Data Science
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Statistics for Data Science - 10 Hours

Day	Session	Topics	Exam
01	Statistics for Data Science Part 1	Need of Statistics in Data Science Understanding the Statistics Terminologies Measures of Central Tendency in Data Measures of Dispersion Understanding Skewness in Data
02	Statistics for Data Science Part 2	Probability Concepts Conditional Probability Bayes’ Theorem	10 min 10 questions
03	Statistics for Data Science Part 3	Practice Problems on Bayes' Theorem Probability Distributions	10 min 10 questions
04	Statistics for Data Science Part 4	Confidence Interval Significance Level Hypothesis Testing	10 min 10 questions
05	Statistics for Data Science Part 5	Central Limit Theorem Introduction to Analysis of Variance - ANOVA	10 min 10 questions

Python for Data Science - 8 Hours

Day	Session	Topics	Exam
01	Python for Data Science Part 1	Python basics Data Structures in Python Control and Loop Statements in Python
02	Python for Data Science Part 2	Functions & Classes in Python Working with Data	10 min 10 questions
03	Python for Data Science Part 3	Pandas and NumPy Data Load from different sources like local directory, from URL link, JSON etc. (Import & Export) Indexing Selection and Filtering Sorting & Summarizing Descriptive Statistics	10 min 10 questions
04	Python for Data Science Part 4	Combining and Merging Data Frames Removing Duplicates Discretization and Binning String Manipulation	10 min 10 questions

Data Science – EDA, Visualization and Pre-processing – 10 Hours

Day	Session	Topics	Exam
01	Exploration Data Analysis with Python	What is EDA? Processes in EDA Handling Data Types Missing Values Outliers Detection
02	Visualization Part 1	Understanding different libraries and tools for visualization How to ask questions from data and visualize the result How to plot different graphs using python	10 min 10 questions
03	Visualization Part 2	Case Study and generating report	10 min 10 questions
04	Data Pre-processing Part 1	Different Operations done during data pre-processing Feature Engineering	10 min 10 questions
05	Data Pre-processing Part 2	Data Preparation for Machine Learning Models Splitting Strategies Sampling Techniques	10 min 10 questions

Machine Learning | ML in Depth – 32 Hours

Day	Session	Topics	Exam
01	Introduction to ML	Types: Supervised vs Un-supervised Parametric vs Non-Parametric ML life cycle – steps involved in Model building Performance Metrics using in ML Confusion Matrix (Statistical Concept)
02	Regression Analysis	Covariance and Correlation Coefficient of determination R-square Adjusted r-square Multicollinearity Linearity vs Non-Linearity	10 min 10 questions
03	Linear Regression	Types of Problems we deal in Linear Regression Assumptions of Linear Regression Best fit Line and its significance in Linear Regression How to draw best fit line Performance metrics SSE, MSAE, RMSE etc.	10 min 10 questions
04	Project in Linear Regression	Step by step approach to build models in linear regression	10 min 10 questions
05	Logistic Regression	Linear to Logistic Regression Transformation Mathematics behind the logistic regression What is log of odds Log-likelihood What is maximizing the log-likelihood	10 min 10 questions
06	Project in Logistic Regression	Step by step approach to build models in Logistic Regression	10 min 10 questions
07	Overfitting and Underfitting	Overfitting and Underfitting Techniques to overcome overfitting problem Bias-variance trade off Regularization	10 min 10 questions
08	KNN	K-nearest neighbour algorithm Techniques to select the optimal k value What is meant by neighbours in KNN	10 min 10 questions
09	Project in KNN	Project in KNN	10 min 10 questions
10	Decision Tree	What is Decision Tree and how to interpret it Decision Tree formation criteria Gini Index and Gini Impurity Entropy and Information Gain Criteria to split a node Tree pruning techniques	10 min 10 questions
11	Project using DT	Project using DT	10 min 10 questions
12	k-fold cross validation	k-fold cross validation Model Tuning	10 min 10 questions
13	SVM	What is Support vectors Machine Understanding support vectors Kernel concepts Hyperparameters in SVM	10 min 10 questions
14	Project using SVM	Project using SVM	10 min 10 questions
15	Ensemble Techniques and Random Forest	Bagging Boosting Random Forest	10 min 10 questions
16	Project using Random Forest	Applying Random Forest Applying Boosting techniques like adaboost and xgboost	10 min 10 questions

NLP in depth – 30 Hours

Day	Session	Topics	Exam
01	NLP Introduction	Types of problems solved using NLP Working with Text Regular Expressions for pattern searching in text File Parsers
02	Introducing NLP concepts and Techniques	Tokenization Lemmatization Stop Words Punctuations Phrase Matching Named Entity Recognition - NER Parts of Speech Tagging – POS Topic Modelling	10 min 10 questions
03	NLP Libraries Introduction	NLTK, spaCy, Etc. Installation and Environment setup	10 min 10 questions
04	Text Pre-processing using spaCy	Tokenization in detail Lemmatization Stop word removal Punctuation removal Phrase Matching using spaCy	10 min 10 questions
05	Parts of Speech Tagging and Named Entity Recognition	POS using spacy POS visualization NER using spaCy NER visualization	10 min 10 questions
06	Feature Extraction Techniques	Count Vectorizer Tf-idf vectorizer Word2vec	10 min 10 questions
07	Text classification Project	Text classification project using spacy and sckit-learn	10 min 10 questions
08	Semantics and sentiment analysis Project	Sentiment analysis using NLTK	10 min 10 questions
09	Topic Modelling	Overview of Topic Modelling Latent Dirchlet Allocation Overview LDA with Python Matrix Factorization Topic Modelling Project	10 min 10 questions
10	Deep Learning for NLP	Introduction to DL for NLP Basic Perceptron Model Neural Networks know-how Understanding keras	10 min 10 questions
11	RNN and LSTM	Overview of Recurrent Neural Network Intro to Text generation using LSTM	10 min 10 questions
12	Project- Text generation	Text generation using LSTM and Keras in python	10 min 10 questions
13	Chat Bot Implementation	Chat bot implementation from scratch	10 min 10 questions
14	Transformers	Introduction to Transformers Transformers based models	10 min 10 questions
15	Introduction to pre-trained models for NLP tasks	BERT and its variants GPT and its variant Huggingface AutoNLP AllenNLP	10 min 10 questions
16	Project using Random Forest	Applying Random Forest Applying Boosting techniques like adaboost and xgboost	10 min 10 questions