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INTRODUCTION TO DATA SCIENCE WITH PYTHON
- What is analytics & Data Science? What is analytics &#...
- Common Terms in Analytics Common Terms in Anal...
- Analytics vs. Data warehousing, OLAP, MIS Reporting Analytics vs. Data w...
- Relevance in industry and need of the hour Relevance in industr...
- Types of problems and business objectives in various industries 6. How leading companies are harnessing the power of analytics? Types of problems an...
- Critical success drivers Critical success dri...
- Overview of analytics tools & their popularity Overview of analytic...
- Analytics Methodology & problem solving framework Analytics Methodolog...
- List of steps in Analytics projects List of steps in Ana...
- Identify the most appropriate solution design for the given problem statement Identify the most ap...
- Project plan for Analytics project & key milestones based on effort estimates Project plan for Ana...
- Build Resource plan for analytics project Build Resource plan ...
- Why Python for data science? Why Python for data ...
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PYTHON: ESSENTIALS (CORE)
- Overview of Python- Starting with Python Overview of Python- ...
- Introduction to installation of Python Introduction to inst...
- Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…) Introduction to Pyth...
- Understand Jupyter notebook & Customize Settings Understand Jupyter n...
- Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Concept of Packages/...
- Installing & loading Packages & Name Spaces Installing & lo...
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries) Data Types & Da...
- List and Dictionary Comprehensions List and Dictionary ...
- Variable & Value Labels – Date & Time Values Variable & Valu...
- Basic Operations – Mathematical – string – date Basic Operations ...
- Reading and writing data Reading and writing ...
- Simple plotting Simple plotting
- Control flow & conditional statements Control flow & ...
- Debugging & Code profiling Debugging & Cod...
- How to create class and modules and how to call them? How to create class ...
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SCIENTIFIC DISTRIBUTIONS USED IN PYTHON FOR DATA SCIENCE
- Numpy, scify, pandas, scikitlearn, statmodels, nltk etc Numpy, scify, pandas...
- Accessing/Importing And Exporting Data Using Python Modules Accessing/Importing ...
- Importing Data from various sources (Csv, txt, excel, access etc) 4. Database Input (Connecting to database) Importing Data from ...
- Viewing Data objects – subsetting, methods Viewing Data objects...
- Exporting Data to various formats Exporting Data to va...
- Important python modules: Pandas, beautifulsoup Important python mod...
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DATA MANIPULATION – CLEANSING – MUNGING USING PYTHON MODULES
- Cleansing Data with Python Cleansing Data with ...
- Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc) Data Manipulation st...
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc) Data manipulation to...
- Python Built-in Functions (Text, numeric, date, utility functions) Python Built-in Func...
- Python User Defined Functions Python User Defined ...
- Stripping out extraneous information Stripping out extran...
- Normalizing data Normalizing data
- Formatting data Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc) Important Python mod...
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DATA EXPLORATION FOR MODELING
- Need for structured exploratory data Need for structured ...
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report) EDA framework for ex...
- Identify missing data Identify missing dat...
- Identify outliers data Identify outliers da...
- Visualize the data trends and patterns Visualize the data t...
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DATA PREPARATION
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LINEAR REGRESSION: SOLVING REGRESSION PROBLEMS
- Introduction – Applications Introduction –...
- Assumptions of Linear Regression Assumptions of Linea...
- Building Linear Regression Model Building Linear Regr...
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc) Understanding standa...
- Assess the overall effectiveness of the model Assess the overall e...
- Validation of Models (Re running Vs. Scoring) Validation of Models...
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.) Interpretation of Results – Business Validation – Implementation on new data Standard Business Ou...
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LOGISTIC REGRESSION: SOLVING CLASSIFICATION PROBLEMS
- Introduction – Applications Introduction –...
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models Linear Regression Vs...
- Building Logistic Regression Model (Binary Logistic Model) Building Logistic Re...
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS etc. Understanding standa...
- 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 Validation of Logist...
- Interpretation of Results – Business Validation – Implementation on new data Interpretation of Re...
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MACHINE LEARNING -PREDICTIVE MODELING – BASICS
- Introduction to Machine Learning & Predictive Modeling Introduction to Mach...
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting Types of Business pr...
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning Major Classes of Lea...
