Course Overview

In our AICOACH course on data analysis with Pandas, students will master essential data manipulation techniques using the powerful Pandas library in Python. Through hands-on exercises and real-world examples, participants will learn how to efficiently clean, transform, and analyze datasets, conduct advanced statistical operations, and create insightful visualizations. By the end of the course, students will possess the skills to tackle diverse data analysis challenges and extract valuable insights from complex datasets, empowering them to make informed decisions in their professional endeavors.

Highlights

1. Real-Time Experts
2. LIVE Project
3. Certification
4. Online training at Flexible times
5. Affordable Fees
6. Placement Support
7. Class recordings
8. Use AI tools and save 3 hours of your daily time.
9. The salary will grow three times as fast.
10. Unlock bonuses worth ₹50,000
11. Resume-Building Support
12. Mock interviews

Why should you learn data analysis with Pandas course?

1. Learning data analysis with Pandas opens up abundant opportunities worldwide.
2. Data-driven decision-making is ubiquitous across industries, creating a high demand for skilled data analysts.
3. From finance to healthcare, marketing to technology, virtually every sector relies on data analysis to drive growth and innovation.
4. Pandas proficiency equips you to explore diverse career paths, from data scientist to business analyst, consultant to researcher.
5. The average salary per year for data analysts proficient in Pandas is:
6. In the United States, the average salary ranges from $70,000 to $120,000, depending on factors like experience, location, and industry.
7. Globally, salaries vary but tend to align with the demand for data analysis skills, offering competitive compensation packages.

Course Content

1. Course Introduction
2. Environment Set-Up
3. Python Crash Course
4. Hands-on Variable Types and Operators
5. Hands-on-Decision-Making-If-Else
6. Python loops explained
7. Hands-on, while loops
8. Hands-on-For Loops
9. Python lists, tuples, and dictionaries explained
10. Hands-on-Lists Operations
11. Hands-on-Tuples Operations
12. Hands-on-Dictionary Operations
13. List Comprehensions
14. Lambdas
15. NumPy Crash Course
16. Pandas Series Crash Course
17. Hands-on different series methods
18. Data Frames in Depth
19. Hands-on on different Pandas DataFrame methods
20. Working with multiple data frames
21. Hands-on, different joins
22. Going Multidimensional
23. GroupBy and Aggregates
24. Reshaping with Pivots
25. Handling Date and Time
26. Regex and text manipulation
27. Visualizing Data with Different Graphs
28. Reading different data formats for input and output files