Data Science tools: Armor for today’s Data Scientists

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Data-Scientists

There’s no doubt that data has taken over the world. The Industry is riding fast on data-driven strategies. Data is connected to every new move that industry is contemplating to implement. Each new business strategy, marketing campaign, product development, talent management, anddata operations.

One undisputable fact is – Data Scientists are behind all these initiatives.  To come up with actionable insights, strategies, and applications, data scientists and other data science professionals use a gamut of tools. The idea behind these tools is to unify data collection, analysis, and predictive modeling.

These tools are important for anyone looking to get into data science. Choosing the right tool for data science can make a huge difference in work performance and career development.

Here are top data science tools to learn in 2020

1. Apache Hadoop

Apache Hadoop is a prominent application in the Big data industry. It is known for its high parallel processing capability.

It means no matter how large a data is (extending to petabytes), complex computational and data-intensive tasks can be performed easily. To work comfortably with Hadoop, you will need to know HDFS (Hadoop Distributed File System), YARN, and Map Reduce. These are common modules that are frequently used.

Apache Hadoop is an open-source application. You’re free to use it.

2. Apache Spark

Spark comes with additional capabilities where Hadoop falls short. For instance, Spark offers in-memory processing, which makes it suitable for processing streamlining data. Hadoop lacks this. Spark inherits a few features from Hadoop such as HDFS, YARN, and Map Reduce. It also offers data cleansing, transformation, model building & evaluation. 

Like Hadoop, Apache Spark is an open-source application too. You’re free to use it.

3. Rapid Miner

Rapid Miner makes is robust application that unifies data preparation, machine learning and predictive model deployment. Rapid Miner assists right from initial preparation of data to analysis of the deployed model. It serves as a complete end-to-end tool for data scientists to carry out their tasks seamlessly.

Overall, in Rapid Miner, a data scientist can perform data preparation, visualize results, validate models, track and analyze data in real time.

4. Matplotlib

Matplotlib is a Python-based open-source graph plotting libraries.  Further, it provides extensive customization option for users to do so without making things complicated. It has a vast collection of libraries that integrate with other programming languages seamlessly.
Using Matplotlib, data scientists can create appealing data graphs. It also gives several export options that make transporting to other platforms easier.

5.  Big ML

Big ML is a prominent tool in the industry for building and sharing datasets and models easily.  It offers frequently used ML algorithms in one framework for reduced dependencies.

Using Big ML, data scientists can perform
– Classification
– Regression
– Time series forecasting
– Cluster analysis
– Anomaly detection
– Topic modeling


The easy-to-use GUI makes interactive visualization, decision-making a breeze for data scientists. BigML also supports REST APIs, which will help you get up and running in no time. You can export models via JASON PML and PMML seamlessly.

Read also – data science course in hyderabad

6. TensorFlow

Tensor is a go-to tool for data science professionals including data scientists, machine learning engineers, and AI Engineers. It serves a cross-functional tool in AI, DL, and ML.Tensor Flow is essentially a Python library that allows data scientists and machine learning engineers to build, train, and deploy models on a diverse set of platforms such as computers, smartphones, and servers to achieve maximum potential with limited resources.

You can create statistical models, data visualizations, and access top-notch and widely-used ML and DL features.  Tensor Flow is based on Python, which makes it easy to store, filter, and manipulate, data for distributed numerical computations. 

7.  Tableau
This is the most sophisticated data visualization tool in the data science industry. The tool makes data visualization a breeze. Unlike libraries where you need to ready data for visualization, using Tableau is comparatively easier. The tool uses online analytical processing, cloud databases, spreadsheets and relational databases. You can get started with data visualization in no time.

Tableau’s offerings include —
1. End-to-end analytics
2. Sophisticated data calculations
3. Highly secure system
4. Highly responsive user interface that adapts to all devices and screen dimensions

You can choose to pursue data science certifications and courses to learn these prominent tools, or take a self-learning approach and master these tools.  

Conclusion

Data science is increasingly becoming simpler. However, it doesn’t the role of data scientists will become simpler, instead data operations across the industry will become simple, thanks to number of new and emerging tools. For anyone who is looking to grow in a data science career, learning these and emerging new tools will be essential.

How many of these data science tools do you already know? Mention in the comment below.

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