The Ultimate Guide to Python and R for Data Analytics
Having a career in data science and analytics requires a mathematical and statistical ability to solve problems. It is also a field based on understanding programming languages and data visualization tools. Learning Python and R is not hard. It depends on the individual and the choice of programming language.
Similarly, the right programming skills, appropriate software, and student-focused tutorials can accelerate the learning journey. If you’re trying to learn Python and R for data science and analytics or looking to make a career change from non-tech to data science, you need the proper learning roadmap. Rolai provides the foundation to help you learn Python and R quickly and land you your next high-paying data analyst job.
What Is Python and R in Data Analytics?
Data Analytics involves collecting, organizing, and analyzing raw data to extract actionable insights. Python is a popular programming language with a vast library collection that effectively handles complex data sets and analytics.
R is a programming language and software environment for statistical analysis, data visualization, and reporting. We’ll discuss libraries in Python and R, including Pandas, NumPy, Dplyr, and ggplot2, etc., to get you in the learning groove.
Python adds impressive functionality and flexibility to data analytics tools. It helps to learn Python early in a data analytics career because it is compatible with several other languages. In addition, Pandas, one of Python’s libraries, can be used to create and manage data structures speedily.
R is primarily for statistical computing and graphics. It is commonly used to clean, analyze, and graph data. Data scientists and analysts use R to estimate and display results. This makes it an important programming language to learn for aspiring data scientists and analysts.
R vs. Python: Which Should I Learn?
There has always been a widespread debate about R vs. Python. But there shouldn’t be competition. A combination of Python and R programming knowledge only opens students to more problem-solving capabilities, which is a huge advantage.
In a data-polarized world, where accuracy is critical, R programming helps to identify patterns and create practical models of data structures. With this knowledge, you possess a highly valuable skill for analyzing organizations’ data and proffering software solutions for accurate statistical analysis.
Python is easier to learn due to its easy-to-read syntax. Although R may be difficult initially, it gets easier when you understand its features and uses.
R offers more packages and tools for statistical calculation and data visualization in data analysis. Python takes the easy-to-learn route and gives thousands of libraries for aspiring data analysts working with deep learning algorithms and artificial intelligence. As one of the most popular programming languages, Python also proves helpful in data manipulation, as you can learn in this comprehensive Rolai course.
R language is the best programming for Data Analysts dealing with statistics and probabilities. Plus, a large community of industry statisticians is readily available to answer your questions.
Python and R offer several opportunities to solve different data science and analytics problems while giving students a pool of distinct career perspectives. If you’re wondering what your job opportunities will be like after learning these programming languages, here are career perspectives on learning Python and R.
Career Perspectives of Learning Python and R
Python and R offer several skills to improve an aspiring data scientist and analyst. There are many career paths, including Data Analyst (Data Analyst – Rolai), Data Scientist (Data Scientist – Rolai), Machine Learning scientist (Machine Learning Scientist – Rolai), and Python Programmer (Python Programmer – Rolai). Here are more details on these career options:
Data Analysts can seek high-paying jobs when they have the skills to practically use the R language in the statistical and data visualization realms of data analysis.
After taking professionally guided courses on the fundamentals of Python and R, aspiring analysts will better understand the scope of the data space, which will help them analyze complex business problems.
Flexibility is one of Python’s selling points. With a certificate in Python and R, Data Scientists can take on models with machine learning.
This arsenal of skills is rewarding in the contemporary world, where unique talents are needed for high-pay tasks such as writing Python scripts and automating several data-related methods and procedures.
Machine Learning Scientist
Python brings data to life. Students learn to build data pipelines with modern tools, visualize data for accurate interpretation and create interactive and intuitive dashboards. Machine learning with R takes on the same prospects but leans towards statistical analysis.
Python Programming is a highly in-demand skill in modern software development. Large organizations are constantly looking for candidates who can demonstrate proficient Python programming knowledge.
Data Visualization stands as an explorable career perspective as well. The global data visualization market is expected to grow at a CAGR of 9.69% between 2020 to 2027.
