Technologies You Will Learn
Python
NumPy
Pandas
Matplotlib
Scikit-learn
TensorFlow
Keras
PyTorch
OpenCV
FastAPI
Jupyter
Git
Curriculum
Data Science, ML & AI Curriculum
01
Introduction to Python and Development Environment
11 Topics
Month 1
Introduction to programming and Python
Applications of Python in Data Science, Machine Learning and AI
Python installation and environment setup
Python interpreter and execution modes
IDEs: VS Code, PyCharm and Jupyter Notebook
Python syntax, indentation and comments
Variables and naming conventions
Keywords and identifiers
Input and output using input() and print()
Type conversion and type checking
Coding standards and PEP 8 basics
02
Python Data Types and Operators
10 Topics
Month 1
Numeric data types: int, float and complex
Boolean and string data types
Type conversion and type casting
Arithmetic operators
Comparison and logical operators
Assignment operators
Membership and identity operators
Operator precedence
Mutable and immutable objects
Type checking using type() and isinstance()
03
Conditional Statements and Loops
10 Topics
Month 1
if, if-else and nested if statements
if-elif-else statements
for loops and while loops
range() function
break, continue and pass statements
Nested loops
Number-based programming problems
Pattern-printing programs
Practical problems using conditions and loops
Common Python interview programs
04
Python Strings and Data Structures
13 Topics
Month 1
String indexing and slicing
String methods and string formatting
String immutability
Lists and list operations
List methods and list comprehensions
Tuples and tuple operations
Sets and set operations
Dictionaries and dictionary methods
Nested lists, tuples and dictionaries
Iterating through collections
Mutable vs immutable data structures
Choosing appropriate data structures
Common string and collection interview problems
05
Functions and Modular Programming
12 Topics
Month 1
Defining and calling functions
Parameters, arguments and return values
Positional, keyword and default arguments
Variable-length arguments: *args and **kwargs
Local, global and nonlocal scope
Lambda functions
map(), filter() and zip()
Recursion fundamentals
Modules and packages
Built-in modules: math, random and datetime
Creating reusable Python modules
Common function-based interview programs
06
Object-Oriented Programming in Python
12 Topics
Month 1
Classes and objects
Constructors and the self parameter
Instance and class variables
Instance, class and static methods
Encapsulation
Inheritance and its types
Polymorphism
Method overriding
Abstraction and abstract classes
Special methods and operator overloading basics
Composition and aggregation fundamentals
Common OOP interview questions and programs
07
Exception Handling, File Handling and Python Essentials
16 Topics
Month 1
Syntax errors and runtime exceptions
try, except, else and finally
Raising exceptions using raise
Creating custom exceptions
Reading and writing text files
CSV and JSON file handling
Working with file paths
Context managers and the with statement
Iterators and iterables
Generators and yield
Regular expressions using re
List, set and dictionary comprehensions
Virtual environments and pip
Installing and managing Python libraries
Debugging and logging basics
Git and GitHub fundamentals
08
Python Problem Solving and Interview Preparation
13 Topics
Month 1
Number-based programming questions
Prime numbers and palindrome checking
Factorial and Fibonacci series
Armstrong numbers and digit manipulation
String reversal and palindrome checking
Character frequency and duplicate characters
List sorting and duplicate removal
Finding minimum, maximum and second-largest elements
Frequency counting using dictionaries
Basic searching and sorting algorithms
Time and space complexity fundamentals
Python output-based interview questions
Common Python interview questions for freshers
09
Mathematics for Data Science and Machine Learning
12 Topics
Month 2
Fractions, percentages, ratios and proportions
Basic algebra and mathematical expressions
Functions and graphs
Coordinate systems and slope
Vectors and vector operations
Matrices and matrix operations
Matrix multiplication and transpose
Dot products and their applications
