Post Graduate Program in Data Science

Expedite your Career with a Prestigious Post Graduate Program in Data Science. Learn Data Science with US #1 course and Get Ahead in Your Career. Learn Extensive use of Tools & Technologies and Analytics Techniques.

Why Post Graduation in Data Science?

Post Graduate Data Science course in the contemporary world offers numerous benefits, including enhanced career prospects, versatile skill development, and opportunities to drive innovation and societal change through data-driven approaches. There is a growing demand for skilled data scientists across industries as organizations increasingly rely on data-driven decision-making to gain a competitive edge. With this proliferation of data, the demand for data scientists is expected to continue growing in the future.

Key Features

Discover essential features of Post Graduate Program in Data Science by College de Paris and enhance your learning journey.

Live Two-Way Training

Get 192 Hours of Live Two-Way Online Session
Job Support

100% Placement Support in 2100+ Top Companies
Ranked Amongst Top 3

Learn from Top Ranked Online Learning Platform
Internship Opportunity

Get Wide Range of Hand-on Experience from Internships Opportunities
5-in-1 Course

Get support in Training, Projects, Placement Support, Certification & Assignments
Experienced Trainers

Attend Unlimited Sessions with Multiple Trainers

Course curriculum

1. SQL
2. SQL Overview
3. SQL Manipulation
4. JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
5. String Functions
6. Mathematical Functions
7. Data-Time Functions
8. Hunting Tips
9. Case Study 1
10. Case Study 2
11. Project 1
12. Project 2
1. Power BI Part 1
2. Power BI Part 2
3. Power BI Part 3
4. Power BI Part 4
5. Power BI Part 5
6. Power BI Part 6
7. Power BI Part 7
8. Power BI Part 8
9. Business Intelligence (BI) Concepts
10. Microsoft Power BI (MSPBI) Introduction
11. Connecting Power BI with Different Data Sources
12. Power Query for Data Transformation
13. Data Modeling in Power BI
14. Reports in Power BI
15. Reports & Visualization Types in Power BI
16. Dashboards in Power BI
17. Data Refresh in Power BI
18. End to End Data Modeling & Visualization
19. Case Study 1
20. Case Study 2
21. Project 1
22. Project 2
1. Python Programming Part 1
2. Python Programming Part 2
3. Python Programming Part 3
4. Python Programming Part 4
5. Python Programming Part 5
6. Python Programming Part 6
7. Python basics
8. Python programming fundamentals
9. Python Data Structures
10. Working with Data in Python
11. Working with NumPy Arrays
12. Case Study 1
13. Case Study 2
14. Project 1
15. Project 2
16. Project 3
17. Data Set Project 3
1. R Programming Part 1
2. R Programming Part 2
3. R Basics
4. R Programming Fundamentals
5. Data Structures in R
6. Working with Data in R
7. Handling Data in R
8. Case Study
9. Project 1
10. Project 2
1. CRISP ML(Q)
2. Project Management Methodology
3. Case Study 1
4. Case Study 2
1. Data Types and Data Processing
2. Nominal, Ordinal, Interval, Ratio, Data Cleaning techniques
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Statistics
2. Descriptive, Inferential, Hypothesis Testing
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. EDA
2. Business moments, Graphical representation, Feature Engineering
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Mathematical Foundation
2. Optimization, Derivatives, Linear Algebra, Matrix Operations
3. Case Study 1
4. Case Study 2
5. Case Study 3
6. Project 1
7. Project 2
1. Clustering
2. Hierarchical Clustering, K Means Clustering
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Dimension reduction
2. PCA, SVD
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Association Rules
2. Market Basket Analysis, Association Rules Intuition, Association Rules Applications, Association Rules Terminology Association Rules Performance Measures
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. Recommendation Engine
2. Intro to personalized strategy, similarity measures, user-based collaborative filtering, item-to-item collaborative filtering, recommendation engine vulnerabilities
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Text Mining and NL
2. Text Mining Importance, BOW, Terminology and Preprocessing, Textual Data cleaning, DTM and TDM, Corpus level, positive and negative word clouds, social media web scraping
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Project 3
1. Naive Bayes
2. Probability, Joint probability, conditional probability, Naive Bayes formula, Use case
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. KNN
2. Nearest Neighbour Classifier, 1- Nearest Neighbour classifier, K- Nearest Neighbour Classifier, Controlling complexity in KNN, Euclidean Distance
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set Project 2
1. Decision Tree
2. What is a Decision Tree, Building a Decision Tree, Greedy Algorithm, Building the best Decision Tree, Attribute selection- Information gain
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Ensemble Techniques
2. Ensemble Primer, Voting, Stacking, Bagging, and Random Forest, Boosting Models
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Confidence Interval
2. Intro to Normal Distribution, Probability Calculation for normally distributed data, Normal QQ plot, Central Limit Theorem, Confidence Interval
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Hypothesis Testing
2. Hypothesis Testing, Flowchart- Y is continuous, 2 sample T-Test, One Way ANOVA, Flowchart- Y is discrete, 2 proportion Test, Chi-Square Test
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Regression Techniques
2. Simple Linear, Multiple Linear, Logistic Regression, Multinomial Regression, Ordinal Regression, Advance Regression
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. SVM
2. SVM Hyperplanes, Best fit Hyperplane, Kernel Tricks, Multiclass Classification using SVM
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. Survival Analytics
2. Intro to Survival Analytics, Applications, Time to event, Censoring, Kaplan Meier Survival Function
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. Forecasting
2. 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
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. ANN
2. Neural Network Primer, Perceptron and Multi-Layered Perceptron Algorithm, Activation Function, Error Surface, Gradient Descent Algorithm
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
7. Data Set
1. CNN
2. Image Net Challenge, Parameters Explosion and MLP, Convolutional Networks, Convolutional Layers and Filters, Pooling Layer, Practical Issues, Adversaries
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2
1. RNN
2. 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
3. Case Study 1
4. Case Study 2
5. Project 1
6. Project 2

About this course

$4,999.00
212 lessons
0 hours of video content

5-in-1 Course

Training
Projects
Exam Assistance
Certification
Assignments

About Kodakco

Kodakco is an upskilling & technology consulting company with offices in Hyderabad & San Francisco. We give trainings to individuals in the field of Medical, Language, Six Sigma, Design & SAP at B2C level. We provide consulting & enterprise application services such as SAP Business Suite S/4HANA, SAP Cloud Platform, Successfactors, C/4HANA with Hybris C4C, BW/4HANA, and Cloud Analytics to companies at B2B level. Our focus is on the customer first approach to bring value with the relationship.

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Live Two-Way Training

Job Support

Ranked Amongst Top 3

Internship Opportunity

5-in-1 Course

Experienced Trainers

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 NL

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

About this course

Training

Projects

Exam Assistance

Certification

Assignments