Data Science with Python
Master Python, Data Analysis, Visualization and Machine Learning
Learn Data Science with Python from the fundamentals to real-world machine learning. Master Python programming, NumPy, Pandas, data visualization, statistics, SQL, machine learning and practical projects.
About This Course
This comprehensive Data Science with Python course is designed for beginners who want to build a strong foundation in data science and develop practical industry-ready skills. You will learn Python programming, data manipulation, exploratory data analysis, statistics, data visualization, SQL, machine learning and model evaluation through hands-on exercises and real-world projects. By the end of the course, you will be able to analyze datasets, create meaningful visualizations, build machine learning models and develop portfolio projects that demonstrate your skills.
- Live Instructor-Led Classes
- Python Programming Fundamentals
- NumPy and Pandas
- Data Cleaning and Preprocessing
- Exploratory Data Analysis
- Matplotlib and Seaborn
- Statistics for Data Science
- SQL for Data Analysis
- Machine Learning Fundamentals
- Supervised and Unsupervised Learning
- Real-World Projects
- Assignments and Practice Exercises
- Interview Preparation
- Resume and Portfolio Guidance
- Certificate of Completion
- Internship Support
Skills You Will Learn
Course Syllabus
Module 1: Introduction to Data Science
- What is Data Science
- Data Science Life Cycle
- Data Science Tools and Technologies
- Introduction to Python for Data Science
- Setting Up Python and Jupyter Notebook
Module 2: Python Programming Fundamentals
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops
- Functions
- Lists, Tuples, Sets and Dictionaries
- String Handling
- File Handling
- Exception Handling
Module 3: NumPy
- Introduction to NumPy
- Arrays and Array Operations
- Indexing and Slicing
- Mathematical Operations
- Statistical Operations
- Reshaping and Broadcasting
Module 4: Pandas
- Series and DataFrames
- Reading CSV and Excel Files
- Data Selection and Filtering
- Sorting and Grouping
- Handling Missing Values
- Removing Duplicates
- Merging and Joining Data
- Data Transformation
Module 5: Data Cleaning and EDA
- Data Quality
- Data Cleaning Techniques
- Outlier Detection
- Feature Understanding
- Exploratory Data Analysis
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
Module 6: Data Visualization
- Introduction to Data Visualization
- Matplotlib
- Seaborn
- Bar Charts
- Line Charts
- Histograms
- Box Plots
- Scatter Plots
- Heatmaps
- Creating Business Insights from Visualizations
Module 7: Statistics for Data Science
- Descriptive Statistics
- Mean, Median and Mode
- Variance and Standard Deviation
- Probability Basics
- Normal Distribution
- Correlation and Covariance
- Sampling
- Hypothesis Testing
Module 8: SQL for Data Science
- Database Fundamentals
- SQL Queries
- SELECT and WHERE
- ORDER BY and GROUP BY
- Aggregate Functions
- Joins
- Subqueries
- Working with Real-World Datasets
Module 9: Machine Learning Fundamentals
- Introduction to Machine Learning
- Types of Machine Learning
- Machine Learning Workflow
- Training and Testing Data
- Feature Selection
- Feature Engineering
- Scikit-Learn
Module 10: Supervised Learning
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- Decision Trees
- Random Forest
- Model Evaluation
- Accuracy, Precision and Recall
- Confusion Matrix
Module 11: Unsupervised Learning
- Introduction to Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
- Introduction to PCA
Module 12: Real-World Data Science Projects
- Project Planning
- Data Collection
- Data Cleaning
- Exploratory Data Analysis
- Feature Engineering
- Model Building
- Model Evaluation
- Project Documentation
- Portfolio Development
Live Projects
Project 1: House Price Prediction
Build a machine learning model to predict house prices using regression techniques.
Project 2: Customer Churn Prediction
Analyze customer data and build a classification model to identify customers likely to leave.
Project 3: Sales Data Analysis Dashboard
Analyze sales data using Pandas and create visualizations to identify business trends and insights.
Project 4: Customer Segmentation
Use K-Means clustering to segment customers based on their purchasing behavior.
Project 5: Student Performance Analysis
Analyze student performance data and identify the factors affecting academic outcomes.
Offline Batch
Afternoon Batch
ActiveWho Should Join
- Beginners who want to learn Data Science
- Students and fresh graduates
- Python beginners
- Aspiring Data Analysts
- Aspiring Data Scientists
- Working professionals switching to Data Science
- Anyone interested in Machine Learning
Course Fee
8 Months · 5+ Real-World Projects
Enroll Now Call to Enroll WhatsApp Enquiry- Demo class available
- Weekday & weekend batches
- Live projects + certificate
- Internship support
- Small batch classes
- Expert mentor guidance
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