Tools Every Data Scientist Should Know: A Practical Guide

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Which tools do data scientists rely on the most?
This question is important, especially before learning data science, because data science is a constantly evolving field, and outdated articles might give you outdated information.
In this article, we’ll cover the must-know recent tools that can elevate your data science game, but let’s start as if you don’t have a clue about data science.

What is Data Science?

Data Science is a multidisciplinary field that combines knowledge from various disciplines to help businesses make intelligent decisions through data-driven analysis.

Python

Along with R, Python is one of the most frequently utilized languages in data research. It is flexible and readable and has many libraries to support it, especially in data science, making it ideal for various tasks, from web scraping to model building.

Here are the critical Libraries for each category in Python

• Web Scraping:
• Data Exploration and Manipulation:
  • Pandas: Python data manipulation and analysis toolkit.
  • NumPy: Supports big multidimensional arrays and mats.
• Data Visualization:
  • Matplotlib: The core Python plotting library
  • Seaborn: A visualization library based on Matplotlib. It offers a high-level interface for creating attractive statistical graphics.
  • Plotly: Interactive graphing library.
• Model Modeling:
  • Scikit-learn: The most critical ML library in Python
  • TensorFlow: Good to apply and scale Deep Learning.
  • PyTorch: A machine learning library for image processing and NLP applications.

R

R is a potent text analysis tool designed to address statistical and data analysis concerns. Its comprehensive statistical power and vast package ecosystem make it quite popular in academia and research.

Here are the critical Libraries for each category in Python

• Web Scraping
  • rvest: Makes web scraping easy by mimicking the exact structure of the web page.
  • RCurl: R bindings to the curl lib, allowing for anything that can be done with the curl itself.
• Data Exploration and Manipulation
  • dplyr: It is a grammar of data manipulation offering data manipulation verbs that help make data manipulation easier.
  • tidyr: Makes your data more accessible by manually spreading and gathering data.
  • Data.table: An extension of data.frame with faster data manipulation capabilities.
• Data Visualization
  • ggplot2: Application of the grammar of graphics.
  • lattice: Better defaults + easy way to create multi-panel-plots.
  • plotly: It converts graphs created with ggplot2 to interactive, user-driven web-based graphs.
• Model Building
  • Caret: Tools for creating classification and regression models.
  • nnet: Offer functions to build neural networks.
  • randomForest: It is a random forest algorithm-based library for classification and regression.

Excel

Excel is easy to use for analyzing and visualizing data. It is easy to learn and compress, and its ability to handle large data sets makes it helpful for fast data manipulation and analysis.

In this section, instead of libraries, we’ll divide the key functions of Excel into subsections to categorize them.

Data Exploration and Manipulation

• FILTER: Filters a spectrum of data depending on your defined criteria.
• SORT: Sort the elements of a range or array.
• VLOOKUP/HLOOKUP: Finds things in tables or ranges by row or column.
• TEXT TO COLUMNS: This will split the content of a cell into multiple cells.

Data Visualization

• Charts (Bar, Line, Pie, etc.): Regular standard chart types to depict data.
• PivotTables: It condenses large data sets and creates interactive summaries.
• Conditional Formatting: It displays which cells fall under a specific rule.

Model Building

• AVERAGE, MEDIAN, MODE: Calculates central tendencies.
• STDEV.P/STDEV.S: Works with the dataset to calculate dataset segregation.
• LINEST: Based on the linear regression analysis, statistics for a straight line that most matches a data set are returned.
• Regression Analysis (Data Analysis Toolpak): This toolkit uses regression analysis to find correlations between variables.

SQL

SQL is the language used to interact with relational databases and is needed to store and process data.

A data scientist primarily uses SQL as the standard way to interact with databases, helping them query, update, and manage data in all the databases. SQL is also required to access the data for retrieval and analysis.

Here are the most popular SQL systems.

• PostgreSQL: An open-source object-relational database system.
• MySQL: A high-level, popular open-source database known for its speed and reliability.
• MsSQL (Microsoft SQL Server): A Microsoft-developed RDBMS fully integrated Microsoft product with enterprise features.
• Oracle: It is a multi-model DBMS widely used in enterprise environments. It combines the best relational model with tree-based storage representation.

Advanced Visualization Tools

With the right advanced visualization tools, complex data can be transformed into vivid, usable insights. These tools allow data scientists and business analysts to create interactive and shareable dashboards that improve, understand, and make the data accessible at the right time.

Here are vital tools to build dashboards.

• • Power BI: A business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their reports and dashboards.
  • Tableau: A robust data visualization tool that allows users to create interactive and shareable dashboards that give insightful views of the data. It can handle large volumes of data and work well with disparate data sources.
  • Google Data Studio: It is a free parts web-based application that allows you to create dynamic and aesthetic dashboards and reports using data from virtually any source, and other parts free, fully customizable, and easy-to-share reports that automatically update using data from your other Google services.

Cloud Systems

Cloud systems are essential to data science because they can scale, increase flexibility, and manage big datasets. They offer computational services, tools, and resources to store, process, and analyze data at scale with cost optimization and performance effectiveness.

Check out popular recipes here.

• AWS (Amazon Web Services): Provides a highly sophisticated and ever-evolving cloud computing platform that includes a range of services such as storage, computation, machine learning, big data analytics, etc.

• Google Cloud: Offers various cloud computing services that run on the same infrastructure Google uses internally for products such as Google Search and YouTube, including cloud data analytics, data management, and machine learning.

• Microsoft Azure: Microsoft offers cloud computing services, including virtual machines, databases, AI and machine learning tools, and DevOps solutions.

• PythonAnywhere: A cloud-based development and hosting environment allowing you to run, develop, and host Python applications through a web browser without IT staff setting up a server. Ideal for data science and web app developers who want to deploy their code quickly.

Bonus: LLM’s

Large Language Models (LLMs) are one of the cutting-edge solutions in AI. They can learn and generate text like humans, and they are quite advantageous in a wide range of applications, such as Natural Language Processing, Customer Service Automation, Content Generation, and so on.

Here are some of the most famous ones.

• ChatGPT: It is a flexible conversational agent created by OpenAI to generate human-like and in-context text, which is beneficial.

• Gemini: The LLM created by Google will allow you to use it directly inside Google apps like Gmail.

• Claude-3: A modern LLM specially built for better understanding and text generation. It is used to assist in every high-level NLP task and conversational AI.

• Microsoft Co-pilot: An AI-powered service integrated into Microsoft applications, Co-pilot helps users by giving context-sensitive recommendations and automating repetitive workflows, enabling productivity and efficiencies across the processes.

If you still have questions about most valuable data science tools, check these 10 Most Useful Data Analysis Tools for Data Scientists.

Final Thoughts

In this article, we explored essential tools for data scientists, starting with Python to Large Language Models. Mastering these tools can significantly enhance your data science capabilities. Stay updated and continually expand your toolkit to stay competitive and effective as a data scientist.

Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.