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### Data Science Course Curriculum (Syllabus)

##### Your Title Goes Here

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##### Module 1: Python Core and Advanced

INTRODUCTION

- What is Python?
- Why does Data Science require Python?
- Installation of Anaconda
- Understanding Jupyter Notebook
- Basic commands in Jupyter Notebook
- Understanding Python Syntax

Data Types and Data Structures

- Variables and Strings
- Lists, Sets, Tuples, and Dictionaries

Control Flow and Conditional Statements

- Conditional Operators, Arithmetic Operators, and Logical Operators
- If, Elif and Else Statements
- While Loops
- For Loops
- Nested Loops and List and Dictionary Comprehensions

Functions

- What is function and types of functions
- Code optimization and argument functions
- Scope
- Lambda Functions
- Map, Filter, and Reduce

File Handling

- Create, Read, Write files and Operations in File Handling
- Errors and Exception Handling

Class and Objects

- Create a class
- Create an object
- The __init__()
- Modifying Objects
- Object Methods
- Self
- Modify the Object Properties
- Delete Object
- Pass Statements

##### Module 2: Exploratory Data Analysis using Python

**Numpy – NUMERICAL PYTHON**

- Introduction to Array
- Creation and Printing of an array
- Basic Operations in Numpy
- Indexing
- Mathematical Functions of Numpy

**2. Data Manipulation with Pandas**

- Series and DataFrames
- Data Importing and Exporting through Excel, CSV Files
- Data Understanding Operations
- Indexing and slicing and More filtering with Conditional Slicing
- Group by, Pivot table, and Cross Tab
- Concatenating and Merging Joining
- Descriptive Statistics
- Removing Duplicates
- String Manipulation
- Missing Data Handling

**DATA VISUALIZATION**

**Data Visualization using Matplotlib and Pandas**

- Introduction to Matplotlib
- Basic Plotting
- Properties of plotting
- About Subplots
- Line plots
- Pie chart and Bar Graph
- Histograms
- Box and Violin Plots
- Scatterplot

**Case Study on Exploratory Data Analysis (EDA) ****and Visualizations**

- What is EDA?
- Uni – Variate Analysis
- Bi-Variate Analysis
- More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.

**UNSTRUCTURED DATA PROCESSING**

**Regular Expressions**

- Structured Data and Unstructured Data
- Literals and Meta Characters
- How to Regular Expressions using Pandas?
- Inbuilt Methods
- Pattern Matching

**PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS**

- Data Mining (WEB – SCRAPING)

This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.- Data Collection
- Data Mining
- Data Preprocessing
- Data Visualization

Ex: Text, CSV, TSV, Excel Files, Matrices, Images

##### Module 3: Advanced Statistics

**Data Types and Data Structures**

- Statistics in Data science:
- What is Statistics?
- How is Statistics used in Data Science?
- Population and Sample
- Parameter and Statistic
- Variable and its types

**Data Gathering Techniques**

- Data types
- Data Collection Techniques
- Sampling Techniques:
- Convenience Sampling, Simple Random Sampling
- Stratified Sampling, Systematic Sampling, and Cluster Sampling

**Descriptive Statistics**

- What is Univariate and Bi Variate Analysis?
- Measures of Central Tendencies
- Measures of Dispersion
- Skewness and Kurtosis
- Box Plots and Outliers detection
- Covariance and Correlation

**Probability Distribution**

- Probability and Limitations
- Discrete Probability Distributions
- Bernoulli, Binomial Distribution, Poisson Distribution
- Continuous Probability Distributions
- Normal Distribution, Standard Normal Distribution

**Inferential Statistics**

- Sampling variability and Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Z-test, T-test
- Chi-Square Test
- F-Test and ANOVA

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##### Module 4. SQL for Data Analysis

**Introduction to Databases**

- Basics of SQL
- DML, DDL, DCL, and Data Types
- Common SQL commands using SELECT, FROM, and WHERE
- Logical Operators in SQL

- SQL Joins
- INNER and OUTER joins to combine data from multiple tables
- RIGHT, LEFT joins to combine data from multiple tables

- Filtering and Sorting
- Advanced filtering using IN, OR, and NOT
- Sorting with GROUP BY and ORDER BY

- SQL Aggregations
- Common Aggregations including COUNT, SUM, MIN, and MAX
- CASE and DATE functions as well as work with NULL values

- Subqueries and Temp Tables
- Subqueries to run multiple queries together
- Temp tables to access a table with more than one query

- SQL Data Cleaning
- Perform Data Cleaning using SQL

##### Module 5: Machine Learning Supervised Learning

**INTRODUCTION**

- What Is Machine Learning?
- Supervised Versus Unsupervised Learning
- Regression Versus Classification Problems Assessing Model Accuracy

