Python
Course Description
Master Python programming with this comprehensive course designed for beginners to advanced learners. Gain a deep understanding of core Python concepts, data structures, and algorithms while working on hands-on projects. This course equips you with the skills to solve real-world problems and develop efficient Python applications. Enhance your career prospects with this in-demand programming language.
What You’ll Learn From This Course
- Understand Python fundamentals and syntax.
- Master data structures and algorithms.
- Develop real-world applications using Python.
- Learn object-oriented programming in Python.
- Work on hands-on projects and coding exercises.
- Prepare for Python certifications and job interviews.
Certification
Upon successful completion of the Python course, you will receive a professional certification that validates your expertise in Python programming. This certification demonstrates your ability to work with core Python concepts, data structures, algorithms, and object-oriented programming. It highlights your hands-on experience in building Python applications and solving real-world problems. This valuable credential will boost your resume and open doors to exciting career opportunities in software development, data science, and more.
About python
Course Content
The Python course offers a comprehensive introduction to one of the most popular programming languages today. You'll master core concepts like variables, data types, loops, and functions, and dive deeper into advanced topics such as object-oriented programming and file handling. With hands-on projects and practical examples, this course will equip you with the skills needed to build robust Python applications and set you on the path to a successful career in software development or data science.
1. Introduction Python
Course Content
- Case study based discussion and problem solving thought process initiation
- Different Analytical Domain
- Industry mentors guest talk
2.Python
Course Content
- Python, Anaconda and relevant packages installations
- Structure of python program (comments, indentations)
- Operators
- Data type, variable
- User input, string methods
3. Data Structure
Course Content
- Mutable / Immutable
- List
- Tuple
- Sets
- Dictionary
- List, sets and dictionary comprehension
4. Loop & Control Statements
Course Content
- If & Else and Nested if else
- While and For loop
- Break, Continue and Pass
- Keywords in python
- Pattern making
5. Functions
Course Content
- Basic functions
- Built-in
- User-defined functions
- *args, **kwargs
6. Advanced Functions
Course Content
- Maps, filter, reduce
- Iterators and iterables
- Closures / decorators
- Generators
- File Handling and Exception Handling
7. Object Oriented Programming
Course Content
- Class & Object
- Data abstraction, encapsulation, Inheritance, Dunder methods
- Customized modules
8. Django
Course Content
- Starting your First Web Application
- Developing Standard Web Template
- Django Admin
- Models
- Views and URLconfs
- Forms
9. Numpy
Course Content
- Indexing/slicing
- Appending /Inserting on axis
- Mathematical and statistical operations
- Sort/Condition
- Transpose Operations
- Joining/splitting
10. Pandas
Course Content
- Data Extraction
- Series Dataframe and Plane
- Indexing and Slicing
- Conditions/Grouping/Imputations
- Append/concat/merge/join
- Date time functionalities and resampling
- Excel functions
11. Data Visualization with Matplotlib and Seaborn
Course Content
- Customization of matplotlib and seaborn
- Scatterplots/barplot/histogram/density plots
- Box Plot and outlier detection
- Visualization Linear relationship
- Univariate, Bivariate and Multivariate analysis
12. Database
Course Content
- MySQL
13. Statistics
Course Content
- Linear algebra
- Matrix operation and properties
- Introduction to calculus
- Theory of optimization
- Probability
- Conditional Probability
- Dependent and Independent events
- Bayes Theorem
- Descriptive / Inferential
- Variance / standard deviation
- Covariance And correlation
- Central Limit theorem
- Types of distributions
- pdf, cdf , pmf
- Confidence Intervals
- Hypothesis testing
- Z Test, t test, chi-2 test
- F-test/Anova
14. Introduction to Machine Learning
Course Content
- Introduction to Artificial Intelligence (DS, ML & DL)
- Applications of Machine Learning
- Categorization of Machine Learning
- Supervised / Unsupervised / Semi Supervised
- Parametric vs Non-Parametric
15. Supervised Learning (Regression/ Classification)
Course Content
- Linear Regression
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Stepwise Regression
- Bayesian Regression
16. Classification
Course Content
- Logistic Regression
- KNN
- SVM (Support Vector Machines)
- Decision Tree
- Naive Bayes
- LDA
- Classification for Imbalanced Dataset
17. Ensemble Learning
Course Content
- Random forest
- Adaboost
- Gradient boosting
- Decision Tree
- Xgboost
8. Other Support
Support Content
- Resume building
- Mock interviews
- LinkedIn account creation
- GitHub repository creation
- Migration of projects to GitHub
- Duration5 Months
- Enrolled150
- Lectures50
- Skill LevelEasy
- LanguageEnglish/Hindi
- Quizzes8
- CertificateYes
Pass Percentage80%
Reviews
Adrash
Great Python course! The lessons were easy to follow, and the hands-on projects helped me understand coding better. Highly recommend
Anurag
This Python course changed my career! From beginner to confident coder, the content was super helpful. Must take.