Python or R for Data Analysis: Which Should You Learn?
Is it better to learn R or Python for a career as a data analyst? Learn more about how to choose the best statistical programming language for your career goals.
February 4, 2025
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This course is part of Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
Instructor: Edureka
Included with
Recommended experience
Beginner level
No prior experience is required, but knowledge of statistics, and coding skills are recommended.
Recommended experience
Beginner level
No prior experience is required, but knowledge of statistics, and coding skills are recommended.
Write Python programs using core concepts like variables, data types, and control flow.
Apply NumPy and Pandas to manipulate and analyze data efficiently.
Create insightful data visualizations using Matplotlib, Seaborn, and Plotly for effective reporting.
Perform statistical analysis and probability tests to solve data-driven problems and validate hypotheses.
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This course introduces Python programming and fundamental statistics concepts, equipping learners with essential skills for data-driven roles in tech and AI. Through hands-on experience, you'll learn how to manipulate data, visualize insights, and apply statistical techniques for data analysis.
By the end of this course, you will be able to: - Understand and apply Python programming concepts such as data types, operators, and control structures - Manipulate data using popular libraries like NumPy and Pandas - Visualize data with Python libraries such as Matplotlib, Seaborn, and Plotly - Analyze data using statistical techniques, including measures of central tendency, dispersion, and probability - Perform hypothesis testing and draw insights from the data This course is designed for beginners, data enthusiasts, and aspiring data scientists who want to build a strong foundation in Python programming and statistical analysis. No prior programming experience is required, although familiarity with basic statistics will be helpful. Join us to start your journey into data analysis and programming with Python!
Welcome to Python and Statistics Foundations, the first course in the AI Exploration program's series! This module is designed to help learners take a significant step towards launching their careers in tech. In the first week, we'll explore how Python programming concepts are essential for creating efficient programs. Let's get started!
25 videos7 readings5 assignments1 discussion prompt
In the second week of this course, Learn how to manipulate data using NumPy and Pandas, working with various data formats. Gain proficiency in visualizing data using a range of charts and graphics.
26 videos4 readings5 assignments1 discussion prompt
In the third week of this course, we'll delve into statistics and probability. We'll explore measures of central tendency to handle various data inconsistencies. Additionally, we'll cover topics such as joint and marginal probability, as well as the fundamentals of hypothesis testing.
20 videos3 readings5 assignments
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Python programming concepts, Data manipulation with NumPy and Pandas with Statistical Analysis
1 video1 reading1 assignment1 discussion prompt
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This course provides an introduction to Python programming and essential statistical concepts. Designed for Python enthusiasts, it emphasizes practical skills for data manipulation and analysis, enabling learners to tackle real-world data challenges.
Learners will explore Python fundamentals, harness libraries like NumPy and Pandas for efficient data handling, and utilize visualization tools such as Matplotlib, Seaborn, and Plotly to present their findings. Additionally, the course covers key statistical methods and probability theory, equipping you with the tools to make data-driven decisions.
By the end of the course, you'll be well-prepared to apply these skills in various data analysis scenarios, setting a foundation for further studies in data science and artificial intelligence.
This course is designed for:
- Freshers looking to enter the fields of data analysis, data science, or artificial intelligence.
- Individuals with a keen interest in programming and statistics who want to enhance their technical skills.
- Professionals seeking to upskill in Python and data manipulation for practical applications in their careers.
- Anyone curious about data and eager to learn how to analyze and visualize it effectively.
Whether you're starting your tech journey or looking to build a strong foundation, this course will guide you through the essentials of Python and statistics.
The Python and Statistics Foundations course spans approximately 15 hours in total and is designed to be completed at a pace of 3-4 hours per week. This allows learners to absorb the material effectively while balancing other commitments.
The course utilizes Google Colab as the primary platform for coding operations. Learners may also use integrated development environments (IDEs) like Jupyter Notebook, PyCharm, Spyder, or VS Code for more extensive coding projects if desired.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.