Details
This data science training covers data handling, visualization, statistical modelling and machine learning effectively with practical examples and case studies making it one of the most practical Python online training. Even if you are looking for live Data Science oriented Python training in your college this is just the right course.
Specifications
Note: Course Curriculum may increase or decrease as per industries need
1.
What
is analytics & Data Science?
2. Common Terms in Analytics
3. Analytics vs. Data warehousing, OLAP, MIS Reporting
4. Relevance in industry and need of the hour
5. Types of problems and business objectives in various industries
6. How leading companies are harnessing the power of analytics?
7. Critical success drivers
8. Overview of analytics tools & their popularity
9. Analytics Methodology & problem solving framework
10. List of steps in Analytics projects
11. Identify the most appropriate solution design for the given problem statement
12. Project plan for Analytics project & key milestones based on effort estimates
13. Build Resource plan for analytics project
14. Why Python for data science?
2. Common Terms in Analytics
3. Analytics vs. Data warehousing, OLAP, MIS Reporting
4. Relevance in industry and need of the hour
5. Types of problems and business objectives in various industries
6. How leading companies are harnessing the power of analytics?
7. Critical success drivers
8. Overview of analytics tools & their popularity
9. Analytics Methodology & problem solving framework
10. List of steps in Analytics projects
11. Identify the most appropriate solution design for the given problem statement
12. Project plan for Analytics project & key milestones based on effort estimates
13. Build Resource plan for analytics project
14. Why Python for data science?
1.
Overview
of Python- Starting with Python
2. Introduction to installation of Python
3. Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
4. Understand Jupyter notebook & Customize Settings
5. Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
6. Installing & loading Packages & Name Spaces
7. Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
8. List and Dictionary Comprehensions
9. Variable & Value Labels – Date & Time Values
10. Basic Operations - Mathematical - string - date
11. Reading and writing data
12. Simple plotting
13. Control flow & conditional statements
14. Debugging & Code profiling
15. How to create class and modules and how to call them?
2. Introduction to installation of Python
3. Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
4. Understand Jupyter notebook & Customize Settings
5. Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
6. Installing & loading Packages & Name Spaces
7. Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
8. List and Dictionary Comprehensions
9. Variable & Value Labels – Date & Time Values
10. Basic Operations - Mathematical - string - date
11. Reading and writing data
12. Simple plotting
13. Control flow & conditional statements
14. Debugging & Code profiling
15. How to create class and modules and how to call them?
1.
Numpy,
scify, pandas, scikitlearn, statmodels, nltk etc
2. Accessing/Importing And Exporting Data Using Python Modules
3. Importing Data from various sources (Csv, txt, excel, access etc)
4. Database Input (Connecting to database)
5. Viewing Data objects - subsetting, methods
6. Exporting Data to various formats
7. Important python modules: Pandas, beautifulsoup
2. Accessing/Importing And Exporting Data Using Python Modules
3. Importing Data from various sources (Csv, txt, excel, access etc)
4. Database Input (Connecting to database)
5. Viewing Data objects - subsetting, methods
6. Exporting Data to various formats
7. Important python modules: Pandas, beautifulsoup
1.
Cleansing
Data with Python
2. Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
3. Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
4. Python Built-in Functions (Text, numeric, date, utility functions)
5. Python User Defined Functions
6. Stripping out extraneous information
7. Normalizing data
8. Formatting data
9. Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
2. Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
3. Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
4. Python Built-in Functions (Text, numeric, date, utility functions)
5. Python User Defined Functions
6. Stripping out extraneous information
7. Normalizing data
8. Formatting data
9. Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
1.
Need
for structured exploratory data
2. EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
3. Identify missing data
4. Identify outliers data
5. Visualize the data trends and patterns
2. EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
3. Identify missing data
4. Identify outliers data
5. Visualize the data trends and patterns
1.
Need
of Data preparation
2. Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
3. Variable Reduction Techniques - Factor & PCA Analysis
2. Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
3. Variable Reduction Techniques - Factor & PCA Analysis
1.
Introduction
- Applications
2. Assumptions of Linear Regression
3. Building Linear Regression Model
4. Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
5. Assess the overall effectiveness of the model
6. Validation of Models (Re running Vs. Scoring)
7. Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of
Results - Business Validation - Implementation on new data
2. Assumptions of Linear Regression
3. Building Linear Regression Model
4. Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
5. Assess the overall effectiveness of the model
6. Validation of Models (Re running Vs. Scoring)
7. Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
1.
