Data science is a collective pool of various algorithms, tools, machine learning principles that work in unison to extract hidden patterns from raw data. It requires a diverse set of skills and demands knowledge from aspects of mathematics, science, communication, and business. Honing a diverse skill set, data scientists gain the ability to analyze numbers and influence decisions.

The core objective of data scientists lay in bridging the gap between numbers and actions by using the information to affect real-world decisions. This demands excellent communication skills along with understanding the difference between data science and big data analysis and recommendations to businesses.


Probably a major responsibility of a data scientist is to make data as presentable as possible for users to get better insights of raw data and to derive the desired information out of it. Visualizations are important in the first place because they guide the thought process of people viewing it for further analysis. They are used to create impactful data stories that communicate an entire set of information in a systematic format so that the audiences are able to extract meaning out of it and detect problem areas in order to propose solutions.

Without data visualization tools, it would be practically impossible to implement change or cater to the desired problems. Today, there are many data visualization tools to select from. In most programming languages, you’ll find libraries that enable visualization of data. In JavaScript, data can be visualized using the D3.js visualization library, Python uses Matplotlib and pandas while R offers many data visualization tools including ggplot2.

Tableau is the most trending, high-level platform that offers amazing data visualization options extracting data from many different sources.


Often the data comes from a variety of sources and needs remodeling to be able to derive informational insights. It is important to make the data free from imperfections such as inconsistent formatting, missing values, etc. Data wrangling allows you to bring the data on a uniform level that can be further processed easily. Obviously, for a data scientist to use data to their best, it is important to possess the knowledge of organizing clean data from the unmanageable raw data.


Data scientists deal with raw data that comes from a variety of sources and in different formats. Such data is filled with misspellings, duplications, misinformation, and incorrect formats that can mislead your results. To correctly present the data, it is important to extract the data, clean it, analyze and visualize it. Below are six broadly used tools that are recommended strongly for data scientists:

  1. R: R is a programming language that is widely used for data visualization, statistical analysis, and predictive modeling. It has been around for many years and has been contributing largely to data analysts with its huge network (CRAN) that provides a complete package to allow analysts to perform various data-related tasks.
  2. Python: Python initially was not looked upon as a data analytics tool. The pandas python library enables vectorized processing operations and efficient data storage. This high-level programming language is fast, user-friendly, easy to learn and powerful. It has been used for general programming purposes for long now and therefore allows easy merger of general-purpose code and Python data processing.
  3. Tableau: Lately emerged as an amazing data visualization tool, Tableau, a Seattle-based software company offers an exclusive suite of high-end products that surpass the science resources such as R and Python. Although Tableau lacks the ultimate efficiency in reshaping and cleaning data and doesn’t provide options for procedural computations or offline algorithms, it is increasingly becoming a popular tool for data analysis and visualizations due to its highly interactive interface and efficiency in creating beautiful, dynamic dashboards.
  4. SQL: Structured Query Language (SQL) is a special-purpose programming language that allows for extracting and curing data that is held in relational database management systems. SQL allows users to write queries, insert data, update, modify, and delete data. Though all of these can also be done using R and Python, writing an SQL code derives more efficient output and provides reproducible scripts.
  5. Hadoop: Hadoop, an open-source software framework fosters distributed processing of large amounts of data sets using simple algorithms from large clusters of computers. Hadoop is largely used in industries due to its immense computing power, fault tolerance, flexibility, and scalability. It enables programming models such as MapReduce that enable the processing of vast amounts of data.


Though there are many automated statistical tests embedded within software, a data scientist needs to possess a rational statistical sensibility to apply the most relevant test for performing result-oriented interpretations. Solid knowledge of linear algebra and multivariable calculus assist data scientists in building analysis routines as needed.

Data scientists are expected to understand linear regression, exponential and logarithmic relationships while also knowing how to use complex techniques such as neural networks. Most of the statistical functions are done by computers in minutes, however, understanding the basics is essential in order to extract the full potential. A major task of data scientists lay in deriving the desired output from computers and this can be done by posing the right questions and learning how to make computers answer them. Computer science is backed in many ways by mathematics and therefore data scientists need to have a clear understanding of mathematical functions to be able to efficiently write codes to make computers do their job perfectly.


AI is the most trending topic today. It empowers machines by providing intelligence in the real sense to minimize manual intervention to extreme levels. Machine learning works on algorithms that are automated to obtain rules and analyze data and is largely used in search engine optimizations, data mining, medical diagnosis, market analysis, and many other areas. Understanding the concepts of AI & Machine Learning for beginners play a vital role in learning industry needs and therefore are at the forefront of data science skills that a data scientist must possess.


Even before any of the modern data analysis tools existed, MS-Excel had been there. It is probably the oldest and most popular data tool.

Although now there are multiple options to replace MS-Excel, it has been proven that Excel offers some really surprising benefits over others. It allows you to name & create ranges, sort/filter/manage data, create pivot charts, clean data, and lookup for certain data among millions of records. So, even though you might feel that MS-Excel is outdated, let me tell you it is absolutely not. Non-technical people still prefer using Excel as their only source of storing and managing data. It is an important pre-requisite for data scientists to have an in-depth understanding of Microsoft Excel to be able to connect to the data source and efficiently pick data in the desired format.

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