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation) Different Phases of ...
- Overfitting (Bias-Variance Trade off) & Performance Metrics Overfitting (Bias-Va...
- Feature engineering & dimension reduction Feature engineering ...
- Concept of optimization & cost function Concept of optimizat...
- Overview of gradient descent algorithm Overview of gradient...
- Overview of Cross validation(Bootstrapping, K-Fold validation etc) Overview of Cross va...
- Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision sensitivity, specificity, confusion metrics ) Model performance me...
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UNSUPERVISED LEARNING: SEGMENTATION
- What is segmentation & Role of ML in Segmentation? What is segmentation...
- Concept of Distance and related math background Concept of Distance ...
- K-Means Clustering K-Means Clustering
- Expectation Maximization Expectation Maximiza...
- Hierarchical Clustering Hierarchical Cluster...
- Spectral Clustering (DBSCAN) Spectral Clustering ...
- Principle component Analysis (PCA) Principle component ...
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SUPERVISED LEARNING: DECISION TREES
- Decision Trees – Introduction – Applications Decision Trees ̵...
- Types of Decision Tree Algorithms Types of Decision Tr...
- Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node Information Gain, Gini Index, Chi Square, Regression Trees Construction of Deci...
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness Generalizing Decisio...
- Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules Pruning a Decision T...
- Decision Trees – Validation Decision Trees ̵...
- Overfitting – Best Practices to avoid Overfitting – ...
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SUPERVISED LEARNING: ENSEMBLE LEARNINGS
- Concept of Ensembling Concept of Ensemblin...
- Manual Ensembling Vs. Automated Ensembling Manual Ensembling Vs...
- Methods of Ensembling (Stacking, Mixture of Experts) Methods of Ensemblin...
- Bagging (Logic, Practical Applications) Bagging (Logic, Prac...
- Random forest (Logic, Practical Applications) Random forest (Logic...
- Boosting (Logic, Practical Applications) Boosting (Logic, Pra...
- Ada Boost Ada Boost
- Gradient Boosting Machines (GBM) XGBoost Gradient Boosting Ma...
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SUPERVISED LEARNING: SUPPORT VECTOR MACHINES
- Motivation for Support Vector Machine & Applications Motivation for Suppo...
- Support Vector Regression Support Vector Regre...
- Support vector classifier (Linear & Non-Linear) Support vector class...
- Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints) Mathematical Intuiti...
- Interpretation of Outputs and Fine tune the models with hyper parameters Interpretation of Ou...
- Validating SVM models Validating SVM model...
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SUPERVISED LEARNING: KNN
- What is KNN & Applications? What is KNN & A...
- KNN for missing treatment KNN for missing trea...
- KNN For solving regression problems KNN For solving regr...
- KNN for solving classification problems KNN for solving clas...
- Validating KNN model Validating KNN model
- Model fine tuning with hyper parameters Model fine tuning wi...
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SUPERVISED LEARNING: NAÏVE BAYES
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SEGMENTATION: SOLVING SEGMENTATION PROBLEMS
- Introduction to Segmentation Introduction to Segm...
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical) Types of Segmentatio...
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation) Heuristic Segmentati...
- Behavioral Segmentation Techniques (K-Means Cluster Analysis) Behavioral Segmentat...
- Cluster evaluation and profiling – Identify cluster characteristics Cluster evaluation a...
- Interpretation of results – Implementation on new data Interpretation of re...
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INTRODUCTION TO STATISTICS
- Basic Statistics – Measures of Central Tendencies and Variance Basic Statistics ...
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem Building blocks R...
- Inferential Statistics -Sampling – Concept of Hypothesis Testing Inferential Statisti...
- Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square 5. Important modules for statistical methods: Numpy, Scipy, Pandas Statistical Methods ...
- Important modules for statistical methods: Numpy, Scipy, Pandas Important modules fo...
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INTRODUCTION TO PREDICTIVE MODELING
- Concept of model in analytics and how it is used? Concept of model in ...
- Common terminology used in analytics & modeling process Common terminology u...
- Popular modeling algorithms Popular modeling alg...
- Types of Business problems – Mapping of Techniques Types of Business pr...
- Different Phases of Predictive Modeling Different Phases of ...
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