Supercharge Your Data Science Skills with Exercises – Master NumPy and Pandas for Effective Data Analysis!
Description
The “230+ Exercises – Python for Data Science – NumPy + Pandas” course is an interactive, hands-on course designed for those who are seeking to gain practical experience in data science tools in Python, specifically the NumPy and Pandas libraries. The course contains over 230 exercises that provide learners with a platform to practice and consolidate their knowledge.
The course begins with NumPy, the fundamental package for scientific computing in Python, covering topics like arrays, matrix operations, statistical operations, and random number generation. Learners will practice the use of NumPy functionality through numerous exercises, gaining the proficiency needed for more complex data science tasks.
The course then transitions to Pandas, a library providing high-performance, easy-to-use data structures, and data analysis tools for Python. Here, learners will practice manipulating, cleaning, and visualizing data with Pandas, reinforcing skills necessary for real-world data science projects.
Each exercise is designed to reinforce key concepts and skills, building a strong foundation in handling numerical data and performing advanced data analysis tasks. At the end of the course, learners will have a deep understanding of these libraries and their applications to data science, enhancing their proficiency and readiness for further study or work in this exciting field.
This course is suitable for beginners in Python who have a basic understanding of programming concepts. However, professionals looking to refresh their skills or transition into a data-oriented role may also find it beneficial.
NumPy – Unleash the Power of Numerical Python!
NumPy, short for Numerical Python, is a fundamental library for scientific computing in Python. It provides support for arrays, matrices, and a host of mathematical functions to operate on these data structures. This course is structured into various sections, each targeting a specific feature of the NumPy library, including array creation, indexing, slicing, and manipulation, along with mathematical and statistical functions.
Pandas – Data Empowered, Insights Unleashed!
Pandas is a powerful open-source library in Python that provides easy-to-use data structures and data analysis tools. It is widely used by data scientists, analysts, and researchers for data manipulation, cleaning, exploration, and analysis tasks. Pandas introduces two primary data structures, namely Series (one-dimensional labeled array) and DataFrame (two-dimensional labeled data table), which allow efficient handling of structured data. With Pandas, you can perform various data operations such as filtering, grouping, sorting, merging, and statistical computations. It also offers seamless integration with other libraries in the Python data ecosystem, making it a versatile tool for data wrangling and analysis.
Who this course is for:
- data scientists or analysts who want to strengthen their Python skills specifically for data manipulation, analysis, and exploration using the NumPy and Pandas libraries
- students or individuals pursuing a career in data science or data analysis who want to gain hands-on experience with NumPy and Pandas, two essential libraries for data science in Python
- programmers or developers who are new to data science and want to learn how to use NumPy and Pandas for data manipulation and analysis tasks
- professionals working with large datasets or data analysis projects who want to leverage the power of NumPy and Pandas for efficient data processing and analysis
- Python developers interested in expanding their knowledge of data science and want to learn how to use NumPy and Pandas for data manipulation and analysis tasks
- self-learners or enthusiasts who are interested in data science and want to develop their Python skills for data manipulation and analysis using NumPy and Pandas
What you’ll learn
- solve over 230 exercises in NumPy and Pandas
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
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