###### REGRESSION TECHNIQUES

**Linear Regression**

- Simple Linear Regression:
- Estimating the Coefficients
- Assessing the Coefficient Estimates
- R Squared and Adjusted R Squared
- MSE and RMSE

**Multiple Linear Regression**

- Estimating the Regression Coefficients
- OLS Assumptions
- Multicollinearity
- Feature Selection
- Gradient Descent

**Evaluating the Metrics of Regression Techniques**

- Homoscedasticity and Heteroscedasticity of error terms
- Residual Analysis
- Q-Q Plot
- Cook’s distance and Shapiro-Wilk Test
- Identifying the line of best fit
- Other Considerations in the Regression Model
- Qualitative Predictors
- Interaction Terms
- Non-linear Transformations of the Predictors

**Polynomial Regression**

- Why Polynomial Regression
- Creating polynomial linear regression
- Evaluating the metrics

**Regularization Techniques**

- Lasso Regularization
- Ridge Regularization
- ElasticNet Regularization
**Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python**

**CAPSTONE PROJECT: ** A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

**CLASSIFICATION TECHNIQUES**

**Logistic regression**

- An Overview of Classification
- Difference Between Regression and classification Models.
- Why Not Linear Regression?
- Logistic Regression:
- The Logistic Model
- Estimating the Regression Coefficients and Making Predictions
- Logit and Sigmoid functions
- Setting the threshold and understanding decision boundary
- Logistic Regression for >2 Response Classes
- Evaluation Metrics for Classification Models:
- Confusion Matrix
- Accuracy and Error rate
- TPR and FPR
- Precision and Recall, F1 Score
- AUC-ROC
- Kappa Score

**Naive Bayes**

- Principle of Naive Bayes Classifier
- Bayes Theorem
- Terminology in Naive Bayes
- Posterior probability
- Prior probability of class
- Likelihood
- Types of Naive Bayes Classifier
- Multinomial Naive Bayes
- Bernoulli Naive Bayes and Gaussian Naive Bayes

**TREE BASED MODULES**

**Decision Trees**

- Decision Trees (Rule-Based Learning):
- Basic Terminology in Decision Tree
- Root Node and Terminal Node
- Regression Trees and Classification Trees
- Trees Versus Linear Models
- Advantages and Disadvantages of Trees
- Gini Index
- Overfitting and Pruning
- Stopping Criteria
- Accuracy Estimation using Decision Trees

**Case Study: **A Case Study on Decision Tree using Python

- Resampling Methods:
- Cross-Validation
- The Validation Set Approach Leave-One-Out Cross-Validation
- K-Fold Cross-Validation
- Bias-Variance Trade-O for K-Fold Cross-Validation

**Ensemble Methods in Tree-Based Models**

- What is Ensemble Learning?
- What is Bootstrap Aggregation Classifiers and how does it work?

**Random Forest**

- What is it and how does it work?
- Variable selection using Random Forest

**Boosting: AdaBoost, Gradient Boosting**

- What is it and how does it work?
- Hyper parameter and Pro’s and Con’s

**Case Study:** Ensemble Methods – Random Forest Techniques using Python

###### DISTANCE BASED MODULES

**K Nearest Neighbors**

- K-Nearest Neighbor Algorithm
- Eager Vs Lazy learners
- How does the KNN algorithm work?
- How do you decide the number of neighbors in KNN?
- Curse of Dimensionality
- Pros and Cons of KNN
- How to improve KNN performance

**Case Study:** A Case Study on KNN using Python

**Support Vector Machines**

- The Maximal Margin Classifier
- HyperPlane
- Support Vector Classifiers and Support Vector Machines
- Hard and Soft Margin Classification
- Classification with Non-linear Decision Boundaries
- Kernel Trick
- Polynomial and Radial
- Tuning Hyper parameters for SVM
- Gamma, Cost, and Epsilon
- SVMs with More than Two Classes

**Case Study:** A Case Study on SVM using Python

**CAPSTONE PROJECT:** A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

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##### Module 6: Machine Learning Unsupervised Learning

- Why Unsupervised Learning
- How it Different from Supervised Learning
- The Challenges of Unsupervised Learning

**Principal Components Analysis**

- Introduction to Dimensionality Reduction and its necessity
- What Are Principal Components?
- Demonstration of 2D PCA and 3D PCA
- Eigen Values, EigenVectors, and Orthogonality
- Transforming Eigen values into a new data set
- Proportion of variance explained in PCA

**Case Study:** A Case Study on PCA using Python

**K-Means Clustering**

- Centroids and Medoids
- Deciding the optimal value of ‘K’ using Elbow Method
- Linkage Methods

**Hierarchical Clustering**

- Divisive and Agglomerative Clustering
- Dendrograms and their interpretation
- Applications of Clustering
- Practical Issues in Clustering

**Case Study:** A Case Study on clusterings using Python

**CAPSTONE PROJECT:** A project on a use case will challenge Data Understanding, EDA, Data Processing, and Unsupervised algorithms.

