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Get Ahead with MBA in Data Science by College De Paris Certification
Description
Learn to use data visualization tools to communicate insights effectively. Learn Real-world case studies to build practical skills. Experience Hands-on exposure to analytics tools & techniques such as Python, Tableau, and SQL. Gain an in-depth working knowledge of Data Science
MBA in Data Science by College de Paris, study for certification ✔️ Get 192 hours of live two-way online session ✔️ Experienced Trainers ✔️ 100% Placement Support in 2100+ Top Companies. Join now
Key Features
- Ranked Amongst Top 3
- Internship Opportunity
- 5-in-1 Course
- Attend Unlimited Sessions with Multiple Trainers
- 100% Job Support
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
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