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
- Date-Time Functions
- Hunting Tips
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 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
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
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
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
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
Module 7: Statistics
In this module the candidate will learn about the statistics and its types.
- Descriptive,Inferential, Hypothesis Testing
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
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
Module 10: Clustering
In this module the candidate will learn about the clustering, its types and function.
- Hierarchical Clustering, K Means Clustering
Module 11: Dimension reduction
In this module the candidate will learn about the concept of Dimension reduction.
- PCA,SVD
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
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
Module 14: Text Mining and NL
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
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
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
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
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
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
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
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
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
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
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 Methods, Forecasting Errors, Seasonal Index
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
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
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
Module 28: Complimentary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Module 29: Complimentary Module 2: Resume Writing
- Resume Writing

Know the complete offerings of our Advanced Certificate Program in Data Science & AI by E&ICT Academy, IIT Guwahati

One of India's cities with the quickest growth rates is Patna, the state of Bihar's capital. Some of the most beautiful historical sites that depict local culture are located in Patna. The city is where many well-known musical genres began, including the dadra, khayaal, hammarkajli, and kajli. Kusumpura, the former seat of the Mauryan monarch Ashoka, then became Pushpapuri, then Pataliputra, then Azimabad, and finally Patna.

Furthermore, Patna has a particular preference for desserts. A common dish made with gram flour and dry fruits is called naivedyam. Chandrakala/Gujiya, Khaja, Kesar Peda, Dal Peda, Malpua, and Parwal ki mithai are some other well-known sweets. Chana Ghugni is a popular fast food snack in the area. foods like samosas, jhal muri, choora, and thekua.

The Madhubani paintings, wall hangings, jewelry made of beads, and leather goods are what Patna is most famous for. There are numerous temples and pilgrimage sites across the city that sell a broad selection of prashad, holy texts, statues, and images of gods and goddesses, as well as other religious items.

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Henry Harvin teaches the best data science course in Patna. The student has the option of obtaining accreditation after completing the course. Those seeking data science courses in Patna are in the right place. They can become gold members of the institute. The data science course in Patna has many advantages. Real-time instruction is provided so that students can communicate with their teachers. Even real-world projects are presented to the students.

Through internship opportunities, individuals are exposed to the real world and learn new skills. After successfully completing the course, the student may call themselves a "certified data scientist." With a gold membership, students can keep current in the data science field. Online education has a variety of alternatives