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
📈 Your Market Value After This Course
Salary ranges are indicative estimates and vary by location, employer, experience, and qualifications. They are not guaranteed earnings or placement outcomes.
Fresher / Entry Level
₹5 - 8 LPA
0-2 years experience
- Junior Data Scientist
- Data Analyst Trainee
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
🎯 Job Roles You Can Apply For
Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
Python Data Analyst
Data Engineer
⚡ Skills You'll Master
Python
NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
Statistics
Machine Learning
Data Visualization
SQL
Jupyter Notebook
Git
📚 Complete Course Syllabus
Master every aspect with our comprehensive curriculum
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
⭐ Why Choose Viroha Academy?
Explore the learning support and course features listed below.
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
💻 Real-Time Projects
Build impressive portfolio with industry-relevant projects
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
🚀 Career Support
Contact Viroha Academy to discuss the career guidance available for this course.
Ask our team about course-specific career preparation and available support.
Contact Viroha AcademyOur Top Hiring Partners
❓ Frequently Asked Questions
Got questions? We've got answers
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.
🚀 Ready to Start Your Data Science with Python Journey?
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