**RECOMMENDATION SYSTEMS**

- What are recommendation engines?
- How does a recommendation engine work?
- Data collection
- Data storage
- Filtering the data
- Content-based filtering
- Collaborative filtering
- Cold start problem
- Matrix factorization
- Building a recommendation engine using matrix factorization
- Case Study

##### Module 7: Artificial Intelligence and Deep Learning

**Introduction to Neural Networks**

- Introduction to Perceptron & History of Neural networks
- Activation functions
- a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh

- Gradient Descent
- Learning Rate and tuning
- Optimization functions
- Introduction to Tensorflow
- Introduction to Keras
- Backpropagation and chain rule
- Fully connected layer
- Cross entropy
- Weight Initialization
- Regularization

**TensorFlow 2.0**

- Introducing Google Colab
- Tensorflow basic syntax
- Tensorflow Graphs
- Tensorboard

**Artificial Neural Network with Tensorflow**

- Neural Network for Regression
- Neural Network for Classification
- Evaluating the ANN
- Improving and tuning the ANN
- Saving and Restoring Graphs

##### Module 8: Computer Vision (CV)

**Working with images & CNN Building Blocks**

- Working with Images_Introduction
- Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
- Sampling, and Quantization
- Working with images – Filtering
- Hands-on Python Demo: Working with images
- Introduction to Convolutions
- 2D convolutions for Images
- Convolution – Backward
- Transposed Convolution and Fully Connected Layer as a Convolution
- Pooling: Max Pooling and Other pooling options

**CNN Architectures and Transfer Learning**

- CNN Architectures and LeNet Case Study
**Case Study:**AlexNet**Case Study:**ZFNet and VGGNet**Case Study:**GoogleNet**Case Study:**ResNet- GPU vs CPU
- Transfer Learning Principles and Practice
- Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
- Transfer learning Visualization (run package, occlusion experiment)
- Hands-on demo T-SNE

**Object Detection**

- CNN’s at Work – Object Detection with region proposals
- CNN’s at Work – Object Detection with Yolo and SSD
- Hands-on demo- Bounding box regressor
- #Need to do a semantic segmentation project

**CNN’s at Work – Semantic Segmentation**

- CNNs at Work – Semantic Segmentation
- Semantic Segmentation process
- U-Net Architecture for Semantic Segmentation
- Hands-on demo – Semantic Segmentation using U-Net
- Other variants of Convolutions
- Inception and MobileNet models

**CNN’s at work- Siamese Network for Metric Learning**

- Metric Learning
- Siamese Network as metric learning
- How to train a Neural Network in Siamese way
- Hands-on demo – Siamese Network

##### Module 9: Natural Language Processing (NLP)

**Introduction to Statistical NLP Techniques**

- Introduction to NLP
- Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
- Preprocessing in NLP Bag of words, TF-IDF as features
- Language model probabilistic models, n-gram model, and channel model
- Hands-on NLTK

**Word Embedding**

- Word2vec
- Golve
- POS Tagger
- Named Entity Recognition(NER)
- POS with NLTK
- TF-IDF with NLTK

**Sequential Models**

- Introduction to sequential models
- Introduction to RNN
- Introduction to LSTM
- LSTM forward pass
- LSTM backdrop through time
- Hands-on Keras LSTM

**Applications**

- Sentiment Analysis
- Sentence generation
- Machine translation
- Advanced LSTM structures
- Keras – machine translation
- ChatBot

##### Module 10: Power BI

### Introduction To Power Bi

What is Business Intelligence?

- Power BI Introduction
- Quadrant report
- Comparison with other BI tools
- Power BI Desktop overview
- Power BI workflow
- Installation query addressal
Data Import And Visualizations

- Data import options in Power BI
- Import from Web (hands-on)
- Why Visualization?
- Visualization types
Data Visualization (Contd.)

Categorical data visualization

Visuals for Filtering

Slicer details and use

Formatting visuals

KPI visuals

Tables and Matix

Power Queries

- Power Query Introduction
- Data Transformation – its benefits
- Queries panel
- M Language briefing
- Power BI Datatypes
- Changing Datatypes of columns
Power Queries (Cond.)

- Filtering
- Inbuilt column Transformations
- Inbuilt row Transformations
- Combine Queries
- Merge Queries
Power Pivot And Introduction To Dax

- Power Pivot
- Intro to Data Modelling
- Relationship and Cardinality
- Relationship view
- Calculated Columns vs Measures
- DAX Introduction and Syntax

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