Name: Introduction To MBA in Data Science by College De Paris in Oklahoma City | Henry Harvin
Uploaded: 2023-06-18 15:38:10

Module 1: SQL
In this module the candidate will learn about concepts of SQL, which includes understanding JOIN, String functions etc.
- SQL Overview
- SQL Manipulation
- JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
- String Functions
- Mathematical Functions
- Data-Time Functions
- Hunting Tips
- Case Study
- Project
Module 2: Power BI
In this module the candidate will learn about Power BI Concepts such as Power Query, Data Modelling, Reporting etc.
- Business Intelligence (BI) Concepts
- Microsoft Power BI (MSPBI) Introduction
- Connecting Power BI with Different Data Sources
- Power Query for Data Transformation
- Data Modeling 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 Modeling & Visualization
- Case Study
- Project
Module 3: Python Programming
In this module the candidate will learn about the major concepts of Python Programming
- Python basics
- Python programming fundamentals
- Python Data Structures
- Working with Data in Python
- Working with NumPy Arrays
- Case Study
- Project
- Dataset
Module 4: R Programming
In this module the candidate will learn about the major concepts of R programming
- R Basics
- R Programming Fundamentals
- Data Structures in R
- Working with Data in R
- Handling Data in R
- Case Study
- Project
Module 5: CRISP ML(Q)
In this module the candidate will learn about the concept of Project Management using CRISP ML(Q)
- Project Management Methodology
- Case Study
Module 6: Data Types and Data Processing
In this module the candidate will learn about the Data types and its processing.
- Nominal, Ordinal,Interval, Ratio, Data Cleaning techniques
- Case Study
- Project
- Dataset
Module 7: Statistics
In this module the candidate will learn about the statistics and its types.
- Descriptive,Inferential, Hypothesis Testing
- Case Study
- Project
Module 8: EDA
In this module the candidate will learn about the EDA, Business moments and features about engineering.
- Business moments, Graphical representation, Feature Engineering
- Case Study
- Project
- Dataset
Module 9: Mathematical Foundation
In this module the candidate will learn about the Mathematical foundation which includes understanding optimization, linear algebra etc.
- Optimization, Derivatives, Linear Algebra, Matrix Operations
- Case Study
- Project
Module 10: Clustering
In this module the candidate will learn about the clustering, its types and function.
- Hierarchical Clustering, K Means Clustering
- Case Study
- Project
- Dataset
Module 11: Dimension reduction
In this module the candidate will learn about the concept of Dimension reduction.
- PCA,SVD
- Case Study
- Project
- Dataset
Module 12: Association Rules
In this module the candidate will learn about the Association rules, terminologies used, performance measures and many more.
- Market Basket Analysis, Association Rules Intuition, Association Rules Applications, Association Rules Terminology Association Rules Performance Measures
- Case Study
- Project
Module 13: Recommendation Engine
In this module the candidate will learn about the Recommendation engine and its functions.
- 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 NLP
In this module the candidate will learn about the Text Mining, importance, terminology, BOW, DTM, TDM Etc.
- 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
In this module the candidate 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
In this module the candidate will 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
In this module the candidate will learn about the Decision tree, how to build it, Attribute selection, etc.
- 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
In this module the candidate will learn about the Ensemble Techniques, Ensemble Primer, voting, stacking, bagging etc.
- Ensemble Primer, Voting, Stacking, Bagging, and Random Forest, Boosting Models
- Case Study
- Project
- Dataset
Module 19: Confidence Interval
In this module the candidate will learn about the Confidence Interval. Normal Distribution, Central Limit Theorem etc.
- 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
In this module the candidate will learn about the Hypothesis testing and different types of tests.
- 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
In this module the candidate will learn about the Regression, its types- Simple, multiple etc.
- Simple Linear, Multiple Linear, Logistic Regression, Multinomial Regression, Ordinal Regression, Advance Regression
- Case Study
- Project
- Dataset
Module 22: SVM
In this module the candidate will learn about the SVM, Best fit, kernel tricks and many more.
- SVM Hyperplanes, Best fit Hyperplane, Kernel Tricks, Multiclass Classification using SVM
- Case Study
- Project
- Dataset
Module 23: Survival Analytics
In this module the candidate will learn about Survival Analytics, its applications and function.
- Intro to Survival Analytics, Applications, Time to event, Censoring, Kaplan Meier Survival Function
- Case Study
- Project
Module 24: Forecasting
In this module the candidate will 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 Methoods, Forecasting Errors, Seasonal Index
- Case Study
- Project
Module 25: ANN
In this module the candidate will 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
In this module the candidate will learn about CNN, Net challenges, MLP Filters and more.
- Image Net Challenge, Parameters Explosion and MLP, Convolutional Networks, Convolutional Layers and Filters, Pooling Layer, Practical Issues, Adversaries
- Case Study
- Project
Module 27: RNN
In this module the candidate will learn more about RNN, RNN Vs. Deep LSTM Etc.
- 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's
- Case Study
- Project

MBA in Data Science in Oklahoma City

23 Dec 2024

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MBA in Data Science

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Key Highlights

192 Hours of Instructor-Led Sessions

64 Hours of Live Interactive Doubt Solving Sessions

32 Hours of Live Master Sessions by Industry Experts

288 Hours Self Paced Learning

288 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 19 Capstone Projects

53 Auto-Graded Assessments

38+ Industry Case Studies

288 Guided Hands-On Exercises

19+ Assignments and Mini Projects

2 Mock Interviews and 2 Hackathons

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13 Hours of Mentorship by Industry Experts

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Curriculum For MBA in Data Science in Oklahoma City

Module 1: SQL

Module 2: Power BI

Module 3: Python Programming

Module 4: R Programming

Module 5: CRISP ML(Q)

Module 6: Data Types and Data Processing

Module 7: Statistics

Module 8: EDA

Module 9: Mathematical Foundation

Module 10: Clustering

Module 11: Dimension reduction

Module 12: Association Rules

Module 13: Recommendation Engine

Module 14: Text Mining and NLP

Module 15: Naive Bayes

Module 16: KNN

Module 17: Decision Tree

Module 18: Ensemble Techniques

Module 19: Confidence Interval

Module 20: Hypothesis Testing

Module 21: Regression Techniques

Module 22: SVM

Module 23: Survival Analytics

Module 24: Forecasting

Module 25: ANN

Module 26: CNN

Module 27: RNN

Admission Closes On 23 Dec 2024

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What you'll Learn in this course

Normal Distribution

Calculation for normally distributed data

Ensemble Primer, Voting & Stacking

Decision Tree

Regression Techniques

Text Minning and NL

User-based collaborative Filtering

Hierarchical Clustering & K Means Clustering

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