● Introduction to Data Science
○ Introduction to Data Science
○ Discussion on Course Curriculum
○ Introduction to Programming
● Python Basics
○ Introduction to Python: Installation and Running (Jupyter Notebook, .pyf
ile from terminal, Google Colab)
○ Data types and type conversion
○ Variables
○ Operators
○ Flow Control : If, Elif, Else
○ Loops
○ Python Identifier
○ Building Funtions (print, type, id, sys, len)
● Python – Data Types & Utilities
○ List, List of Lists and List Comprehension
○ List creation
○ Create a list with variable
○ List mutable concept
○ len() || append() || pop()
○ insert() || remove() || sort() || reverse()
○ Forward indexing
○ Backward Indexing
○ Forward slicing
○ Backward slicing
○ Step slicing
● Set
○ SET creation with variable
○ len() || add() || remove() || pop()
○ union() | intersection() || difference()
● Tuple
○ TUPLE Creation
○ Create Tuple with variable
○ Tuple Immutable concept
○ len() || count() || index()
○ Forward indexing
○ Backward Indexing
● Dictionary and Dictionary comprehension
○ create a dictionary using variable
○ keys:values concept
○ len() || keys() || values() || items()
○ get() || pop() || update()
○ comparision of datastructure
○ Introduce to range()
○ pass range() in the list
○ range() arguments
○ For loop introduction using range()
● Functions
○ Inbuilt vs User Defined
○ User Defined Function
○ Function Argument
○ Types of Function Arguments
○ Actual Argument
○ Global variable vs Local variable
○ Anonymous Function | LAMBDA
● Packages
● Map Reduce
● OOPs
● Class & Object
○ what is mean by inbuild class
○ how to creat user class
○ crate a class & object
○ __init__ method
○ python constructor
○ constructor, self & comparing objects
○ instane variable & class variable
● Methods
○ what is instance method
○ what is class method
○ what is static method
○ Accessor & Mutator
● Python DECORATOR
○ how to use decorator
○ inner class, outerclass
○ Inheritence
● Polymorphism
○ duck typing
○ operator overloading
○ method overloading
○ method overridding
○ Magic method
○ Abstract class & Abstract method
○ Iterator
○ Generators in python
● Python – Production Level
○ Error / Exception Handling
○ File Handling
○ Docstrings
○ Modularization
● Pickling & Unpickling
● Pandas
○ Introduction, Fundamentals, Importing Pandas, Aliasing, DataFrame
○ Series – Intro, Creating Series Object, Empty Series Object, Create series
from List/Array/Column from DataFrame, Index in Series, Accessing
values in Series
○ NaN Value
○ Series – Attributes (Values, index, dtypes, size)
○ Series – Methods – head(), tail(), sum(), count(), nunique() etc.,
○ Date Frame
○ Loading Different Files
○ Data Frame Attributes
○ Data Frame Methods
○ Rename Column & Index
○ Inplace Parameter
○ Handling missing or NaN values
○ iLoc and Loc
○ Data Frame – Filtering
○ Data Frame – Sorting
○ Data Frame – GroupBy
○ Merging or Joining
○ Data Frame – Concat
○ DataFrame – Adding, dropping columns & rows
○ DataFrame – Date and time
○ DataFrame – Concatenate Multiple csv files
● Numpy
○ Introduction, Installation, pip command, import numpy package, Module
Not Found Error, Famous Alias name to Numpy
○ Fundamentals – Create Numpy Array, Array Manipulation, Mathematical
Operations, Indexing & Slicing
○ Numpy Attributes
○ Important Methods- min(),max(), sum(), reshape(), count_nonzero(),
sort(), flatten() etc.,
○ adding value to array of values
○ Diagonal of a Matrix
○ Trace of a Matrix
○ Parsing, Adding and Subtracting Matrices
○ “Statistical Functions: numpy.mean()
○ numpy.median()
○ numpy.std()
○ numpy.sum()
○ numpy.min()”
○ Filter in Numpy
● Matplotlib
○ Introduction
○ Pyplot
○ Figure Class
○ Axes Class
○ Setting Limits and Tick Labels
○ Multiple Plots
○ Legend
○ Different Types of Plots
○ Line Graph
○ Bar Chart
○ Histograms
○ Scatter Plot
○ Pie Chart
○ 3D Plots
○ Working with Images
○ Customizing Plots
● Seaborn
○ catplot() function
○ stripplot() function
○ boxplot() function
○ violinplot() function
○ pointplot() function
○ barplot() function
○ Visualizing statistical relationship with Seaborn relplot() function
○ scatterplot() function
○ regplot() function
○ lmplot() function
○ Seaborn Facetgrid() function
○ Multi-plot grids
○ Statistical Plots
○ Color Palettes
○ Faceting
○ Regression Plots
○ Distribution Plots
○ Categorical Plots
○ Pair Plots
● Scipy
○ Signal and Image Processing (scipy.signal, scipy.ndimage):
○ Linear Algebra (scipy.linalg)
○ Integration (scipy.integrate)
○ Statistics (scipy.stats)
○ Spatial Distance and Clustering (scipy.spatial)
● Statsmodels
○ Linear Regression (statsmodels.regression)
○ Time Series Analysis (statsmodels.tsa)
○ Statistical Tests (statsmodels.stats)
○ Anova (statsmodels.stats.anova)
○ Datasets (statsmodels.datasets)

