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Data Science Course Curriculum (Syllabus)

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Module 1: Python Core and Advanced



  • 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


  • 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


  • 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 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.

    Regular Expressions

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


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

    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.


    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

    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


    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.


    • 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


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    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


    • 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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