Example Curriculum
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Introduction to Data Science and Overview
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Introduction to BI and EXCEL Analytics
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6 July
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EXCEL -class Exercise
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Python
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Session data and Assignment -answer key-EXCEL
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Python data types
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Python Basics-Data types, variable, Operators,Conditional Statement,Loops
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Python--1. Function & it's type 2. Lambda Function 3. Module-Math ,Random 4. Exceptional Error handling
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Python--1.Pandas Library --Series, Dataframe, Basics of Statistics
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1. Read the file in panda 2. Basic functions on the data 3. Preprocessing-Missing value treatment
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1. Preprocessing on data 2. Data Type conversion- Replace, 1-Hot Encoding, Label Encoder 3. Duplicate checking 4. Library- Numpy
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1) Numpy lib. 2)Exploratory Data Analysis 3) Introduction to Class Imbalance|
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1) Introduction to Class Imbalance 2) Class Imbalance treatment
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1) Introduction to Outlier 2) Outlier Treatment
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1. Skewness 2.Python Visualization By Matplotlib
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1. visualization 2. Correlation
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Inferential Statistics
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1. Introduction to ML 2. Types of ML 3. Supervised ML--Introduction to Linear Regression
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Machine Learning--Linear Regression(Theory + Practical) -- Introduction to LASSO(L1) & RIDGE(L2) -- Overfitting & Underfitting concept --Feature Selection by LASSO
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Machine Learning--LASSO (Theory + Practical) -Ridge (Theory + Practical -Feature Selection by LASSO -Logistic Regression (Theory) -Confusion Matrix
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Machine Learning--Logistics Regression( Practical) , Approach with multiple files , Car data Assignment, Decision Tree (Theory)
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Decision Tree(Practical) , Random Forest (Theory +Practical), Practical approach for 3 files for one project, KNN (Theory)
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KNN [Theory +Practical] , NB [Theory +Practical ], SVM [Theory +Practical ], Boosting [Adaboost, XGBoost], Feature Selection [DT,RF,ChiSqurae test]
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1. Feature selection --RFE, Boruta 2.Unsupervised learning ML 3. K-Means (Theory + Practical) 4. DBSCAN (Theory)
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DBSCAN (Theory+ Practical) 2.PCA(Theory + Practical) 3. Validation
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Advance EXCEL-- Correlation, Covariance, Regression Time Series--Introduction
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NLP--Theory
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Time Series-Practical NLP-Practical, Word cloud Deep Learning
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Brief about Artificial Neural N/W, Introduction to MySQL,Step by step guide for MySQL Workbench
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DDL-CREATE, DROP, ALTER,RENAME,TRUNCATE DML:INSERT INTO
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MySQL-DML,DCL,TCL,CONSTRAINTS-UNIQUE,NOT NULL
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MySQL--CONSTRAINTS-PRIMARY KEY, FOREIGN KEY, DEFAULT, CHECK Functions- limit, sum, avg, count, min, max
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New section
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Power BI: Installation Introduction Charts Table
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Power BI: Installation Introduction Charts Table
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Power BI -Charts
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Power BI -Charts,filters
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Power BI -Filters, Slicers, Text function( Power query)
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Power Query DAX Expression
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22 March
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Further Placement Assistance Process
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Career Building Session
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Power BI- Last session (Dashboard) Interview Question discussion
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