Data Science with Python Course

Master Data Science using Python. Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Machine Learning, and real-world data analysis.

Duration 4 Months (130 Hours)

Mode Live Online / Offline

4,800+ Students

420+ Partners

91% Placement

Fresher / Entry Level

₹5 - 8 LPA

0-2 years experience

Junior Data Scientist
Data Analyst Trainee

⭐ Most Common

Mid Level

₹9 - 18 LPA

2-5 years experience

Data Scientist
Machine Learning Engineer

Senior / Expert Level

₹20 - 40+ LPA

5+ years experience

Senior Data Scientist
Lead Data Scientist

Data Scientist

Data Analyst

Machine Learning Engineer

Business Intelligence Analyst

Python Data Analyst

Data Engineer

Python

NumPy

Pandas

Matplotlib

Seaborn

Scikit-learn

Statistics

Machine Learning

Data Visualization

SQL

Jupyter Notebook

Git

Module 1: Python Programming Basics

Module 2: Python for Data Science

Introduction to Data Science with Python
Installing Data Science Libraries - pip install
Jupyter Notebook & Google Colab Setup
Working with CSV & Excel Files
Reading Data from APIs
Basic Data Exploration Techniques

Module 3: NumPy - Numerical Computing

Introduction to NumPy - Why NumPy
Creating NumPy Arrays - array(), zeros(), ones(), arange()
Array Attributes - shape, size, dtype, ndim
Array Indexing & Slicing
Reshaping Arrays - reshape(), flatten(), ravel()
Array Operations - addition, subtraction, multiplication
Universal Functions - sin, cos, exp, log, sqrt
Statistical Functions - mean, median, std, var, min, max
Matrix Operations - dot product, transpose
Broadcasting in NumPy
Loading & Saving Data with NumPy

Module 4: Pandas - Data Manipulation

Introduction to Pandas - Series & DataFrame
Creating DataFrames - from dict, list, CSV, Excel
Data Inspection - head(), tail(), info(), describe()
Selecting Columns & Rows - iloc, loc
Filtering Data - boolean indexing
Handling Missing Values - isnull(), dropna(), fillna()
Handling Duplicates - duplicated(), drop_duplicates()
Data Transformation - apply(), map(), replace()
Grouping Data - groupby(), agg(), transform()
Merging & Joining DataFrames - merge(), concat(), join()
Pivot Tables - pivot_table()
Time Series Analysis with Pandas
Reading/Writing Data - CSV, Excel, JSON, SQL

Module 5: Data Visualization (Matplotlib & Seaborn)

Introduction to Data Visualization
Matplotlib Basics - Figure, Axes, Subplots
Line Charts & Area Charts
Bar Charts & Histograms
Scatter Plots & Bubble Charts
Pie Charts & Donut Charts
Box Plots & Violin Plots
Seaborn - Statistical Data Visualization
Heatmaps, Pairplots, Jointplots
Customizing Plots - colors, labels, legends, titles
Plotly - Interactive Visualizations
Creating Dashboards with Visualizations

Module 6: Statistics for Data Science

Descriptive Statistics - Mean, Median, Mode
Measures of Dispersion - Variance, Standard Deviation, Range
Probability Distributions - Normal, Binomial, Poisson
Correlation & Covariance
Central Limit Theorem
Hypothesis Testing - t-test, chi-square
P-values & Confidence Intervals
ANOVA - Analysis of Variance

Module 7: Machine Learning Fundamentals

Module 8: Supervised Learning - Regression

Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Ridge & Lasso Regression (Regularization)
Evaluation Metrics for Regression - MAE, MSE, RMSE, R²

Module 9: Supervised Learning - Classification

Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machines (SVM)
Naive Bayes Classifier
Evaluation Metrics - Accuracy, Precision, Recall, F1-Score
Confusion Matrix & ROC Curve

Module 10: Unsupervised Learning - Clustering

K-Means Clustering
Hierarchical Clustering
DBSCAN
Principal Component Analysis (PCA)
Choosing Optimal Number of Clusters - Elbow Method

Module 11: Feature Engineering & Model Evaluation

What is Feature Engineering
Handling Missing Values
Handling Outliers
Encoding Categorical Variables - One-Hot, Label Encoding
Feature Scaling - StandardScaler, MinMaxScaler
Feature Selection Techniques
Cross-Validation - K-Fold, Stratified K-Fold
Hyperparameter Tuning - GridSearchCV, RandomizedSearchCV

Module 12: Real-World Data Science Projects

Project 1: Exploratory Data Analysis (EDA) on Titanic Dataset
Project 2: House Price Prediction using Regression
Project 3: Customer Churn Prediction using Classification
Project 4: Customer Segmentation using Clustering
Project 5: Sales Forecasting with Time Series
Capstone Project - Complete Data Science Pipeline

Expert Trainers

Learn from industry professionals with 10+ years of Data Science experience

Hands-on Projects

Work on 8+ real-world Data Science projects

Industry Certification

Get globally recognized Data Science certification

100% Placement Support

Tie-ups with 420+ companies for Data Science roles

Resume Building

Professional resume & portfolio with Data Science projects

Mock Interviews

Regular mock interviews with detailed feedback

House Price Prediction

Build a regression model to predict house prices using features like location, size, number of rooms, etc.

Python Pandas Scikit-learn Linear Regression Matplotlib

Customer Churn Prediction

Create a classification model to predict customer churn for a telecom company.

Python Pandas Scikit-learn Logistic Regression Random Forest

Customer Segmentation

Perform customer segmentation using K-Means clustering for targeted marketing campaigns.

Python Pandas Scikit-learn K-Means Seaborn

Placement Support Includes:

Resume & LinkedIn Profile Building
Aptitude & Technical Training
Mock Interviews with Industry Experts
Soft Skills & Communication Training

Our Hiring Partners:

500+ Hiring Partners
Unlimited Interview Opportunities
Job Portal Access
Life-long Placement Support

What are the prerequisites for Data Science with Python course?

Basic programming knowledge is helpful. We cover Python from scratch, so no prior Python experience required.

What is the duration of the course?

The course duration is 4 months (130 hours) with flexible batch timings.

What libraries will I learn?

You will learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and other data science libraries.

What projects will I build?

You will build 8+ projects including House Price Prediction, Churn Prediction, and Customer Segmentation.

Is placement assistance provided?

Yes, we provide 100% placement assistance with 420+ hiring partners.

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