Technologies You Will Learn
Python
NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
SQL
Excel
Jupyter
Streamlit
Git
Curriculum
Python with Data Science Curriculum
01
Introduction to Python and Development Environment
7 Topics
Month 1
What is Python? Features, applications, and career scope
Python interpreter and execution process
Installing Python and setting up VS Code
Using IDLE, Jupyter Notebook, and Google Colab
Running Python scripts and notebooks
Comments, indentation, and coding conventions
Introduction to Git and GitHub
02
Variables, Data Types, and Operators
9 Topics
Month 1
Variables, identifiers, and naming conventions
Numeric, string, and Boolean data types
Dynamic typing and type checking
Type conversion and type casting
Arithmetic, comparison, logical, assignment, and bitwise operators
Membership and identity operators
Operator precedence and associativity
Taking user input and displaying output
print(), input(), type(), and id()
03
Conditional Statements and Loops
8 Topics
Month 1
if, if-else, and if-elif-else
Nested conditional statements
for and while loops
range() function
break, continue, and pass
Nested loops
Loop control and common programming mistakes
Pattern-based and logic-building programs
04
Python Strings
8 Topics
Month 1
Creating strings and string immutability
Indexing and slicing
String operators and concatenation
Important string methods
Searching, replacing, splitting, and joining
String formatting: f-strings, format(), and formatted output
Character frequency and palindrome problems
Basic text preprocessing exercises
05
Python Collections and Data Structures
9 Topics
Month 1
Lists: creation, indexing, slicing, and methods
Tuples and tuple operations
Sets, uniqueness, and set operations
Dictionaries: keys, values, and key-value operations
Mutable versus immutable objects
Nested collections
List, set, and dictionary comprehensions
Sorting and custom sorting using key
Choosing the right data structure for a problem
06
Functions and Modular Programming
9 Topics
Month 1
Defining and calling functions
Parameters, arguments, and return values
Positional, keyword, default, and variable-length arguments
*args and **kwargs
Local and global scope
Lambda functions
map(), filter(), and reduce()
Recursion fundamentals
Modules, packages, imports, and __name__ == "__main__"
07
Exception Handling and File Handling
8 Topics
Month 1
Syntax errors versus runtime errors
try, except, else, and finally
Raising exceptions and creating custom exceptions
Reading and writing text files
File modes and context managers using with
Working with CSV, JSON, and basic Excel files
File paths and directory operations
Handling missing files and invalid data
08
Object-Oriented Programming in Python
8 Topics
Month 1
Classes, objects, and constructors
Instance variables and class variables
Instance, class, and static methods
Encapsulation and access conventions
Inheritance and method overriding
Polymorphism and abstraction
Special methods such as __str__
Basic use of modules and reusable class design
09
Python Utilities and Programming Practice
8 Topics
Month 1
Iterators and iterables
Generators and yield
Introduction to decorators
datetime, math, random, and os modules
Debugging and reading tracebacks
Writing reusable, readable code
Basic time and space complexity
Problem-solving exercises using arrays, strings, and dictionaries
10
Data Science Fundamentals
8 Topics
Month 2
What is Data Science?
Data Science versus Data Analytics versus Machine Learning
Structured, semi-structured, and unstructured data
Numerical, categorical, ordinal, and time-series data
Data Science lifecycle: problem definition to communication
Introduction to datasets, data sources, and data quality
CSV, Excel, JSON, and database data
Introduction to Jupyter Notebook and Google Colab
11
NumPy
11 Topics
Month 2
NumPy installation and importing
Creating arrays using different methods
Array dimensions, shape, size, and data types
Indexing, slicing, and reshaping
Array operations and vectorization
Broadcasting
Aggregation: sum, mean, minimum, maximum, and standard deviation
Boolean indexing and filtering
Sorting, splitting, joining, and stacking arrays
Random number generation
Handling missing or invalid numerical values
12
Pandas Fundamentals
10 Topics
Month 2
Series and DataFrame
Creating DataFrames from lists, dictionaries, and files
Reading and exporting CSV and Excel files
