Course Introduction
Python, the most popular programming language today owing to its user friendliness founded by Guido van Rossum, is a key tool in data science. Data science involves extracting insights from data, and Python simplifies this process. Companies like Google and Facebook rely on Python for their data operations.
In this course, you’ll learn to analyze data, create visualizations, and draw valuable conclusions. With the growing demand for data-driven insights, completing this course opens doors to exciting job opportunities as a data science professional, making you a sought-after candidate in various industries.
Course Objective
This Course aims to empower participants with a comprehensive skill set essential for effective data analysis. Covering key concepts such as data manipulation, statistical analysis, and machine learning using Python, the course ensures proficiency in handling real-world data challenges.
Participants will gain hands-on experience in data visualization and interpretation, enabling them to derive meaningful insights. Upon completion, students will confidently apply operational-level skills in Python for data cleaning, exploration, and predictive modeling, positioning them as adept data scientists capable of addressing complex business problems through data-driven decision-making.
Key Topics
- Introduction to Python for Data Science
- Data Manipulation and Analysis with Pandas
- Statistical Analysis with NumPy
- Data Visualization with Matplotlib and Seaborn
- Machine Learning Basics with Scikit-Learn
- Real-world Data Projects
- Data Cleaning and Preprocessing
Scope & Career Opportunities
Python for Data Science unlocks diverse career opportunities. This course equips participants to analyze data, extract insights, and make informed decisions across industries. Become a sought-after professional, meeting the rising demand for data-driven expertise, and secure exciting opportunities in various fields.
Target Audience
Salary Range
Job Roles
- Data Enthusiasts
- Freshers/Professionals
- Researchers
- Data Analysts
- Maximum :25 LPA
- Medium:10 LPA
- Minimum:5 LPA
- Hike upto 50%
- Data Analyst
- Business Analyst
- ML Engineer
- Quantitative Analyst
Assessment Pattern
- Step-1: Go through the course curriculum & details
- Step-2: Apply through Website
- Step-3: Take mock test for level based personalized training
- Step-4: Take regular classes & assessments
- Step-5: Real time project
- Step 6: Resume Building & Mock Interviews
- Step-7: Internship
- Step-8: Placements
