9 Best Data Science Tools For Data Scientists 2020

What are the best data science tools in 2020?

As a data science professional, your duty is to organize and analyze huge data sets in order to extract value from them.

By being able to assess and analyze this data you’ll be able to gain insights into the market and improve your company products.

In order to extract, manipulate, process and generate predictions from this data, you’ll need to employ various statistical tools and programming languages.

In fact, I wrote another article on the best programming languages for data science that you should check out.

So, in this article we are going to look at the best data science tools every data scientists must try.

Through the use of these top data science tools, you’ll be able to handle, analyze big data sets and derive useful insights from them.

Along the way we’ll look into the key features that make them stand out as the top data science tools as well as the benefits they bring to you as a data scientist.

Even though there are many other data science tools specialized for different tasks, I’ll limit myself to the most common tools.

Let’s get started.

1. Hadoop

Hadoop, also known as Apache Hadoop, is an open source data science tool for storing and manipulating huge data sets on clusters of stock hardware.

It is one of the top data science tools that enables you storage of massive sizes of both structured and unstructured data.

Coupled with monstrous processing capacity, it is capable of running virtually limitless numbers of tasks.

It also comes with many other popular data science frameworks that can be used with Scala or Java.

2. TensorFlow

TensorFlow, named after multidimensional arrays of tensors, is one of the best data science tools that is very popular among data science engineers.

It is a data science tools that implements machine learning algorithms like deep learning to an elevated level of efficiency and capability.

Due to its high processing capabilities, it has a wide range of applications including language recognition, image classification and discovery of medicines.

Because if its efficiency, it’s common among beginners, researchers as well as professional data scientists.

Here are some TensorFlow tutorials online to get you started with this amazing data science tool.

Through TensorFlow, you can harness the power of deep learning without requiring the knowledge of some of the sources behind it.

3. Spark

Spark, also known as Apache Spark, is another very popular data science tool designed for handling batch and stream processing.

It is considered to have enhanced speed over Hadoop and is common among beginner level learners of data science.

In fact performance benchmarks prove that it can perform up to 100x faster than MapReduce.

Click here for beginner friendly Apache Spark tutorials to get you started.

What makes it one of the best data science tools is because it enables the user of multiple programming languages like Java, R, Python and Scala.

Spark also comes with numerous APIs that enables data scientists to make accurate data predictions.

4. Matlab

Matlab is a closed-source multi paradigm numerical computing environment that facilitates matrix, algorithm and statistical information modeling.

It is one of the top data science tools used for neural networks and fuzzy logic simulations.

You can also use the Matlab graphics library to general powerful data visualizations.

Apart from that, it can also be used in data science for image and signal processing, data cleaning and analysis and development of advanced deep learning algorithms.

Matlab allows for easy and simple integrations for the various enterprise applications which increases its market demand.

5. SAS

SAS is a closed source data science tool designed for statistical operations that is used by large companies to analyze data.

Because it is expensive and has considerably less amount of libraries in its base pack, it’s preferred mostly by big corporates as a data science tool.

It, however, has a wide range of data science applications including advanced analytics, business intelligence, multivariate analysis and predictive analysis.

Even though relatively few, it has a useful array of statistical libraries and tools that are great for data organization and modeling. 

6. Microsoft Excel

Excel is another popular data science tool developed by Microsoft that is designed for spreadsheet calculations.

It is also used for visualization, processing and calculations involving complex data.

In fact, it is one of the most used, if not the most used, data analysis tools among data scientists because of the powerful features it comes packed with.

Apart from coming with various formulas for manipulating tables and applying filters, it also lets you create your own custom formulas and functions.

While not a great tool when working with massive data sets, Excel has been traditionally great for creating powerful visualizations and spreadsheets.

7. Tableau

Tableau is one of the top data science tools that is packed with great graphics used for creating interactive data visualizations.

It’s mostly focused on business intelligence and interfaces well with databases, spreadsheets, OLAP cubes among others.

Through these Tableau tutorials online, you’ll not only learn how to use it for visualizations but also how to use its analytics tools for analyzing data.

While Tableau is a paid enterprise software, it comes with a free version for you to get started with called Tableau Public.

Operating Tableau does not require technical programming skills and is great for data collaboration and real time data analysis.

8. BigML

BigML is one of the best data science tools that provides an interactive cloud-based GUI environment for processing machine learning algorithms.

Some of the machine learning algorithms it uses include clustering, classification, time-forecasting etc.

It is popular mostly because of its amazing Predictive Modeling feature.

This top data science tool enables companies to use and implement different machine learning algorithms in various business functions like sales forecasting, risk analytics, product innovation among others. 

It also comes out of the box with a variety of automation techniques that will enable you automate your workflows.

9. Matplotlib

Matplotlib is a popular data science tool mostly used by data scientists for data visualization.

Or simply put, it is a plotting and visualization library developed for Python and NumPy, used for generating graphs for the analyzed data.

Its popularity is based on its ability to plot complex graphs using very simple lines of code.

In fact Matplotlib is the preferred data visualization tool for generating bar plots, bar divisions, histograms, scatterplots among other kinds of charts or graphs.

It has various essential modules and is the recommended data science tool for beginners who want to learn data visualization using Python.

Conclusion

Data science has been consistently ranked as the most paying career in tech for the last three years.

It is an exciting and rewarding career option for you if you are interested in extracting, manipulating and generating insights from massive volumes of data.

However, to fully leverage the power of data science and make better business decisions, you need to use the best data science tools.

There are numerous data science tools, varying from those used in data collection, storage, visualization etc…

It can be overwhelming when you want to choose the best data science tool for you.

So, I hope this list of the top data science tools in 2020 has helped you narrow down on the particular tools you need to get your data science career started.

Using these top data science tools, you’ll be able to extract, process and analyze complex data and derive useful insights from them.

But in order to be able to make the most of these data science tools, you’ve got to first learn how to use them.

That’s why I put together some of the best data science tutorials that will not only teach you the fundamentals of data science, but will also give you hands-on skills on how to use these tools to analyze data.

Data science is a practical skill, and what’s a better to learn data science than to learn by doing?

Through these courses you’ll learn how to integrate machine learning and artificial intelligence into your data science tasks.

Have you learnt data science before?

What is your experience with some of the top data science tools that I have mentioned in this list?

Please share your thoughts in the comments below.

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Author: Larissa Nunes

I'm Larissa Nunes, a business analyst with a background in big data and business intelligence tools. So I like researching and writing about data science, machine learning and everything in between. A few times I have given talks on artificial intelligence...

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