Distance measures and similarity
Derivatives and gradients: conceptual introduction
Gradient descent: basic mathematical intuition
Mathematical concepts used in common ML algorithms
10
Probability and Statistics
17 Topics
Month 2
Population and sample
Types of data and measurement scales
Mean, median and mode
Range, variance and standard deviation
Percentiles, quartiles and interquartile range
Probability fundamentals
Conditional probability
Bayes' theorem
Independent and dependent events
Random variables
Normal, binomial and uniform distributions
Sampling techniques and sampling bias
Correlation and covariance
Outlier detection using statistical methods
Basic hypothesis testing
P-values and confidence intervals: introduction
Statistics interview questions for freshers
11
NumPy for Numerical Computing
16 Topics
Month 2
Introduction to NumPy
Creating NumPy arrays
Array dimensions, shape and data types
Indexing and slicing
Reshaping and flattening arrays
Array arithmetic and broadcasting
Vectorization
Aggregation functions
Statistical functions
Boolean masking and filtering
Sorting and searching arrays
Random number generation
Matrix operations and linear algebra basics
Handling missing and invalid numerical values
Python lists vs NumPy arrays
Common NumPy interview questions
12
Pandas for Data Analysis
21 Topics
Month 2
Introduction to Series and DataFrames
Creating DataFrames from dictionaries, lists and files
Reading CSV, Excel and JSON files
Inspecting data using head(), info() and describe()
Selecting rows and columns
Filtering data using conditions
Indexing with loc and iloc
Sorting and ranking data
Adding, updating and deleting columns
Handling missing values
Removing duplicate records
Changing data types
String operations on columns
Date and time processing
Grouping and aggregation using groupby()
Merging and joining DataFrames
Concatenating datasets
Pivot tables and cross-tabulation
Applying functions to columns
Exporting processed datasets
Common Pandas interview questions
13
SQL and Database Fundamentals
18 Topics
Month 2
Introduction to relational databases
Database, tables, rows and columns
Primary keys and foreign keys
CREATE, ALTER, DROP and TRUNCATE
INSERT, UPDATE and DELETE
SELECT, WHERE, DISTINCT and ORDER BY
Comparison, logical and pattern-matching operators
Aggregate functions
GROUP BY and HAVING
INNER, LEFT, RIGHT and CROSS joins
Subqueries
CASE expressions
Common Table Expressions (CTEs)
Window functions: ROW_NUMBER, RANK, LAG and LEAD
Date functions and NULL handling
Connecting Python with databases
Loading SQL data into Pandas
SQL interview queries for freshers
14
Data Cleaning and Exploratory Data Analysis
15 Topics
Month 2
Understanding business problems and datasets
Understanding dataset structure and data dictionaries
Identifying missing and duplicate records
Handling inconsistent data
Detecting and treating outliers
Univariate analysis
Bivariate analysis
Multivariate analysis
Distribution analysis
Correlation analysis
Data transformation and normalization
Feature relationships
Documenting data quality issues
Extracting trends and business insights
Performing EDA using Pandas
15
Data Visualization
15 Topics
Month 2
Introduction to Matplotlib
Line charts
Bar charts and grouped bar charts
Histograms
Scatter plots
Pie charts and their limitations
Box plots
Subplots and figure customization
Labels, legends, axes and annotations
Seaborn fundamentals
Heatmaps and correlation matrices
Pair plots and distribution plots
Choosing appropriate visualizations
Data storytelling and business insights
Creating an EDA report with visualizations
16
Practical Data Science and Business Analytics
12 Topics
Month 2
Data Science lifecycle
Understanding business questions
Data collection and data sources
Data importing and preprocessing
Exploratory data analysis workflow
Descriptive and comparative analysis
Identifying trends and patterns
Customer and sales data analysis
KPI calculation and interpretation
Creating analytical summaries
Preparing a business insights report
Data Science interview questions for freshers
17
Introduction to Artificial Intelligence and Machine Learning
13 Topics
Month 3
What is Artificial Intelligence?
What is Machine Learning?