Introduction
- Applications
2. Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
3. Building Logistic Regression Model (Binary Logistic Model)
4. Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS etc.
5. Validation of Logistic Regression Models (Re running Vs. Scoring)
6. Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers
7. Interpretation of Results - Business Validation - Implementation on new data
2. Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
3. Building Logistic Regression Model (Binary Logistic Model)
4. Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS etc.
5. Validation of Logistic Regression Models (Re running Vs. Scoring)
6. Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers
7. Interpretation of Results - Business Validation - Implementation on new data
1.
Introduction
to Machine Learning & Predictive Modeling
2. Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting 3. Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
4. Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
5. Overfitting (Bias-Variance Trade off) & Performance Metrics
6. Feature engineering & dimension reduction
7. Concept of optimization & cost function
8. Overview of gradient descent algorithm
9. Overview of Cross validation(Bootstrapping, K-Fold validation etc)
10. Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision
sensitivity, specificity, confusion metrics )
2. Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting 3. Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
4. Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
5. Overfitting (Bias-Variance Trade off) & Performance Metrics
6. Feature engineering & dimension reduction
7. Concept of optimization & cost function
8. Overview of gradient descent algorithm
9. Overview of Cross validation(Bootstrapping, K-Fold validation etc)
10. Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision
sensitivity, specificity, confusion metrics )
1.
What
is segmentation & Role of ML in Segmentation?
2. Concept of Distance and related math background
3. K-Means Clustering
4. Expectation Maximization
5. Hierarchical Clustering
6. Spectral Clustering (DBSCAN)
7. Principle component Analysis (PCA)
2. Concept of Distance and related math background
3. K-Means Clustering
4. Expectation Maximization
5. Hierarchical Clustering
6. Spectral Clustering (DBSCAN)
7. Principle component Analysis (PCA)
1.
Decision
Trees - Introduction - Applications
2. Types of Decision Tree Algorithms
3. Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node Information Gain, Gini Index, Chi Square, Regression Trees
4. Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
5. Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
6. Decision Trees - Validation
7. Overfitting - Best Practices to avoid
2. Types of Decision Tree Algorithms
3. Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node Information Gain, Gini Index, Chi Square, Regression Trees
4. Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
5. Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
6. Decision Trees - Validation
7. Overfitting - Best Practices to avoid
1.
Concept
of Ensembling
2. Manual Ensembling Vs. Automated Ensembling
3. Methods of Ensembling (Stacking, Mixture of Experts)
4. Bagging (Logic, Practical Applications)
5. Random forest (Logic, Practical Applications)
6. Boosting (Logic, Practical Applications)
7. Ada Boost
8. Gradient Boosting Machines (GBM)
XGBoost
2. Manual Ensembling Vs. Automated Ensembling
3. Methods of Ensembling (Stacking, Mixture of Experts)
4. Bagging (Logic, Practical Applications)
5. Random forest (Logic, Practical Applications)
6. Boosting (Logic, Practical Applications)
7. Ada Boost
8. Gradient Boosting Machines (GBM)
1.
Motivation
for Support Vector Machine & Applications
2. Support Vector Regression
3. Support vector classifier (Linear & Non-Linear)
4. Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
5. Interpretation of Outputs and Fine tune the models with hyper parameters
6. Validating SVM models
2. Support Vector Regression
3. Support vector classifier (Linear & Non-Linear)
4. Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
5. Interpretation of Outputs and Fine tune the models with hyper parameters
6. Validating SVM models
1. What is KNN & Applications?
2. KNN for missing treatment
3. KNN For solving regression problems
4. KNN for solving classification problems
5. Validating KNN model
6. Model fine tuning with hyper parameters
1.
Concept
of Conditional Probability
2. Bayes Theorem and Its Applications
3. Naïve Bayes for classification
4. Applications of Naïve Bayes in Classifications
2. Bayes Theorem and Its Applications
3. Naïve Bayes for classification
4. Applications of Naïve Bayes in Classifications
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- Delivery ModeOnline
- Program Fee
₹7999
- Registration Fee
₹1499
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