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How to Improve Data Science Skills

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 information to affect real world decisions. This demands excellent communication skills along with understanding the implications of their analysis and recommendations to businesses.

Data science skills are not confined to data, statistics and pre-defined tools, it goes far more beyond to simplifying information for people in a way that it could be utilized for decision-making effectiveness.

Moreover, a data scientist may be an opportune for a wide variety of roles and includes a variety of positions which mandates the different skill sets based on the industry.

Let us have a look at the technical as well as non-technical skills that can help improve your data science skills.

Non-Technical Skills Required By Data Scientists

Here are a few non-technical skills that a data scientist must possess which are mostly overlooked but play an important role in applying the technical skills right to the job.


The most vital of the skills a data scientist must possess is comprehension and communication. The data language isn’t understood by all so it is necessary that data scientists have the skill of communicating the technical findings in a simplified manner to their non-technical colleagues or the senior management in the board meetings.

Narrate a story with the data that gives a compelling message to the onlookers.

Though data cleaning, wrangling, processing and analysing are important steps in data science, all of them do not carry much worth without effective communication. In order to communicate, one must be able to visualize in the first place. The art of visualization leads the data scientist to craft an influential story from data. Humans inherently understand and get impacted by visuals than numbers. So, creatively presenting a piece of information and communicating it further is of absolute importance that too in a way that is understood by the audience.


Data scientists are looked upon as profound resources of data analysis and predictions. Unlike before, data scientists are required in almost every industry and with the growing amount of data, its applications are also tremendously increasing.

Every industry is different and has diverse goals and unique datasets. In order to apply data science skills accurately to a specific industry, a data scientist needs to have a clear understanding of the business functions and must possess the ability to interpret business implications of their data insights. Some industries have a unique vocabulary and terms that need to be studied by the data scientist first to exhibit the data in a useable manner. Although metrics such as revenue and costs are common across industries, there are some specific KPIs (key performance indicators) that are industry-specific. Without a thorough understanding of the industry, it’s unique goals and limitations, it would be almost impossible to get the right insights and make useful recommendations to management.

Considering the fact that a data scientist has a pre-defined job title, roles and set of responsibilities, it must also be added that he/she must have an in-depth understanding of the respective industry as the data scientist’s tasks would vary greatly depending on the industry.


Data scientists are ultimate problem solvers. With data-driven problem-solving skills, they excel in presenting problems in a way that triggers decision making. With the help of a structured approach in framing and identifying problem areas, data scientists help simplify and speed up the decision-making process.

An underlying expectation from a data scientist is to know how to approach a problem area productively. With the vision of a data expert, it is easier to identify the features of a situation and intelligently tap the areas to investigate to yield the desired answer. Additionally, they are deemed to know which data science methods to apply to specific problems. Apart from understanding machine learning and statistics, one must know how to integrate the available information with the business’ goals while deciding ways of solving problems.

One can not anticipate data science problems nor there is a full-proof solution for every problem. It is likely for decision makers to get overwhelmed by the options to explore. Here comes the role of a data scientist who can exactly gauge a probably right track and also manage the progress. Data science is a pool of techniques such as Six Sigma that enable data scientists to solve real world data science problems in a structured manner.

Technical Skills Required By A Data Scientist

Technical Skills Required for  Data Science Course

Technical skills are what set the data scientists apart. These include knowledge of specialized tools and programming languages that are used by specific businesses. However, there are a list of technical skills that ideally every data scientist must possess. Usually, data scientists use data mining, machine learning and artificial intelligence with certain programming languages to apply various data analysing tools. In addition, they must also have a brief understanding of the software engineering principles in order to integrate the tools and different programming languages that they would need to use.


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 of the 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 remodelling 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 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 modelling. It has been around since 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 enables 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. A 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 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 topics 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 analyse 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 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 tools.

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 look up 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.

Data scientist are greatly in demand with the growing amounts of data every second and it poses an alluring career path for people who love to work with data. However, the world is already aware of the huge potential of data science and are crowding up in the marketplace. It is important to upgrade yourself with the necessary skill sets to ensure you don’t lose the race. Yu don’t have to search any farther. Vinsys offers a wide pool of trainings to enable you uplift your career path now. With Vinsys, you get to learn from highly experienced industry professionals and secure a promising addition to your profile for unlocking many career opportunities at very reasonable prices.

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