Learn Data Science From Scratch: Tools You Will Need
By: Royston Dm
Tags: Data Scientist, Python, Data Science, Learning, Tools

Learn Data Science From Scratch: Tools You Will Need

What is “data science”?

We’re entering a new world in which data may be more important than software.

— Tim O’Reilly

The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

Top basic tools to learn data science in 2021

When talking about data science tools, I am referring to the collection of tools that turn data into real-world actions. These include machine learning, database technologies, statistics, programming, and domain-specific technologies.

Becoming data scientist is hard. In any hard task, focus is critical. As a data scientist, Python should probably be the first tool you should master.

Kaggle, the community for data science competitions, publishes surveys of data scientist such as their “2020 Data Science and Machine Learning Survey“. Below, you can find the most popular tools from their survey:

Python, SQL and R are the top performers. As of most data scientists that use both R and Python strongly recommend Python as shown above. I expect Python’s popularity to continue to grow.

1. Jupyter Notebook:

Interactive environment to build, explore and model your data.

There were a ton of IDE options when it came to coding in Python. But in my opinion, Jupyter Notebooks are probably the best interface to use when starting out, especially when it comes to Data Science workloads. Unlike programmers or developers, data scientists have to deal with a lot of data types and need to visualize it each time we code to continue forward. They provide you with an interactive environment to build, explore and model your data. The easiest way I’ve found to have a stable operating environment for Jupyter is to use Anaconda.

There are a range of flavors when it comes to Jupyter as well, they all provide a similar working interface with a few pros and cons. For a FREE environment that allows you to use GPUs and TPUs. Take a look at Google Colab.

You can also use VSCode with Jupyter Notebook extension. I usually go with Jupyter Notebook with VSCode since it gives me more information and I can easily switch to other languages or programs if I want to.

2. Matplotlib:

Matplotlib makes easy things easy and hard things possible.

Matplotlib is one of the essential open-source graph plotting libraries for Python that any Data Scientist must know. Not only does it provide extensive customization options to the user but it also does so without over-complicating anything.

Anyone familiar with Python knows how powerful Python can be with its vast collection of libraries and integration with other programming languages.

With Matplotlib’s simple GUI, data scientists can create compelling data visualizations. Several export options make it easier for you to take your custom graph to the platform of your choice.

3. Microsoft Excel:

Better picture of your data.

Excel would be a simple spreadsheet management tool but, in the hands of power users such as data scientists, it becomes an extremely efficient tool. Excel is known for being a simple tool for newbies to get into but, once they figure out how powerful it is, they stay.

Excel presents data in such a way that makes decision-making at a glance effortless with its powerful data visualization style. The formulas offered by Excel act as the cherry on top as this transforms Excel from a mere number presenting application into something that can also process those huge numbers, be it concatenation, finding length, finding sum and average and hundreds more.