Inspecting datasets using head(), tail(), info(), and describe()
Selecting rows and columns using loc and iloc
Filtering and sorting data
Adding, updating, and deleting columns
Data types and type conversion
Renaming columns and resetting indexes
Applying functions to columns
13
Data Cleaning and Preprocessing
10 Topics
Month 2
Identifying and handling missing values
Removing duplicate records
Correcting inconsistent data
Detecting invalid values and data-entry errors
Converting data types
Cleaning strings and categorical values
Handling outliers at an introductory level
Data normalization and standardization concepts
Working with dates and times
Data validation and data-quality checks
14
Data Manipulation Using Pandas
10 Topics
Month 2
groupby() and aggregation
value_counts() and frequency analysis
merge(), join(), and concat()
Pivot tables and cross-tabulation
Reshaping using melt() and pivot()
Ranking and sorting
Applying custom functions
Working with multiple datasets
Efficient DataFrame operations
Exporting cleaned and summarized datasets
15
SQL and Relational Databases for Data Science
14 Topics
Month 2
Database, table, row, column, and schema concepts
Primary keys and foreign keys
CREATE, ALTER, INSERT, UPDATE, and DELETE
SELECT, WHERE, ORDER BY, and DISTINCT
Aggregate functions
GROUP BY and HAVING
Joins: inner, left, right, and full outer
Subqueries and common table expressions (CTEs)
CASE expressions
Date and string functions
NULL handling
Window functions: ROW_NUMBER(), RANK(), and SUM() OVER()
Connecting Python with a database
Loading SQL query results into Pandas
16
Excel for Data Science
9 Topics
Month 2
Excel workbook and worksheet fundamentals
Sorting and filtering
Common formulas and functions
Conditional formulas: IF, SUMIF, COUNTIF, and related functions
Lookup functions: VLOOKUP, XLOOKUP, and INDEX-MATCH
Data validation and conditional formatting
Pivot tables and pivot charts
Importing and exporting Excel data with Pandas
Choosing between Excel, SQL, and Python for different tasks
17
Mathematics and Statistics for Data Science
15 Topics
Month 3
Population, sample, parameter, and statistic
Mean, median, and mode
Range, variance, and standard deviation
Quartiles, percentiles, and interquartile range
Outlier detection using IQR and z-scores
Basic probability rules
Conditional probability and Bayes' theorem fundamentals
Common probability distributions: normal, binomial, and Poisson
Sampling and sampling bias
Correlation versus causation
Covariance and correlation coefficients
Confidence intervals
Hypothesis testing, p-values, and significance
Type I and Type II errors
Introduction to A/B testing
18
Exploratory Data Analysis (EDA)
11 Topics
Month 3
Defining analytical questions
Understanding dataset structure and data quality
Univariate analysis
Bivariate and multivariate analysis
Distribution analysis
Outlier and anomaly investigation
Correlation analysis
Identifying trends and relationships
Feature-level analysis
Asking business questions and validating assumptions
Preparing an EDA report with conclusions
19
Data Visualization Using Matplotlib
11 Topics
Month 3
Figure, axes, and plotting fundamentals
Line charts
Bar charts and horizontal bar charts
Histograms
Scatter plots
Pie charts and their appropriate use
Box plots
Titles, labels, legends, ticks, and annotations
Subplots and multiple charts
Formatting and saving visualizations
Selecting suitable charts for different questions
20
Data Visualization Using Seaborn
10 Topics
Month 3
Seaborn fundamentals
Count plots and bar plots
Histograms and distribution plots
Box plots and violin plots
Scatter plots and regression plots
Pair plots
Heatmaps and correlation matrices
Categorical versus numerical comparisons
Visualizing outliers and distributions
Chart readability and avoiding misleading visualizations
21
Business Analysis and KPI Reporting
9 Topics
Month 3
Converting business requirements into analytical questions
Defining KPIs and business metrics
Revenue, profit, growth, and average order value
Customer retention and repeat-purchase metrics
Month-over-month and year-over-year comparisons
Product, region, and customer segmentation
Trend and seasonality analysis
Communicating findings to non-technical stakeholders
Building a report with recommendations and limitations
22
Introduction to Machine Learning
11 Topics
Month 4
What is Machine Learning?
