230+ Exercises – Python for Data Science – NumPy + Pandas
230+ Exercises – Python for Data Science – NumPy + Pandas, available at $54.99, has an average rating of 4.15, with 248 lectures, 235 quizzes, based on 49 reviews, and has 27263 subscribers.
You will learn about 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 This course is ideal for individuals who are data scientists or analysts who want to strengthen their Python skills specifically for data manipulation, analysis, and exploration using the NumPy and Pandas libraries or 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 or 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 or 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 or 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 or 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 It is particularly useful 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 or 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 or 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 or 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 or 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 or 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.
Enroll now: 230+ Exercises – Python for Data Science – NumPy + Pandas
Summary
Title: 230+ Exercises – Python for Data Science – NumPy + Pandas
Price: $54.99
Average Rating: 4.15
Number of Lectures: 248
Number of Quizzes: 235
Number of Published Lectures: 248
Number of Published Quizzes: 235
Number of Curriculum Items: 483
Number of Published Curriculum Objects: 483
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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.
Course Curriculum
Chapter 1: Tips
Lecture 1: A few words from the author
Lecture 2: Requirements
Lecture 3: Configuration
Chapter 2: —– NUMPY —–
Lecture 1: Intro
Chapter 3: Starter
Lecture 1: Solution 0
Chapter 4: Exercises 1-10
Lecture 1: Solution 1
Lecture 2: Solution 2
Lecture 3: Solution 3
Lecture 4: Solution 4
Lecture 5: Solution 5
Lecture 6: Solution 6
Lecture 7: Solution 7
Lecture 8: Solution 8
Lecture 9: Solution 9
Lecture 10: Solution 10
Chapter 5: Exercises 11-20
Lecture 1: Solution 11
Lecture 2: Solution 12
Lecture 3: Solution 13
Lecture 4: Solution 14
Lecture 5: Solution 15
Lecture 6: Solution 16
Lecture 7: Solution 17
Lecture 8: Solution 18
Lecture 9: Solution 19
Lecture 10: Solution 20
Chapter 6: Exercises 21-30
Lecture 1: Solution 21
Lecture 2: Solution 22
Lecture 3: Solution 23
Lecture 4: Solution 24
Lecture 5: Solution 25
Lecture 6: Solution 26
Lecture 7: Solution 27
Lecture 8: Solution 28
Lecture 9: Solution 29
Lecture 10: Solution 30
Chapter 7: Exercises 31-40
Lecture 1: Solution 31
Lecture 2: Solution 32
Lecture 3: Solution 33
Lecture 4: Solution 34
Lecture 5: Solution 35
Lecture 6: Solution 36
Lecture 7: Solution 37
Lecture 8: Solution 38
Lecture 9: Solution 39
Lecture 10: Solution 40
Chapter 8: Exercises 41-50
Lecture 1: Solution 41
Lecture 2: Solution 42
Lecture 3: Solution 43
Instructors
-
Paweł Krakowiak
Python Developer/Data Scientist/Stockbroker
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 0 votes
- 3 stars: 5 votes
- 4 stars: 12 votes
- 5 stars: 29 votes
Frequently Asked Questions
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