Difference between AI, ML, Deep Learning and Data Science
Real-world applications of AI and ML
Supervised learning
Unsupervised learning
Reinforcement learning: basic overview
Features and target variables
Training, validation and testing datasets
Machine Learning workflow
Model parameters and hyperparameters
Data leakage and its prevention
Introduction to scikit-learn
18
Data Preprocessing for Machine Learning
13 Topics
Month 3
Loading datasets into Python
Identifying numerical and categorical features
Handling missing values
Removing duplicate records
Detecting and treating outliers
Label encoding and one-hot encoding
Feature scaling
Standardization and normalization
Train-test split
Stratified sampling
Data preprocessing pipelines
Preventing data leakage
Using scikit-learn preprocessing tools
19
Linear and Polynomial Regression
16 Topics
Month 3
Introduction to regression
Dependent and independent variables
Simple linear regression
Regression equation and coefficients
Multiple linear regression
Training regression models using scikit-learn
Predictions on unseen data
Residuals and error analysis
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Coefficient of determination (R-squared)
Polynomial regression
Underfitting and overfitting
Practical regression problems
Regression interview questions
20
Classification Algorithms
14 Topics
Month 3
Introduction to classification
Binary and multiclass classification
Logistic regression
K-Nearest Neighbors (KNN)
Decision trees
Gini impurity and entropy
Naive Bayes
Support Vector Machines (SVM)
Random Forest fundamentals
Training and predicting with scikit-learn
Decision boundaries: conceptual introduction
Comparing classification algorithms
Practical classification problems
Classification interview questions
21
Classification Model Evaluation
15 Topics
Month 3
Confusion matrix
True positives, true negatives, false positives and false negatives
Accuracy
Precision
Recall
F1-score
Specificity
Classification report
ROC curve
Area Under the ROC Curve (ROC-AUC)
Precision-recall curve
Class imbalance
Class weights and resampling fundamentals
Selecting metrics for business problems
Model evaluation interview questions
22
Unsupervised Learning and Clustering
11 Topics
Month 3
Introduction to unsupervised learning
Clustering vs classification
K-Means clustering
Selecting the number of clusters
Elbow method
Silhouette score
Hierarchical clustering
Dendrograms
DBSCAN: introductory concepts
Cluster interpretation
Customer segmentation case study
23
Model Validation, Feature Selection and Regularization
14 Topics
Month 3
Holdout validation
K-fold cross-validation
Stratified cross-validation
Model parameters vs hyperparameters
Grid search
Randomized search
Ridge regression
Lasso regression
Feature selection fundamentals
Pipeline-based model training
Comparing model performance
Reproducibility and random seeds
Saving and loading models using joblib
Common ML model optimization interview questions
24
Ensemble Learning
10 Topics
Month 4
Introduction to ensemble learning
Bagging and boosting
Random Forest classification
Random Forest regression
Feature importance
AdaBoost fundamentals
Gradient Boosting fundamentals
XGBoost: introductory concepts
Voting classifiers
Comparing individual and ensemble models
25
Feature Engineering and Dimensionality Reduction
12 Topics
Month 4
Features and their importance
Feature selection vs feature extraction
Removing redundant features
Correlation-based feature selection
Principal Component Analysis (PCA)
Explained variance
Scaling before dimensionality reduction
Creating numerical features
Binning and transformations
Date-based feature extraction
Categorical feature engineering
Practical feature engineering using Pandas and scikit-learn
26
Time Series Analysis and Basic Forecasting
11 Topics
Month 4
Introduction to time series data
Trend, seasonality and residual components
Time-based train-test splitting
Moving averages
Lag features
Time series visualization
Forecasting evaluation metrics
Baseline forecasting methods
Introduction to ARIMA
Basic sales and demand forecasting
Preventing future-data leakage
27
Introduction to Deep Learning
13 Topics
Month 4
What is Deep Learning?
Machine Learning vs Deep Learning
Artificial neurons and neural networks
Inputs, weights, biases and outputs
Activation functions: ReLU, sigmoid and softmax
Forward propagation
Loss functions
Gradient descent
Backpropagation: conceptual introduction
Epochs, batches and learning rates
Training and validation loss
Overfitting in neural networks
Introduction to TensorFlow/Keras
28
Basic Neural Network Implementation
9 Topics
Month 4
Preparing numerical datasets for neural networks
Building a basic neural network using Keras
Selecting an optimizer
Choosing a loss function
Training and validating a model
Plotting learning curves
Saving and loading a trained model
Comparing a basic neural network with traditional ML
Introductory neural network interview questions
29
Introduction to Natural Language Processing
14 Topics
Month 4
Introduction to NLP and its applications
Text data preparation
Text normalization
Tokenization
Removing punctuation and stop words
Stemming and lemmatization
N-grams
Bag of Words
TF-IDF
Text vectorization using scikit-learn
Basic text classification
Sentiment analysis
Spam detection
Evaluating NLP classification models
30
Introduction to Generative AI and Language Models
15 Topics
Month 4
What is Generative AI?
