
• Applications of LSTM
• Advanced LSTM Structures
• Encoder-Decoder Attention

Elective 3: AI Tools and ChatGPT

Module 1: Prompt Engineering and ChatGPT Plugins

• Introduction to Prompt Engineering
• Why Prompt Engineering?
• What is Prompt Engineering?
• Applications of Prompt Engineering
• Types of Prompting
• Priming Prompts
• Prompt Decomposition
• How to Get Better Responses from ChatGPT
• ChatGPT Plugins

Module 2: ChatGPT for Developers and Exploring ChatGPT API

• ChatGPT for Creating Programs
• ChatGPT for Debugging
• ChatGPT for Integrating New Features
• ChatGPT for Testing
• Documenting Your Code with ChatGPT
• Essential Application Programming Interface (API) Concepts
• Introducing OpenAI and ChatGPT API

Module 3: GPT Models, Pre-processing and Fine-tuning ChatGPT

• Overview of language models
• Understanding the architecture of the GPT model
• GPT models: advantages and disadvantages
• Overview of the pre-trained GPT models available for fine-tuning
• Training of ChatGPT
• Data preparation
• Model architecture
• Hyperparameter tuning
• Training process

Module 4: Popular AI Tools

• Dall-E 2
• Midjourney
• Bard
• Hugging Face
• NLG Cloud
• Copy.ai
• Tome
• Codeium
• WriteSonic

Elective 4: Data Mining and Data Wrangling

Module 1: Introduction to Data mining and data wrangling

• Overview of Data Mining
• Introduction to Data Wrangling
• Types of Data: Structured vs. Unstructured
• Common Tools and Technologies

Module 2: Data Collection

• Data Sources: Databases, APIs, Web Scraping
• Introduction to SQL for Data Retrieval
• Ethical Considerations in Data Collection

Module 3: Data Transformation

• Data Transformation Techniques (Aggregation, Pivoting)
• Feature Engineering: Creating New Features
• Encoding Categorical Variables
• Data Scaling Techniques

Module 4:Exploratory Data Analysis (EDA)

• Importance of EDA in Data Mining
• Visualization Techniques (Matplotlib, Seaborn, ggplot)
• Descriptive Statistics and Correlation Analysis

Module 5: Data Cleaning Techniques

• Identifying and Handling Missing Values
• Data Type Conversion
• Outlier Detection and Treatment
• Standardization and Normalization