● Set Theory
○ Data Representation & Database Operations
● Combinatorics
○ Feature Selection
○ Permutations and Combinations for Sampling
○ Hyper parameter Tuning
○ Experiment Design
○ Data Partitioning and Cross-Validation
● Probability
○ Basics
○ Theoretical Probability
○ Empirical Probability
○ Addition Rule
○ Multiplication Rule
○ Conditional Probability
○ Total Probability
○ Probability Decision Tree
○ Bayes Theorem
○ Sensitivity & Specificity in Probability
○ • Bernouli Naïve Bayes, Gausian Naïve Bayes, Multinomial Naïve Bayes
● Distributions
○ Binomial, Poisson, Normal Distribution, Standard Normal Distribution
○ Gaussian Distribution, Uniform Distribution
○ Z Score
○ Skewness
○ Kurtosis
○ Geometric Distribution
○ Hypergeometric Distribution
○ Markov Chain
● Linear Algebra
○ Linear Equations
○ Matrices(Matrix Algebra: Vector Matrix Vector matrix multiplication
Matrix matrix multiplication)
○ Determinant
○ Eigen Value and Eigenvector
● Euclidean Distance & Manhattan Distance
● Calculus
○ Differentiation
○ Partial Differentiation
○ Max & Min
● Indices & Logarithms

● Introduction
○ Population & Sample
○ Reference & Sampling technique
● Types of Data
○ Qualitative or Categorical – Nominal & Ordinal
○ Quantitative or Numerical – Discrete & Continuous
○ Cross Sectional Data & Time Series Data
● Measures of Central Tendency
○ Mean, Mode & Median – Their frequency distribution
● Descriptive statistic Measures of symmetry
○ skewness (positive skew, negative skew, zero skew)
○ kurtosis (Leptokurtic, Mesokurtic, Platykurtic)
● Measurement of Spread
○ Range, Variance, Standard Deviation
● Measures of variability
○ Interquartile Range (IQR)
○ Mean Absolute Deviation (MAD)
○ Coefficient of variation
○ Covariance
● Levels of Data Measurement
○ Nominal, Ordinal, Interval, Ratio
● Variable
○ Types of Variables.
○ Categorical Variables – Nominal variable & ordinal variables
○ Numerical Variables: discrete & continuous
○ Dependent Variable
○ Independent Variable
○ Control Moderating & Mediating
● Frequency Distribution Table
○ Nominal, Ordinal, Interval, Ratio
● Types of Variables
○ Categorical Variables – Nominal variable & ordinal variables
○ Numerical Variables: discrete & continuous
○ Dependent Variable
○ Independent Variable
○ Control Moderating & Mediating
● Frequency Distribution Table
○ Relative Frequency, Cumulative Frequency
○ Histogram
○ Scatter Plots
○ Range
○ Calculate Class Width
○ Create Intervals
○ Count Frequencies
○ Construct the Table
● Correlation, Regression & Collinearity
○ Pearson & Spearman Correlation Methods
○ Regression Error Metrics
● Others
○ Percentiles, Quartiles, Interquartile Range
○ Different types of Plots for Continuous, Categorical variable
○ Box Plot, Outliers
○ Confidence Intervals
○ Central Limit Theorem
○ Degree of freedom
● Bias and Variance in ML
● Entropy in ML
● Information Gain
● Surprise in ML
● Loss Function & Cost Function
○ Mean Squared Error, Mean Absolute Error – Loss Function
○ Huber Loss Function
○ Cross Entropy Loss Function
● Inferential Statistics
○ Hypothesis Testing: One tail, two tail and p-value
○ Formulation of Null & Alternative Hypothesis
○ Type-I error & Type-II error
○ Statistical Tests
○ Sample Test
○ ANOVA Test
○ Chi-square Test
○ Z-Test & T-Test

