Python & Data Science
Learn Python for Data Science to analyze, visualize, and derive insights from data for informed decision-making and predictive modeling.
6 Months
Online & Offline
4.8
1000+Course Modules
Python Setup & First Script
Welcome to Python! Today you'll set up your environment, write your first script, and learn basic syntax and programming concepts.
Variables & Data Types
In this we’ll explore Python’s core building blocks—variables and data types—and learn how to store, manage, and work with data efficiently.
Operators & Expressions
Explore Python operators and expressions, including arithmetic, comparison, logical operators, and operator precedence for building complex programs.
Conditional Statements
Learn to use conditional statements—if, elif, else, nested conditions, and logical combinations—to make decisions in Python programs.
Loops – for and while
Learn loops in Python, including for and while loops, and control statements like break and continue for iterating and repeating actions.
Functions & Scope
Learn to create reusable Python functions, covering definitions, parameters, return values, and variable scope for clean, maintainable code.
Lists & Tuples
Learn Python lists and tuples, including creation, access, manipulation, and choosing the right structure for data storage.
Dictionaries & Sets
Explore Python dictionaries and sets, learning to create, manipulate, and use key-value pairs and unique data effectively.
String Operations
Learn Python string manipulation, including indexing, slicing, methods, and formatting with f-strings.
File Handling in Python
Learn Python file handling, including reading, writing, file modes, and safe, efficient file operations.
Error Handling
Learn Python exception handling with try-except blocks to manage errors and create robust, error-resistant code.
Introduction to NumPy
Learn NumPy for scientific computing in Python, including arrays, operations, and efficient multi-dimensional data handling.
Introduction to Pandas
Pandas is a Python library for data manipulation and analysis, providing powerful tools for working with structured data.
Data Filtering with Pandas
Data filtering in Pandas involves selecting and manipulating specific rows or columns based on conditions for analysis.
Data Cleaning with Pandas
Data cleaning in Pandas involves detecting, handling, and correcting missing or inconsistent data for accurate analysis.
Data Aggregation
Data aggregation combines and summarizes data, often using functions like sum, mean, or count, to extract meaningful insights.
Data Visualization with Matplotlib
Data visualization with Matplotlib involves creating graphs and plots to visually analyze and interpret data.
Visualization with Seaborn
Seaborn enables advanced statistical data visualization in Python with attractive, informative plots.
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) involves examining and summarizing datasets to uncover patterns, trends, and insights before formal modeling.
Intro to Machine Learning
Machine Learning is a field of AI that enables systems to learn from data and make predictions or decisions without explicit programming.
Linear Regression with Scikit-learn
Linear Regression with Scikit-learn predicts continuous outcomes by modeling the relationship between dependent and independent variables.
Logistic Regression
Logistic Regression models the probability of categorical outcomes, commonly used for binary classification tasks.
Decision Trees
Decision Trees are a supervised learning method that splits data into branches to make predictions or classifications.
k-NN Algorithm
The k-NN algorithm classifies data points by analyzing the closest neighbors in feature space, assigning the most common label among them.
Model Evaluation
Model evaluation assesses a machine learning model's performance using metrics like accuracy, precision, recall, and F1-score.
Clustering with K-Means
K-Means clustering partitions data into distinct groups by minimizing distances within clusters, helping identify patterns and similarities.
Real-World Project – Data Cleaning
Real-world data cleaning project involves detecting, correcting, and preparing raw data for accurate analysis and modeling.
Real-World Project – Analysis & Visualization
Real-world analysis and visualization project focuses on exploring data, uncovering insights, and presenting findings through effective visualizations.
Real-World Project – Modeling & Insights
Real-world modeling and insights project involves building predictive models and deriving actionable insights from data.
Project Presentation & Review
Project presentation and review involves showcasing results, explaining methods, and receiving feedback to refine and validate work.
Course Overview
Master Python fundamentals and libraries like Pandas, NumPy, and Matplotlib for data manipulation and visualization. Learn statistical analysis, data cleaning, and exploratory data analysis to prepare datasets for real-world problems. Gain hands-on experience in machine learning, predictive modeling, and data-driven projects, building practical skills for a career in data science.
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