Last updated on April 6th, 2024 at 08:10 pm
A career in data science requires in-depth knowledge of various software tools and programming languages. Languages like R, Python and SQL offer massive advantages that one can use for efficiently conducting data analysis.
This article will discuss in detail the role each language carries while we delve into the advantages of a data science programme. For instance, the programmer can utilise R and SQL for complex queries and tables while conducting fundamental statistics. On the other hand, one can easily use Python objects to create and automate tasks while working on various data sets.
Python in Data Analysis
Python is a powerful tool in data analysis as it provides a colossal library count that can be used for data visualisation and manipulation.
A. Advantages of Python in Data Analysis
- Easy Programming Language: Data analysis is a large venture requiring a lot of work with every task. With Python objects, syntax and code can be written straightforwardly.
- Extensive Library Count: Python's standard library allows for complex data analysis tasks like data manipulation, statistics, and data visualisation to be done with ease.
- Open-source Feature: Since Python can be modified, it is free for users to access and write, making it the ideal choice for different data analysis projects.
- Community Support: Python boasts a large group of developers available anytime to answer and help each other with queries.
B. Popular Libraries and Frameworks Used in Data Analysis
Python utilises many libraries like TensorFlow and Scikit-learn to exercise machine learning algorithms. Other libraries and frameworks include Keras, Pandas, PyTorch and Matplotlib.
Pandas uses the 'sort_values' function to make way for the action. In this example, we can see the popular instance of listing items and their prices arranged in descending order in store 1. The prerequisite ‘Pandas’ from the Python library has been used for this particular action.
items[items.store_id == 1][['description','price']]\
.sort_values(by='price', ascending=False)
|
Store ID |
Description |
Price |
|
2 |
zucchini | 7.45 |
|
1 |
orange |
1.45 |
|
3 |
pear |
1.45 |
|
1 |
butter |
1.40 |
|
8 |
onion |
1.35 |
| 1 | celery |
0.75 |
SQL in Data Analysis
A. Advantages of SQL in Data Analysis
SQL's performance is commendable as it can be used to query and manipulate the data present in the database. SQL can also create numerous reports and dashboards for visualising data.
- High Performance: SQL is widely known for its efficient form that aids in offering faster results than other programming languages.
- Secure Database: SQL's most relevant feature is high security for storing and retrieving data which can be used against unauthorised access and malicious attacks.
- Scalability: SQL is known for holding a substantial amount of databases that can store more data over time.
B. Popular SQL Programming Database Management Systems Used in Data Analysis
SQL utilised a range of DMS systems for analysing data effectively. These include MySQL, PostgreSQL, MSSQL, MariaDB and Oracle.
This list shows how MySQL has been implemented for acting:
mysql> SELECT DESCRIPTION, PRICE
-> FROM ITEMS
-> WHERE STORE_ID = 1
-> ORDER BY PRICE DESC;
|
Description |
Price |
|
zucchini |
7.45 |
| orange |
1.45 |
|
pear |
1.45 |
| butter |
1.40 |
| onion |
1.35 |
|
celery |
0.75 |
R in Data Analysis
R is an essential language for data analysts as it helps create robust data structures and visualisations.
A. Advantages of R in Data Analysis
- Cost-effective Features: R's libraries and frameworks are open-source and free to use, making them an excellent option for greater accessibility in data analysis.
- User-friendly Visualisation Tools: R comprises various user-friendly visualisation tools that rapidly form graphs and charts.
- Flexibility: R's vivid tools can analyse many data types — text, audio and images.
B. Popular R Libraries and Packages Used in Data Analysis
The most common and widely used R frameworks for analysing data include dplyr, tidyr, Shiny, plotly, XGBoost and data.table.
In this example, you can see the data.table format:
> items[store_id == 1, .(description, price)][order(-price)]
|
Store ID |
Description | Price |
|
1 |
zucchini | 7.45 |
| 2 | orange |
1.45 |
|
3 |
pear | 1.45 |
|
4 |
butter |
1.40 |
| 5 | onion |
1.35 |
| 6 | celery |
0.75 |
Conclusion
All three languages mentioned above have a substantial role in data analysis as they offer numerous functions for managing and manipulating data effectively. While R is a powerful statistical language, SQL programming is a database query for storing databases. Furthermore, Python's general-purpose language can be accessed for machine learning purposes.
A career in data science can be gratifying, especially when using your technical skill sets. It can be especially beneficial while forming simple descriptive statistics or creating complex machine learning models. Opt for Imarticus’s Post graduate program in Data Science and Analytics while you work on amplifying your resume.
