MBA in Data Science by College De Paris

About This Course

MBA in Data Science by College de Paris

The MBA in Data Science College De Paris is designed to equip scholars with the chops and knowledge needed 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 by College de Paris 

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 the Course Completion Certification for the MBA in Data Science by College de Paris in association with Kodakco Consultancy™

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