Requirements
- Basic computer skills + comfort with Excel-level data handling
- Helpful (not mandatory): basic Python syntax and math at school level
- Laptop/PC (8GB RAM recommended) + stable internet
- Willingness to practice 4–6 hrs/week (assignments + projects)
Features
- Live Project-Based Training
- Expert-Led Sessions
- Flexible Learning Options
- Interactive Learning
- Study Material
- Globally Recognized Certification
- One-on-One Mentorship
Target audiences
- 12th pass / undergrads aiming for data roles
- Working professionals: MIS, support, operations, finance, marketing
- QA/Developers moving towards analytics/ML
- Anyone preparing for Data Analyst / Junior Data Scientist roles
If you’re building a career in analytics or ML, Data Science with Python Training is one of the most practical paths because Python is what teams actually use for day-to-day work—cleaning messy data, automating reports, testing hypotheses, and shipping models. At Ascents Learning, this Data Science with Python Training is designed around hands-on practice, not slide-heavy theory.
You’ll work in Jupyter/Colab, write clean Python for data tasks, and get comfortable with NumPy and pandas for real datasets. We cover EDA, statistics that matter for decision-making, and visualization for explaining insights to non-technical stakeholders. Then we move into machine learning with scikit-learn—regression, classification, clustering, and model evaluation—using examples like customer churn, lead scoring, and sales forecasting. We also include mini-projects, weekly practice sets, and a capstone so your portfolio shows real work.
This Data Science with Python Training also fits job preparation: resume + LinkedIn support, mock interviews, and project reviews. If you want a course that helps you go from “I know Python” to “I can solve business problems,” Data Science with Python Training at Ascents Learning is built for that.
Curriculum
- 15 Sections
- 0 Lessons
- 10 Weeks
- Module 1: Data Science Basics + Workflow0
- Module 2: Python Fundamentals (for Data Work)0
- Module 3: Control Flow + Functions0
- Module 4: Working with Files + Environment Setup0
- Module 5: NumPy for Data Processing0
- Module 6: pandas Basics (Core Data Skills)0
- Module 7: Data Cleaning + Handling Missing Values0
- Module 8: Data Transformation + Feature Engineering0
- Module 9: Exploratory Data Analysis (EDA)0
- Module 10: Data Visualization (Storytelling)0
- Module 11: Statistics for Data Science0
- Module 12: SQL for Data Science (Industry Use)0
- Module 13: Machine Learning Foundations0
- Module 14: Supervised Learning (Regression + Classification)0
- Module 15: Unsupervised Learning + Capstone Project0