● Introduction
○ DBMS vs RDBMS
○ Intro to SQL
○ SQL vs NoSQL
○ MySQL Installation
● Keys
○ Primary Key
○ Foreign Key
● Constraints
○ Unique
○ Not NULL
○ Check
○ Default
○ Auto Increment
● CRUD Operations
○ Create
○ Retrieve
○ Update
○ Delete
● SQL Languages
○ Data Definition Language (DDL)
○ Data Query Language
○ Data Manipulation Language (DML)
○ Data Control Language
○ Transaction Control Language
● SQL Commands
○ Create
○ Insert
○ Alter, Modify, Rename, Update
○ Delete, Truncate, Drop
○ Grant, Revoke
○ Commit, Rollback
○ Select
● SQL Clause
○ Where
○ Distinct
○ OrderBy
○ GroupBy
○ Having
○ Limit
● Operators
○ Comparison Operators
○ Logical Operators
○ Membership Operators
○ Identity Operators
● Wild Cards

● Aggregate Functions
● SQL Joins
○ Inner Join & Outer Join
○ Left Join & Right Join
○ Self & Cross Join
○ Natural Join

● EDA
○ Univariate Analysis
○ Bivariate Analysis
○ Multivariate Analysis
● Data Visualisation
○ Various Plots on different data types
○ Plots for Continuous Variables
○ Plots for Discrete Variables
○ Plots for Time Series Variables
● ML Introduction
○ What is Machine Learning?
○ Types of Machine Learning Methods
○ Classification problem in general
○ Validation Techniques: CV,OOB
○ Different types of metrics for Classification
○ Curse of dimensionality
○ Feature Transformations
○ Feature Selection
○ Imbalanced Dataset and its effect on Classification
○ Bias Variance Tradeoff
● Important Element of Machine Learning
● Multiclass Classification
○ One-vs-All
○ Overfitting and Underfitting
○ Error Measures
○ PCA learning
○ Statistical learning approaches
○ Introduce to SKLEARN FRAMEWORK
● Data Processing
○ Creating training and test sets, Data scaling and Normalisation
○ Feature Engineering – Adding new features as per requirement,
Modifying the data
○ Data Cleaning – Treating the missing values, Outliers
○ Data Wrangling – Encoding, Feature Transformations, Feature Scaling
○ Feature Selection – Filter Methods, Wrapper Methods, Embedded
Methods
○ Dimension Reduction – Principal Component Analysis (Sparse PCA &
Kernel PCA), Singular Value Decomposition
○ Non Negative Matrix Factorization
● Regression
○ Introduction to Regression
○ Mathematics involved in Regression
○ Regression Algorithms
○ Simple Linear Regression
○ Multiple Linear Regression
○ Polynomial Regression
○ Lasso Regression
○ Ridge Regression
○ Elastic Net Regression
● Evaluation Metrics for Regression
○ Mean Absolute Error (MAE)
○ Mean Squared Error (MSE)
○ Root Mean Squared Error (RMSE)
○ R²
○ Adjusted R²
● Classification
○ Introduction
○ K-Nearest Neighbors
○ Logistic Regression
○ Support Vector Machines (Linear SVM)
○ Linear Classification
○ Kernel-based classification
○ Non-linear examples
○ 2 features forms straight line & 3 features forms plane
○ Hyperplane and Support vectors
○ Controlled support vector machines
○ Support vector Regression
○ Kernel SVM (Non-Linear SVM)
○ Naives Bayes
○ Decision Trees
○ Random Forest / Bagging
○ Ada Boost
○ Gradient Boost
○ XG Boost
○ Evaluation Metrics for Classification
● Clustering
● Introduction
● K-Means Clustering
○ Finding the optimal number of clusters
○ Optimizing the inertia
○ Cluster instability
○ Elbow method
● Hierarchical Clustering
● Agglomerative clustering
● DBSCAN Clustering
● Association Rules
○ Market Basket Analysis
○ Apriori Algorithm
● Recommendation Engines
○ Collaborative Filtering
○ User based collaborative filtering
○ Item based collaborative filtering
○ Recommendation Engines
● Time Series & Forecasting
○ What is Time series data
○ Different components of time series data
○ Stationary of time series data
○ ACF, PACF
○ Time Series Models
○ AR
○ ARMA
○ ARIMA
○ SARIMAX
● Model Selection & Evaluation
● Over Fitting & Under Fitting
○ Bianca-Variance Tradeoff
○ Hyper Parameter Tuning
○ Joblib And Pickling
● Others
○ Dummy Variable, One Hot Encoding
○ gridsearchcv vs randomizedsearchcv
● ML Pipeline
● ML Model Deployment in Flask

● Introduction
○ Power BI for Data scientist
○ Types of reports
○ Data source types
○ Installation
● Basic Report Design
○ Data sources and Visual types
○ Canvas and fields
○ Table and Tree map
○ Format button and Data Labels
○ Legend,Category and Grid
○ CSV and PDF Exports
● Visual Sync, Grouping
○ Slicer visual
○ Orientation, selection process
○ Slicer: Number, Text, slicer list
○ Bin count,Binning
● Hierarchies, Filters
○ Creating Hierarchies
○ Drill Down options
○ Expand and show
○ Visual filter,Page filter,Report filter
○ Drill Thru Reports
● Power Query
○ Power Query transformation
○ Table and Column Transformations
○ Text and time transformations
○ Power query functions
○ Merge and append transformations
● DAX Functions
○ DAX Architecture,Entity Sets
○ DAX Data types,Syntax Rules
○ DAX measures and calculations
○ Creating measures
○ Creating Columns

