Data science tools are becoming increasingly popular as the demand for data scientists increases. However, with so many different tools, knowing which ones to learn can be challenging
In this blog post, we will discuss the top 7 data science tools that you must learn. These tools will help you analyze and understand data better, which is essential for any data scientist.
So, without further ado, let’s get started!
List of 7 data science tools
There are many tools a data scientist must learn, but these are the top 7:
- R Programming
- Apache Spark
And now, let me share about each of them in greater detail!
Python is a popular programming language that is widely used in data science. It is easy to learn and has many libraries that can be used to analyze data, machine learning, and deep learning.
It has many features that make it attractive for data science: An intuitive syntax, rich libraries, and an active community.
Python is also one of the most popular languages on GitHub, a platform where developers share their code.
Therefore, if you want to learn data science, you must learn Python!
There are several ways you can learn Python:
- Take an online course: There are many online courses that you can take to learn Python. I recommend taking several introductory courses to familiarize yourself with the basic concepts.
PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.
- Read a book: You can also pick up a guidebook to learning data science. They’re usually highly condensed with all the information you need to get started with Python programming.
- Join a Boot Camp: Boot camps are intense, immersive programs that will teach you Python in a short amount of time.
Whichever way you learn Python, make sure you make an effort to master the language. It will be one of the essential tools for your data science career.
2. R Programming
R is another popular programming language that is highly used among statisticians and data scientists. They typically use R for statistical analysis, data visualization, and machine learning.
R has many features that make it attractive for data science:
- A wide range of packages
- An active community
- Great tools for data visualization (ggplot2)
These features make it perfect for scientific research!
In my experience with using R as a healthcare data analyst and data scientist, I enjoyed using packages like ggplot2 and tidyverse to work on healthcare and biological data too!
If you’re going to learn data science with a strong focus on statistics, then you need to learn R.
SQL (Structured Query Language) is a database query language used to store, manipulate, and retrieve data from data sources. It is an essential tool for data scientists because it allows them to work with databases.
SQL has many features that make it attractive for data science: it is easy to learn, can be used to query large databases, and is widely used in industry.
If you want to learn data science involving big data sets, then you need to learn SQL. SQL is also commonly used among data analysts if that’s a career you’re also considering exploring.
There are several ways you can learn SQL:
- Take an online course: There are plenty of SQL courses online. I’d pick one or two of them to start with
- Work on a simple SQL project
- Watch YouTube tutorials
- Do SQL coding questions
Java is another programming language to learn as a data scientist. Java can be used for data processing, analysis, and NLP (Natural Language Processing).
Java has many features that make it attractive for data science: it is easy to learn, can be used to develop scalable applications, and has a wide range of frameworks commonly used in data science. Some popular frameworks include Hadoop and Kafka.
There are several ways you can learn Java:
- Work on a project
- Practice using programming exercises
5. Apache Spark
Apache Spark is a powerful big data processing tool that is used for data analysis, machine learning, and streaming. It is an open-source project that was originally developed at UC Berkeley’s AMPLab.
Apache Spark is known for its uses in large-scale data analytics, where data scientists can run machine learning on single-node clusters and machines.
Spark has many features made for data science:
- It can process large datasets quickly
- It supports multiple programming languages
- It has high scalability
- It has a wide range of libraries
If you want to learn big data science, then Apache Spark is a must-learn. Consider taking an online course or watching a webinar on big data to get started.
TensorFlow is a powerful toolkit for machine learning developed by Google. It allows you to build and train complex models quickly.
Some ways TensorFlow is useful for data science:
- Provides a platform for data automation
- Model monitoring
- Model training
Many data scientists use TensorFlow with Python to develop machine learning models. TensorFlow helps them to build complex models quickly and easily.
If you’re interested to learn TensorFlow, do consider these ways:
- Read the official documentation
- Complete online courses
- Attend a TensorFlow meetup
However, to learn and practice your Tensorflow skills, you’ll need to pick up decent deep learning hardware to support the running of your algorithms.
Git is a version control system used to track code changes. It is an essential tool for data scientists because it allows them to work on projects collaboratively and keep track of their work.
Git is useful in data science for:
- Tracking changes in code
- Allowing collaboration on coding projects
- Keeping track of work
If you’re planning to enter data science, Git is a must-know tool! Since you’ll be coding a lot in Python/R/Java, you’ll want to master Git to work with your team well in a collaborative coding environment.
Git is also an essential part of using GitHub, a code repository platform used by many data scientists.
To learn Git, I’d recommend just watching simple tutorials on YouTube.
And these are the top seven data science tools that you must learn!
The most important thing is to get started and keep upskilling yourself! There is no one-size-fits-all solution in data science, so find the tools that work best for you and your team and start learning.
I hope this blog post has been helpful in your journey to becoming a data scientist. Happy learning!