Course duration | 200 |
Classes | 24 |
Tools | R, Python, Tableau, Notebook, Excel |
Learning Mode | Classroom, online |
Fees | ₹ 25,000 |
Batch |
Who should do this course?
Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who are looking for building or switching their career to data science.
Prerequisites:
- Strong interest in data science.
- Good in maths.
- Strong background in statistics.
- Python/Java programming experience or understanding of programming concepts such as variables, functions, loops, and basic python data structures like lists and dictionaries etc.
- Understand data and know how to extracts insights from it.
Course
SCIENTIFIC DISTRIBUTIONS USED IN PYTHON FOR DATA SCIENCE
Python data wrangling and important packages
- Numpy, scify, pandas, scikitlearn, statmodels, nltk etc
- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects – subsetting, methods
- Exporting Data to various formats
- Important python modules: Pandas, beautifulsoup
- Cleansing Data with Python
- Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
- Python Built-in Functions (Text, numeric, date, utility functions)
- Python User Defined Functions
- Stripping out extraneous information
- Normalizing data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
- Data analysis and visualization
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
INTRODUCTION TO PREDICTIVE MODELING
Predictive modeling
- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Types of Business problems – Mapping of Techniques
- Different Phases of Predictive Modeling
Exploratory analytics
- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers data
- Visualize the data trends and patterns
- Need of Data preparation
- Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
- Variable Reduction Techniques – Factor & PCA Analysis
Classification
- Introduction to Segmentation
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
- Behavioral Segmentation Techniques (K-Means Cluster Analysis)
- Cluster evaluation and profiling – Identify cluster characteristics
- Interpretation of results – Implementation on new data
Regression
- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Assess the overall effectiveness of the model
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data
Logistic Regression
- Introduction – Applications
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- Building Logistic Regression Model (Binary Logistic Model)
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
- Interpretation of Results – Business Validation – Implementation on new data
TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS
Time series
- Introduction – Applications
- Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques(Pattern based – Pattern less)
- Basic Techniques – Averages, Smoothening, etc
- Advanced Techniques – AR Models, ARIMA, etc
- Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
Predictive modeling
- Introduction to Machine Learning & Predictive Modeling
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- Overfitting (Bias-Variance Trade off) & Performance Metrics
- Feature engineering & dimension reduction
- Concept of optimization & cost function
- Overview of gradient descent algorithm
- Overview of Cross validation (Bootstrapping, K-Fold validation etc)
- Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)
Unsupervised machine learning: Segmentation
- What is segmentation & Role of ML in Segmentation?
- Concept of Distance and related math background
- K-Means Clustering
- Expectation Maximization
- Hierarchical Clustering
- Spectral Clustering (DBSCAN)
- Principle component Analysis (PCA)
Supervised learning: Decision Tree
- Decision Trees – Introduction – Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
- Decision Trees – Validation
- Overfitting – Best Practices to avoid
Supervised learning: ensemble learning
- Concept of Ensembling
- Manual Ensembling Vs. Automated Ensembling
- Methods of Ensembling (Stacking, Mixture of Experts)
- Bagging (Logic, Practical Applications)
- Random forest (Logic, Practical Applications)
- Boosting (Logic, Practical Applications)
- Ada Boost
- Gradient Boosting Machines (GBM)
- XGBoost
Supervised learning: Artificial neural network
- Motivation for Neural Networks and Its Applications
- Perceptron and Single Layer Neural Network, and Hand Calculations
- Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
- Neural Networks for Regression
- Neural Networks for Classification
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating ANN models
Supervised learning: support vector machine
- Motivation for Support Vector Machine & Applications
- Support Vector Regression
- Support vector classifier (Linear & Non-Linear)
- Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating SVM models
Supervised learning: KNN
- hat is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters
Supervised learning: Naïve based
- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications
Text Mining:
- Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- Finding patterns in text: text mining, text as a graph
- Natural Language processing (NLP)
- Text Analytics – Sentiment Analysis using Python
- Text Analytics – Word cloud analysis using Python
- Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- Text Analytics – Classification (Spam/Not spam)
- Applications of Social Media Analytics
- Metrics (Measures Actions) in social media analytics
- Examples & Actionable Insights using Social Media Analytics
- Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
- Fine tuning the models using Hyper parameters, grid search, piping etc.
Case studies
Credit Card Customers Segmentation
A credit card company wishes to understand its customer behavior so to have an enriched customer profile by having intelligent KPI’s. The idea is to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.
Proactive Attrition Management
A wireless telecom companies wants to reduce customer churn by developing a proactive churn management model. The idea is to build a logistic regression based predictive model to develop an incentive plan for enticing would-be churners to remain with the company.
Predicting Loan Default
A bank would like to build credit risk model (application score card using PD models) to accept/ reject applications for loans. Also it wants to understand the key drivers for default or delinquency.
Key Drivers for Customer credit card spending
The objective of this case study is to understand what’s driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process and help set the credit limit and designing new product offerings.
Time Series Forecasting
Use time series analysis to forecast the outbound passenger movement for next few quarters.
Sentiment Analysis
Objective of this analysis is to obtain data from Twitter and check how the sentiment varies by country for a particular brand/keyword/company.
Social Media Analytics Case Study
Objective of this analysis is to obtain the data from social media platforms like Twitter/Facebook/Youtube etc and perform different analysis using text mining and Machine learning techniques