AI versus Machine Learning versus Deep Learning
Supervised and unsupervised learning
Regression versus classification
Features, labels, and target variables
Training, validation, and testing datasets
The end-to-end ML workflow
Introduction to scikit-learn
Baseline models and evaluation
Overfitting and underfitting
Bias-variance trade-off fundamentals
23
Data Preprocessing for Machine Learning
10 Topics
Month 4
Handling missing values
Encoding categorical variables
Label encoding and one-hot encoding
Feature scaling
Standardization versus normalization
Train-test split
Data leakage and how to avoid it
Feature selection fundamentals
ColumnTransformer and preprocessing pipelines
Reproducible experiments and random states
24
Regression Algorithms
11 Topics
Month 4
Regression problem formulation
Simple linear regression
Multiple linear regression
Polynomial regression
Decision tree regression
Random forest regression
Understanding coefficients and predictions
Residual analysis
MAE, MSE, RMSE, and \(R^2\)
Comparing regression models
Practical prediction exercises
25
Classification Algorithms
12 Topics
Month 4
Classification problem formulation
Logistic regression
K-nearest neighbors (KNN)
Decision tree classifier
Random forest classifier
Naive Bayes fundamentals
Support vector machine (SVM) fundamentals
Binary versus multiclass classification
Confusion matrix
Accuracy, precision, recall, and F1-score
ROC curve and AUC fundamentals
Choosing evaluation metrics based on the business problem
26
Model Evaluation and Improvement
11 Topics
Month 4
Establishing a baseline
Cross-validation
Hyperparameters versus model parameters
Grid search and randomized search
Class imbalance
Class weights and resampling concepts
Feature importance and model interpretation
Comparing multiple models fairly
Model limitations and error analysis
Saving and loading models using joblib
Creating a reproducible ML pipeline
27
Unsupervised Machine Learning
10 Topics
Month 5
Supervised versus unsupervised learning recap
Clustering use cases
K-means clustering
Selecting the number of clusters
Elbow method
Silhouette score
Hierarchical clustering fundamentals
DBSCAN fundamentals
Customer segmentation
Interpreting and profiling clusters
28
Advanced Feature Engineering and Model Selection
10 Topics
Month 5
Feature creation and transformation
Handling skewed numerical features
Categorical feature engineering
Feature selection methods
Dimensionality reduction concepts
Principal Component Analysis (PCA) fundamentals
Pipeline-based preprocessing
Comparing models with cross-validation
Hyperparameter tuning
Model selection based on accuracy, interpretability, and business needs
29
Time-Series Analysis Fundamentals
10 Topics
Month 5
Time-series data and common use cases
Date-time parsing and resampling
Trend, seasonality, and noise
Rolling averages
Time-based train-test splitting
Lag features
Forecasting baselines
Introduction to ARIMA concepts
Forecast evaluation using MAE and RMSE
Avoiding data leakage in forecasting
30
Introduction to Natural Language Processing
10 Topics
Month 5
Text data and common NLP use cases
Text cleaning and normalization
Tokenization
Stop-word removal
Stemming and lemmatization
Bag of Words
TF-IDF
Text classification fundamentals
Sentiment analysis using a prebuilt dataset
Evaluating a text classification model
31
Introduction to Data Science Applications
8 Topics
Month 5
Saving and loading datasets and trained models
Building a simple interface using Streamlit
Accepting user inputs and showing predictions
Displaying charts and analytical summaries
Connecting the interface with a trained model
Introduction to APIs and deployment concepts
Environment setup and dependency management
Responsible data use, privacy, bias, and model limitations
32
End-to-End Data Science Workflow
10 Topics
Month 6
Understand and define a real-world problem
Identify data sources and project requirements
Extract data using files, SQL, or APIs
Clean, validate, and transform data
Perform EDA and statistical analysis
Engineer features
Build and evaluate suitable models
Compare alternatives and document decisions
Present findings and recommendations
Create a reproducible project structure
33
Git, GitHub, and Project Documentation
9 Topics
Month 6
Git fundamentals and repository creation
git init, add, commit, push, and pull
Branching and merging basics
Organizing notebooks, datasets, scripts, and reports
Writing a useful README
Documenting installation and execution steps
Requirements files and dependency management
Managing configuration and sensitive credentials
Publishing a professional project portfolio
34
Model Deployment Fundamentals
8 Topics
Month 6
Saving trained models
Building a Streamlit application
User input, validation, and prediction output
Displaying model metrics and visualizations
Running and testing an application locally
Introduction to API-based model serving
Basic deployment options
Deployment limitations, error handling, and monitoring concepts
35
Major Project Development and Review
9 Topics
Month 6
Finalize the project problem statement
Prepare a project proposal and timeline
Develop the data pipeline and analytical workflow
Train, evaluate, and refine models where appropriate
Build a dashboard or user interface
Write technical documentation
Conduct code review and testing
Prepare a final presentation and demonstration
Complete mentor feedback and revisions
36
Interview and Placement Preparation
12 Topics
Month 6
Python programming interview questions
Lists, dictionaries, strings, functions, and OOP revision
NumPy and Pandas interview questions
SQL queries, joins, aggregations, and window functions
Statistics and probability questions
EDA and data-cleaning scenarios
Machine learning concepts and algorithm comparisons
Model evaluation and problem-solving questions
Project-based technical interviews
Resume and LinkedIn profile preparation
GitHub portfolio review
Mock interviews and project presentation practice
