Must-Know Data Science Tools In 2022

Data science is a growing field, and it's one of the most in-demand careers right now. This means you have many options when looking for jobs or considering a career change. However, it also means that competition is fierce. Therefore, you need to be able to differentiate yourself from other candidates by showing off your skills. If you're determined to become an expert data scientist, then having these ten tools on hand will help you get there:

TensorFlow

TensorFlow is an open-source software library for numerical computation using data flow graphs. It is used mainly to perform deep learning and machine learning, initially developed by the Google Brain team for internal Google use.

It was initially released as an open-source project in November 2015. Then it was announced in March 2016 that TensorFlow was the most popular deep learning framework at Kaggle's World AI Competition 2016. So in 2017, Google donated TensorFlow to the newly established non-profit TensorFlow Foundation.

Tableau

Tableau is used for data visualization and is a business intelligence tool that allows you to create dashboards, reports, and visualizations quickly. It is used by companies in many industries, including finance, telecommunications, retail, and government.

Tableau can be used by non-technical users who wish to explore their data interactively. In addition, tableau integrates with most BI tools like Microsoft Power BI, Salesforce Analytics Cloud (SAC), Google BigQuery, etc., and other databases such as Amazon Redshift and Azure SQL Database.

Apache Hadoop

Apache Hadoop is a software framework for writing applications that process vast amounts of data in a parallel fashion. It was designed to scale up from a single server to thousands of machines, each offering local computation and storage. Rather than relying on high-end hardware, the power of Hadoop comes from having lots of cheap processors working in parallel. The name refers to it being both the elephant (from Yahoo! where the project started) and a toy elephant (a popular children's toy).

Hadoop can be used for any data processing or analysis. Moreover, it is incredibly well suited for unstructured data sets because it includes tools that make storing and accessing this information accessible. It also supports various programming languages, including Java, Python, and R.

Microsoft Excel

Microsoft Excel is one of the most popular data science tools because it's easy to use and can be used by users of all experience levels, including beginners and experts, making it an excellent tool for learning data science.

The following are some examples of how Microsoft Excel can be used in data science:

• Data cleaning: You can use Microsoft Excel to filter out any unnecessary values or fill in missing ones so that you don't have errors later in your analysis.
• Data analysis: You can create graphs and charts to represent your raw data before processing it further with other tools such as Python scripts or R libraries like Pandas or ggplot2. Therefore it helps you spot patterns easier than looking at numbers on a plain page would do alone.
• Machine learning models: If you want a machine learning model that works well but doesn't require much effort from yourself, then using a pre-programmed template could be helpful. However, if you want something custom made for yourself, then creating one from scratch may be necessary but this means learning about ML algorithms before even starting anything else.

Jupyter Notebook

Jupyter Notebook is a web-based application that gives you the ability to generate and share documents that contain live code, equations, visualizations, and narrative text.

You can use Jupyter Notebook to create documents that contain code, equations, and visualizations. In addition, the Jupyter notebook allows you to create a document from scratch or convert existing files from other formats such as Markdown or Microsoft Word into the Jupyter format. It also allows users to share their work through a URL link so others can view it on their computers or tablets without downloading anything locally.

Jupyter notebooks have become very popular for data scientists because they allow them to organize their notes in one place. As a result, users don't have to scroll up/down when reading back through them later (which would happen if all your notes were just stored digitally). In addition, you can use these notebooks as teaching materials because they provide some context while still providing an overview of what was going through your head when writing down those thoughts. For example, concepts that might not make sense right away but, after reviewing again, could help understand why certain decisions were made based on previous knowledge gained during these sessions.

Microsoft Power BI

It is a business analytics service that is cloud-based, allowing users to access, analyze and visualize data. It's also a suite of tools that helps organizations gather data from any source (including on-premises servers), prepare the information for analysis, create models and reports, share their knowledge with employees across the organization, and more.

Power BI is part of the Microsoft Cloud; it integrates with other services in that cloud like Azure Machine Learning, Analysis Services (formerly known as Data Warehouse), or Stream Analytics (formerly known as Azure Stream Analytics).

Microsoft uses Power BI internally to analyze its data, to gain insights into internal trends such as employee performance or customer satisfaction and then shares these findings with employees through dashboards and reports to make better decisions based on those insights.

Python

Python has many data science applications as it is a high-level and general-purpose programming language. In addition, it has a simple syntax, making it easier to learn than other languages. Python is also used for web development and scientific computing, so it's a versatile tool that can be applied across multiple disciplines.

Guido van Rossum created Python in 1991 at the National Research Institute for Mathematics and Computer Science (CWI). It's now run by the non-profit Python Software Foundation and supported by an active community of developers worldwide.

Google Analytics

Google Analytics is a free tool that aids in tracking user behavior on your website. It can help you understand who is visiting and how they use your site so that you can improve it.

Google Analytics provides an excellent way to measure your website's performance. You'll be able to see how many people visit your site, where they come from, what they do when they get there, and much more.

Microsoft HDInsight

HDInsight is a cloud-based data platform that helps you to quickly process large volumes of data in HDFS and Apache HBase. It's a fully managed service that offers all the benefits of Hadoop, Spark, Azure Data Lake Storage Gen2 (ADLS2), and other technologies in one seamless environment. It's also available as an open source project on GitHub under the Apache License 2.0.

HDInsight is a managed service, so it doesn't require any additional setup or configuration besides installing it in your subscription and creating an app for what you want to do with its resources. In addition, because Microsoft fully manages it, HDInsight will automatically scale up or down depending on how much processing power you need at any given time without any work from you.

RapidMiner

RapidMiner is an open-source data science tool for data preparation, predictive modeling, and machine learning.

RapidMiner Studio is a free edition of RapidMiner that gives you complete access to the platform's feature set. You can build visual workflows from various templates or from scratch, run those workflows with different settings on your data sets, test them out and even deploy them on Amazon Web Services (AWS) or Google Cloud Platform (GCP).

Conclusion

I hope this article will guide you and help you choose the right tools for your data science projects. I have listed some of the most favored instruments in each category and briefly described what they do. Nonetheless, there are many other tools out there that I haven't covered here because they aren't quite as popular or don't fit into the categories very well (e.g., Apache Storm). If you think other tools should be included in this list, please let me know by leaving a comment below!

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