Python Intro,IDE and Python Packages
Python Programming
Python Data Types – Dictionary, List and Set
Numpy Packages – Array Handling and Manupulation
Pandas Packages – Dataframe and Loading Excel, CSV File
Matplotlib Packages – Line graph and Visualisation
Histogram, Scatter Diagram, Box Plot and Bar Graph
Area Chart, Dual Axis, Array reshaping, reverse matrix analysis
Python – Operators and String Manupulation Control Structures(IF,IF-ELSE,IF-ELIF-ELSE,WHILE & FOR LOOP)
Python – Data Preparation Process
Python – Functions WITH and WITHOUT arguments
Python – File Processing and Data Collection Methods
Python – Time Series Analysis and Forcasting
Python – Simple Predictive Analysis
Data Science with Python
Data Science Application across Multiple Domain and Business Function
Data Science Project LifeCycle
Multiple Predictive Model using Python
Python – Simple and Multiple Predictive Model in Practical
Python Correlation Analysis
Python Classication Model Buildin
Data Science – Experimental Design Analysis
Classication Technique – Discriminant Analyssi
Data Science – Association Rule – Apriori Algorithm
Data Science – Building Recommendation System – (Market Basket Analysis) Data Architecture Design, Data Warehousing and it’s Schema Design Image Processing and Image Extraction Image Processing and Object Recognition Summarisation of Data Science Algorithm (Data Science Process)
Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning
Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building
Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classication, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling
ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA
Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis
Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes
Distance as Classier, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classication
Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear
Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling
Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA
Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method
Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule
Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation
Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models
Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau
What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.
Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions
Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.
R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data
Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages
Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.
Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression
Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example
Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering
Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis
Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes
K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN
What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree
Ensample of Decision Tree.
Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM
RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics
Unsupervised Machine Learning – Merger and Acquisitions Analytics
Bank Loan Modeling – Automation of loan eligibility process – Dream Housing Finance Company
Prediction of English Premier League (EPL) Championship
Zomato Delivery Performance Analysis
Employee Attrition Rate Analysis
Team Deposit Plan – Machine Learning Classification – Portuguese Bankinh Institution
Predicting house prices for using supervised Machine Learning
Predictive Analytics with model simulationm – Ames Housing Authority.
Employee Termination Analysis
Principal Component Analysis – Dimension Reduction – LKP Share & Securities.
Analysis the Real-time Stock market using Regression
Predict the Medical condition of Person
Iris Flower classification is done using sepal and petal
Will person survive on titanic ship .
Will classify the person detect with cancer or not Customer Segmentation : Customer will divided into segments and behavior will analyze
Person will be loan defaulters in future of not .
kids Handwritten digits will be classified
test the wine quality and classifiy it.
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Our Industry mentor team will guide you with:
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– Help execute in-class assignments and case studies
– Discuss & identify learning gaps and other solutions such as refresher sessions and one-on-one project feedback
-Set learning goals
-Discuss your progress status with trainers and other industry mentors on a regular basis to ensure consistent advancement
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