What you'll get
- 23+ Hours
- 8 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Covers practical methods for examining, interpreting, and presenting data using widely adopted Python libraries.
- Provides one year of uninterrupted access, allowing learners to progress at their own pace.
- Suitable for individuals committed to mastering predictive modeling and building a professional path in data analytics.
- Assumes prior understanding of fundamental statistical principles and basic predictive modeling concepts.
- Award a recognized course completion certificate upon successful completion of the program.
- Issue verifiable certificates for each course and project, each supported by a unique verification link that can be shared on resumes or professional networking profiles to highlight validated skills.
- Delivered as a self-paced video-based training program, enabling flexible and independent learning.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Predictive Modeling with Python | 8h 26m | ✔ | View Curriculum |
| Data Science with Python Project-Predict Diabetes on Diagnostic Measures | 1h 02m | ✔ | View Curriculum |
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| Financial Analytics with Python | 1h 6m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
| Forecasting the Sales of the Store Using Time Series Analysis | 2h 13m | ✔ | View Curriculum |
Description
The Python Predictive Modeling course empowers learners to work with data, develop predictive models, and uncover actionable insights through Python. The program focuses on applying statistical concepts and machine learning techniques to real-world datasets, enabling learners to transform raw data into actionable predictions. Through structured lessons and practical demonstrations, participants gain hands-on experience with industry-relevant Python libraries used in data analytics and modeling.
This course is well-suited for students, graduates, and working professionals who aspire to build or transition into careers in data analytics, data science, or related domains. By the end of the training, learners develop the confidence to interpret data patterns, create predictive solutions, and apply these capabilities across various business and analytical use cases.
Sample Certificate
Requirements
- A strong grasp of statistical fundamentals is highly beneficial when beginning predictive modeling with Python, as it enables accurate interpretation and validation of data insights.
- Learners are expected to demonstrate proficiency in Python programming, as it serves as the primary language for building and implementing predictive models.
- Familiarity with SQL is an added advantage, enabling efficient data handling and analysis.
- Even learners who lack some of these skills can succeed, as consistent effort, dedication, and a willingness to learn can bridge knowledge gaps over time.
Target Audience
- The Predictive Modeling with Python program is suitable for individuals with a genuine interest in data-driven analysis, regardless of their current career stage.
- Beginning early allows learners to build deeper expertise and gain a stronger advantage as they progress in this domain.
- Students pursuing statistics and graduates from computer science or related disciplines can effectively align their career paths with predictive analytics through this course.
- As predictive modeling remains a highly sought-after capability, many IT professionals leverage these skills to transition into analytics-focused roles.
- Practical expertise in predictive analytics enables access to a wide spectrum of professional roles, including Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, and Statistician.