Exploratory Data Analysis
R programming offers the skills to simplify complex data structures using graphical and numerical techniques. This is particularly useful for the exploratory data analysis space. As an aspiring data scientist, combining it with data visualization skills can help you:
- Gather information about features or critical characteristics of a data set.
- Capture variables that are essential and useful in problem-solving.
- Gather knowledge about variables and their relationships.
NumPy, for example, is a tool in the Python library that simplifies descriptive statistics. This opens a career path in statistics, where you can handle not-too-large data sets with the right approach and arrive at an accurate endpoint.
However, R language is more beneficial here as it simplifies statistical analysis and data visualization. Get ready to dabble into another world of statistics and divulge complex data.
Common Questions in Data Analytics
How Can I Learn Programming in Python and R?
The best way to learn Programming in Python and R is to have the right development software and Python tutorial guide. Rolai’s experimental learning provides a faster route to learn Python and R through their three ultimate goals; Learn, Apply, and Solve.
How Long Does it Take to Learn Python and R?
You can expect to take 8 – 10 weeks to learn basic Python and R. This covers syntax, if statements, links, variables, loops, and data types.
Which Software Should I Use for Coding?
There are several software programs for coding. Atom Code Editor, Sublime Text, and Notepad++ are some that work.
Which Free Resources do you Recommend?
Some free resources for scaling in Python and R are Microsoft Visual Studio 2022, InventwithPython, and Awesome Python (on Github). If you want a guided learning roadmap, Rolai offers a simplified route to quickly becoming a data scientist and analyst.
Which Books Should I Read?
What Skills are needed for Python and R, and where can I build them?
The skills needed for Python are Machine Learning and AI, understanding of Web Frameworks, understanding of Multi-Process Architecture, Deep Learning, and Analytical Skills.
Students can build these skills through reliable online Python tutorials, such as on Rolai, where you’ll also get an industry-recognized certificate upon completion. This will help you:
- Develop job-ready skills.
- Stand out in the hiring process.
- Learn from experts in the industry and a helpful community of like-minded people.
- Have a comprehensive assessment framework.
How can I be Consistent in Learning Python and R?
Consistency begins with sticking to learning schedules. Like Rolai’s learning structure: Learn, Apply, and Solve, it gets easier as practice.
You learn the fundamental and technical skills needed in Python and R. Then, apply these skills immediately to data sets. And solve real-world problems in live contests with a global community of more than 200,000 other learners like you.
What are the main Libraries to Learn in Python and R?
Numpy, TensorFlow, sci-kit-learn, Theano, Pandas, Dplyr, Ggplot2, Esquis, etc., are libraries in Python and R.
What are some of the Python and R Communities I can Join?
Among growing and intuitive Python and R communities you can join are; PySlackers, Full Stack Python, NHS Python, HackerEarth, Dream in Code, etc.
Will Python and R be in Constant Demand in the Future?
Yes, very much so. Employment of Data Scientists is expected to take a 36 percent leap from 2021 to 2031, according to the U.S Bureau of Labour Statistics. Python and R are essential skills to thrive in the Data Science world.
Which Websites can I use to see other code and learn?
Leetcode and Github are reliable websites that let you see other developers’ codes and learn.
Jobs After Learning Python and R
Learning Python and R opens the door to high-pay jobs. A data analyst in the U.S. makes $71,601 in November 2022, according to Glassdoor. You can also expect to earn closer or more with varieties of lucrative programming and data science jobs. This gets higher for Fortune 500 entry-level data scientists or analysts roles with an industry-standard certificate in Python and R.
Other jobs you can apply to after learning Python and R are:
- Software Engineer
- Full Stack Developer
- Python and R Developer
- DevOps Engineer
- Data Scientist
- Machine Learning Engineer
- Application Architect
- Enterprise Architect
- Data Engineer
- Business Intelligence (BI) Developer
Python and R are two in-demand skills for a lucrative job in 2022 and beyond. These two languages offer proactive and industry-standard tools and techniques to handle enormous data sets and reach accurate analysis. An R or Python certification is the leap you need to begin a lucrative data science and analytics career. You can learn Python and R for data analytics free on Rolai and earn industry-recognized certifications in record time. Learn with a complete content library, including used cases and projects. Start your professional data analytics journey for free now.