What you'll get
- 19+ Hours
- 6 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Develop a strong proficiency in Python programming.
- Learn to code effectively using Jupyter Notebooks.
- Understand the core principles of programming.
- Create and manage variables in Python.
- Work with various data types, including integers, floats, booleans, strings, and more.
- Implement while() and for() loops to control program flow.
- Learn to install and manage Python packages.
- Understand statistical concepts such as the Law of Large Numbers and their application in programming.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Data Science with Python | 4h 14m | ✔ | View Curriculum |
| Statistics for Data Science using Python | 3h 23m | ✔ | View Curriculum |
| Advanced Python for IoT & IoT based Data analysis | 6h 29m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Forecasting the Sales of the Store Using Time Series Analysis | 2h 13m | ✔ | View Curriculum |
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
This course provides a structured, step-by-step guide to learning Python for Data Science, helping beginners avoid feeling overwhelmed. It takes a step-by-step approach, building on each concept through live examples and real-life analytical challenges. Students solve exercises during tutorials and receive homework to reinforce learning.
Participants will gain hands-on experience with Python for Data Science, including Exploratory Data Analysis (EDA), statistical techniques, and predictive modeling. Core Python libraries covered include NumPy, Pandas, Statsmodels, Scikit-Learn, Matplotlib, and Seaborn for data analysis and visualization.
The course covers essential Data Science and Machine Learning concepts, including bias, variance, overfitting, performance metrics, model evaluation, hyperparameter tuning, and grid search with cross-validation. Learners will work with classification, regression, and clustering models, exploring real-world scenarios and deployment use cases.
By the end of the course, participants can perform detailed data analysis, develop predictive models, optimize and evaluate them, and use Python programming to solve practical Data Science problems.
Requirements
- Basic computer skills, including the ability to install programs.
- A strong desire to learn Data Science.
- Prior knowledge of Python is helpful but not required.
Target Audience
- Individuals who want to learn programming in Python.
- Learners seeking a simpler, step-by-step approach to Python courses that are overly complicated.
- Those who prefer learning by doing with practical exercises.
- Participants who enjoy hands-on challenges and applied problem-solving.
- Learners willing to complete homework and exercises to reinforce their understanding.