Traditional AI vs Generative AI
Introduction to Large Language Models (LLMs)
Basic understanding of tokens and context windows
Common applications of AI chatbots
Prompt engineering fundamentals
Zero-shot and few-shot prompting
Writing clear and structured prompts
Understanding model responses and limitations
Hallucinations and output verification
Responsible AI use and data privacy
Introduction to using an LLM through a hosted API
Basic API request and response handling using Python
API keys and environment variables
Simple AI-assisted application demonstration
31
Machine Learning Projects and End-to-End Workflow
13 Topics
Month 5
Identifying a real-world problem
Defining project scope and objectives
Collecting and understanding datasets
Data preprocessing and exploratory analysis
Feature engineering
Establishing a baseline model
Training and comparing ML algorithms
Selecting appropriate evaluation metrics
Improving model performance
Documenting limitations and assumptions
Building a reusable preprocessing and prediction pipeline
Saving trained models
Preparing a project report and presentation
32
Data Science and Analytics Projects
9 Topics
Month 5
Exploratory data analysis project
Sales data analysis using Pandas
Business insights and KPI analysis
Customer data analysis
Data cleaning and transformation
Statistical summaries and visualization
Creating an analytical report
Presenting findings and recommendations
Uploading project notebooks and reports to GitHub
33
Practical AI and NLP Projects
9 Topics
Month 5
Building a basic sentiment analysis application
Creating an email or SMS spam classifier
Building a simple FAQ chatbot using predefined responses or an LLM API
Integrating a hosted LLM API with Python
Handling API errors and invalid responses
Protecting API keys
Testing AI-generated responses
Documenting limitations and responsible use
Project presentation and demonstration
34
Deploying Machine Learning Models with Flask
13 Topics
Month 5
Introduction to application deployment
Introduction to Flask
Routing and request handling
GET and POST methods
Processing JSON requests and responses
Input validation and exception handling
Saving and loading trained ML models
Connecting a trained model to a Flask application
Creating prediction endpoints
Building a simple HTML prediction form
Testing APIs using Postman
Managing dependencies and environment variables
Basic deployment to a suitable hosting platform
35
Python Interview Preparation
13 Topics
Month 6
Python fundamentals and data types
Mutable and immutable objects
Strings, lists, tuples, sets and dictionaries
Functions and argument types
Scope and variable lifetime
Object-oriented programming concepts
Exception and file handling
Iterators, generators and comprehensions
Python output-based questions
Common Python coding problems
Basic searching and sorting questions
Python memory management and garbage collection: fundamentals
Python interview questions for freshers
36
Data Science, NumPy, Pandas and SQL Interview Preparation
13 Topics
Month 6
NumPy arrays and vectorized operations
Pandas Series and DataFrames
Data cleaning and missing-value handling
GroupBy, merging and aggregation
Exploratory data analysis questions
Data visualization concepts
Descriptive statistics
Probability and distributions
Correlation and covariance
SQL joins and aggregate functions
Subqueries and window functions
Business analysis and data interpretation questions
Practical data manipulation exercises
37
Machine Learning and AI Interview Preparation
18 Topics
Month 6
AI, ML, Deep Learning and Data Science differences
Supervised and unsupervised learning
Regression vs classification
Linear and logistic regression
Decision trees, KNN, Naive Bayes and SVM
Random Forest and ensemble learning
Clustering and K-Means
Overfitting and underfitting
Bias-variance trade-off
Feature engineering and feature selection
Cross-validation and hyperparameter tuning
Regression and classification evaluation metrics
Data leakage and model validation
Basic neural network concepts
NLP and text classification fundamentals
Generative AI and prompt engineering basics
LLM API integration fundamentals
Explaining model choices and project results
38
GitHub Portfolio, Resume and Placement Preparation
14 Topics
Month 6
Git and GitHub revision
Creating and organizing project repositories
Writing professional README files
Documenting project setup and execution
Uploading notebooks, source code and requirements
Managing configuration and secrets
Python and Data Science resume preparation
Highlighting projects and technical skills
LinkedIn profile preparation
Explaining projects in technical interviews
Common HR interview questions
Technical mock interviews
Coding practice and timed assessments
Project viva and presentation
39
Major Industrial Project
17 Topics
Month 6
Project selection and problem statement
Data Science: Sales analysis and business insights
Data Science: Customer behavior analysis
Machine Learning: Customer churn prediction
Machine Learning: Loan risk classification
Machine Learning: House price prediction
Forecasting: Basic sales demand prediction
NLP: Sentiment analysis or spam classification
AI: Basic FAQ chatbot using an LLM API
Dataset selection and preparation
Exploratory data analysis
Feature engineering and model development
Model evaluation and error analysis
Application integration where appropriate
GitHub repository and README
Project report and presentation
Live demonstration and viva
