About This Course
Data Science with Python: Beginner to Advanced
Unlock the power of data with our comprehensive Data Science with Python course. Learn Python programming fundamentals including variables, data types, control flow, functions, and data structures. Master data manipulation and analysis with Pandas and NumPy libraries. Create stunning visualizations with Matplotlib and Seaborn. Explore SQL for data extraction and statistical methods for data analysis. Dive into machine learning with supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction). Advance to deep learning with Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) for image processing, and Recurrent Neural Networks (RNN) for sequence data using TensorFlow and PyTorch. Gain hands-on experience with real-world projects in big data analytics, automation workflows, and predictive analytics. Whether you're a beginner or aspiring data scientist, this course equips you with the skills to analyze data, build models, and make data-driven decisions. Enroll now and start your data science journey! 📊
Course Benefits
- 35+ hrs of Training
- Python to Deep Learning
- Industry assignments
- Real-world projects
- Lifetime LMS Access
- MNC-certified trainers
Course Curriculum
1. Introduction to Python for Data Science
- Why Python? – Python's popularity and real-world applications across industries
- Real-World Applications – Data science, AI, automation, and analytics use cases
- Installation & Setup – Install Python and set up IDEs like Jupyter Notebook and VS Code
2. Basics of Python Syntax
- Variables & Data Types – int, float, string, boolean, and type conversion
- Type Conversion – Casting between different data types
- Input/Output Functions – Using print() and input() for user interaction
3. Operations in Python
- Arithmetic Operators – Addition, subtraction, multiplication, division, modulus
- Relational & Logical Operators – Comparison and boolean logic
- Bitwise & Assignment Operators – Bit manipulation and variable assignments
- Identity & Membership – is, in operators for object and collection operations
4. Control Flow Statements
- Conditional Statements – if, elif, else for decision making
- Loops – for loops, while loops, break, and continue statements
- Iteration – Iterating through lists, strings, dictionaries, and ranges
5. Functions and Modules
- Creating Functions – Using def keyword, arguments, and return values
- Lambda Functions – Anonymous functions for concise operations
- Built-in Modules – Importing and using math, random, and other modules
6. Data Structures in Python
- Lists – Indexing, slicing, and methods like append(), pop(), extend()
- Tuples & Sets – Immutable structures and set operations (union, intersection)
- Dictionaries – Key-value pairs and methods like get(), update(), keys(), values()
7. File Handling
- File Operations – Opening, reading, and writing files using built-in functions
- With Statement – Safe file handling with context managers
- CSV & JSON – Working with structured data formats
8. Exception Handling
- Try-Except Blocks – Handling errors gracefully to prevent crashes
- Finally & Else – Complete error handling control flow
- Debugging – Preventing program crashes and easier debugging
9. SQL for Data Science and Statistics
- SQL Queries – Extracting and filtering data from databases
- Joins & Aggregations – Performing joins, grouping, and data aggregation
- Real-World SQL – Applying SQL to data science problems
- Statistics – Mean, median, mode, variance, standard deviation, probability, and distributions
- Statistical Methods – Applying statistics in data analysis and hypothesis testing
10. Data Analysis (Pandas & NumPy Hands-on)
- Pandas DataFrames – Handling and manipulating structured data
- NumPy Arrays – Performing mathematical calculations and array operations
- Data Cleaning – Cleaning, filtering, and analyzing real datasets hands-on
11. Machine Learning & Deep Learning
- ML Fundamentals – Core machine learning concepts and workflows
- Supervised Learning – Regression and classification algorithms
- Unsupervised Learning – Clustering and dimensionality reduction techniques
- Artificial Neural Networks (ANN) – Understanding deep learning basics
- Convolutional Neural Networks (CNN) – Image processing and computer vision
- Recurrent Neural Networks (RNN) – Sequence data and time series analysis
Requirements
- No prior programming knowledge required
- Basic mathematics understanding (helpful but not mandatory)
- Computer with internet connection
- Enthusiasm to learn data science and analytics
Material Includes
- 35+ Hours of Video Lectures by MNC Certified Trainer
- Lifetime LMS Access
- Industry-Based Hands-on Projects
- Section Quizzes and Assessments
- Completion Certification
Why Choose This Course
Python Mastery
Complete Python programming from basics to advanced data science applications
Data Visualization
Create stunning visualizations with Matplotlib, Seaborn, and Pandas plotting
Machine Learning
Master supervised and unsupervised learning algorithms with scikit-learn
Deep Learning
Build neural networks with TensorFlow and PyTorch for AI applications
Hands-On Projects
Real-world analytics projects in big data, automation, and predictions
Career Ready
Build portfolio projects and gain skills for data scientist roles
Technologies You'll Master
Python 3
Core programming language
Pandas & NumPy
Data manipulation & analysis
Matplotlib & Seaborn
Data visualization
Scikit-learn
Machine learning algorithms
TensorFlow & PyTorch
Deep learning frameworks
SQL
Database querying
Ready to Master Data Science?
Join 1,500+ students building data-driven solutions. Launch your data science career with Python, ML, and AI expertise.