● Deep learning at Glance
○ Introduction to Neural Network
○ Biological and Artificial Neuron
○ Introduction to perceptron
○ Perceptron and its learning rule and drawbacks
○ Multilayer Perceptron, loss function
○ Neural Network Activation function
● Training MLP: Backpropagation
● Cost Function
● Gradient Descent Backpropagation – Vanishing and Exploding Gradient Problem
● Introduce to Py-torch
● Regularization
● Optimizers
● Hyperparameters and tuning of the same
● TENSORFLOW FRAMEWORK
○ Introduction to TensorFlow
○ TensorFlow Basic Syntax
○ TensorFlow Graphs
○ Variables and Placeholders
○ TensorFlow Playground
● ANN (Artificial Neural Network)
○ ANN Architecture
○ Forward & Backward Propagation, Epoch
○ Introduction to TensorFlow, Keras
○ Vanishing Gradient Descend
○ Fine-tuning neural network hyperparameter
○ Number of hidden layers, Number of neurons per hidden layer
○ Activation function
○ INSTALLATION OF YOLO V8, KERAS, THEANO
● PY-TORCH Library
● RNN (Recurrent Neural Network)
○ Introduction to RNN
○ Backpropagation through time
○ Input and output sequences
○ RNN vs ANN
○ LSTM (Long Short-Term Memory)
○ Different types of RNN: LSTM, GRU
○ Bidirectional RNN
○ Sequential-to-sequential architecture (Encoder Decoder)
○ BERT Transformers
○ Text generation and classification using Deep Learning
○ Generative-AI (Chat-GPT)
● Basics of Image Processing
○ Histogram of images
○ Basic filters applied on the images
● Convolutional Neural Networks (CNN)
○ ImageNet Dataset
○ Project: Image Classification
○ Different types of CNN architectures
○ Recurrent Neural Network (RNN)
○ Using pre-trained model: Transfer Learning

● Natural Language Processing (NLP)
○ Text Cleaning
○ Texts, Tokens
○ Basic text classification based on Bag of Words
● Document Vectorization
○ Bag of Words
○ TF-IDF Vectorizer
○ n-gram: Unigran, Bigram
○ Word vectorizer basics, One Hot Encoding
○ Countvectorizer
○ Word cloud and gensim
○ Word2Vec and Glove
○ Text classification using Word2Vec and Glove
○ Parts of Speech Tagging (PoS Tagging or POST)
○ Topic Modelling using LDA
○ Sentiment Analysis
● Twitter Sentiment Analysis Using Textblob
○ TextBlob
○ Installing textblob library
○ Simple TextBlob Sentiment Analysis Example
○ Using NLTK’s Twitter Corpus
● Spacy Library
○ Introduction, What is a Token, Tokenization
○ Stop words in spacy library
○ Stemming
○ Lemmatization
○ Lemmatization through NLTK
○ Lemmatization using spacy
○ Word Frequency Analysis
○ Counter
○ Part of Speech, Part of Speech Tagging
○ Pos by using spacy and nltk
○ Dependency Parsing
○ Named Entity Recognition(NER)
○ NER with NLTK
○ NER with spacy

● Human vision vs Computer vision
○ CNN Architecture
○ Convolution – Max Pooling – Flatten Layer – Fully Connected Layer
○ CNN Architecture
○ Striding and padding
○ Max pooling
○ Data Augmentation
○ Introduction to OpenCV & YoloV3 Algorithm
● Image Processing with OpenCV
○ Image basics with OpenCV
○ Opening Image Files with OpenCV
○ Drawing on Images, Image files with OpenCV
○ Face Detection with OpenCV
● Video Processing with OpenCV
○ Introduction to Video Basics, Object Detection
○ Object Detection with OpenCV
● Reinforcement Learning
○ Introduction to Reinforcement Learning
○ Architecture of Reinforcement Learning
○ Reinforcement Learning with Open AI
○ Policy Gradient Theory
● Open AI
○ Introduction to Open AI
○ Generative AI
○ Chat Gpt (3.5)
○ LLM (Large Language Model)
○ Classification Tasks with Generative AI
○ Content Generation and Summarization with Generative AI
○ Information Retrieval and Synthesis workflow with Gen AI
● Time Series and Forecasting
○ Time Series Forecasting using Deep Learning
○ Seasonal-Trend decomposition using LOESS (STL) models.
○ Bayesian time series analysis
● MakerSuite Google
○ PaLM API
○ MUM models
● Azure ML