Skills required to Build a Career in Data Science

Data Science is the future. A lot of professionals are slowly molding their careers to become a data scientist and a lot of freshers are looking for ways to make an entry into the data science world. There is an increasing demand for Data Science professionals across the industry as data is the new oil and every industry is trying to use data to leverage their business. 

In such scenarios, Data Scientists are drivers of growth and in order to avail of these opportunities and become a data leader, there are certain prerequisites that need to be met. This article talks about the essential skills that will help you earn a rewarding data science career. 

Data Science is actually a very broad term under which a lot of roles are covered. There’s no particular definition of “data scientist” or “data analyst” that every company agrees on, their job description varies. It is also possible that the same role might be having different requirements of skills in different companies. 

There is a number often used job titles that involve data science work which cannot be particularly brought under the umbrella of data science. So, the key is to acquire the most demanded skills and apply for job titles that match the most to your skills. 

In this article, I will be diving into broad Data Science titles and what are the relevant skills for these. The skills comprise of both hard and soft skills.

Hard skills mainly comprise specific knowledge and abilities which indicate specialization and competence for that particular job. If I had to give some examples, they would be computer programming, web design, typing, accounting, etc. Now, these are necessary skills that a person needs to be specifically ready for being a Data Scientist. 

Soft skills often take a back seat but they are necessary to get you a job that you dream and also help you reach greater heights in your career. These are basically communication skills that will help you communicate with the team about the required steps. 

Apart from the skills that I talk about, there can be more skills that can be particularly mentioned in the job description but this is a basic skeleton of requirements that are usually demanded to land a Data Science job. You can acquire more skills after you have mastered this.

Major roles as in Data Science 

The big three: Data Analyst, Data Scientist, and Data Engineer are often touted as the major roles that Data Science offers. We will see a basic description of the job title and what does the job expectations. 

Data Analysts

Data Analysts are the people who work with the abundant data possessed by business and help them make a data-driven decision. A business needs a lot of attempts on all fronts right from production to marketing, sales to public outreach and the modifications in these help businesses prosper. For example, if a company has to decide if their product A can be made more successful, then Data Analysts are the right people to find an answer. 

Hard Skills required: 

    • Programming: You need to master a programming language to be able to talk to machines and help leverage tools that will help the business prosper. The language could be of your choice. The most preferred ones are R/Python/Java/C++. R and Python are the most in-demand. For the profile of Data Analyst, R is preferred as it is a statistical language but for Machine Learning, Python is preferable. The languages have a lot of packages which will make your job easier. 
    • Data wrangling: Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one “raw” data form into another format with the intent of making it more appropriate and valuable. Most of the data we get in real-life are unstructured data which needs careful handling and efforts to get meaningful results from it. 
    • Data visualization: Data visualization is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Although Python has packages like Matplotlib and Seaborn which are helpful in Data Visualization, learning Tableau, Kibana, etc is an added advantage as it provides additional tools that help to visualize data in a better way and help reach meaningful conclusions with the data. 
    • SQL: Structured Query Language (SQL) is a programming language used for querying and managing data in databases. Together with Python and R, SQL is now considered to be one of the most requested skills in Data Science. It helps the data science professionals to query data stored in the databases. SQL is used for relational databases, NoSQL is used for other forms of databases, except relational databases. 
  • Mathematics: Mathematics forms the backbone of Data Science and it becomes elemental to learn the basics of it. The important topics include Matrices, Linear algebra, Probability, Relational algebra and Database basics. 
  • Statistics: Statistics becomes vital for implementing various types of algorithms to implement data science. Important topics include Descriptive statistics (mean, median, mode, range, standard deviation, variance, etc.), Exploratory statistics, Bayes theorem, skewness, etc. 

Soft skills required:

  • Communication: You should be proficient in telling a story out of the data you have and should be able to explain the meaning of the data to a non-technical person in a lucid manner. This forms the core of a Data Analyst who has to communicate his/her findings from the data to other people in the team who are working together to help the business grow. 
  • Teamwork:  As stated above, Data analysts work with other different departments and in teams inside the Data Science department. Being a team player becomes a vital skill that organizations focus upon because that helps them to get the best out of the investment that they are making on you as a Data Analyst. 
  • Curiosity: To weave a story out of data and turn them into something meaningful you need to be curious about the data and question yourself repetitively to arrive at conclusions. The more you ask, the more you get. 
  • Business competence: What do I mean by this skill? Well, businesses are constantly looking for new ways to maximize profits and to multiply their impacts. You have to be aligned with the mission and vision of the business to fully thrive in the surrounding.


Data Scientist

The job of a Data scientist is very much similar to that of an analyst. Alongside this, they also focus on building machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.

The skills required to become a Data Scientist, apart from Data Analyst are:

  • Algorithms:  You need to be well versed with supervised and unsupervised machine learning algorithms and the kind of algorithms that are used under both these heads. While the packages make it easier to implement these algorithms with simple commands, you might want to learn these in order to develop and design an algorithm on your own, which can be a combination of two or a new one altogether. 
  • Statistics:  While you need a basic level of statistical and mathematical understanding for being a Data Analyst, you need an in-depth understanding of Statistics for being a Data Scientist. 

There is a lot of overlap between a Data Scientist and a Machine Learning engineer. So, both jobs might look similar to each other. 


Data Engineer 

A data engineer is basically responsible for managing a company’s data infrastructure. This role tilts more towards software engineering rather than a statistical analysis which means greater command over R/Python and SQL. In the Data Science team, they are essential for establishing infrastructure for getting the latest data out from the businesses and help Data Analysts and Data Scientists use it for meaningful purposes. 

These are the three broad categories under which various Data Science roles can be fit into. There are other special jobs related to Data Science which Big Data Engineer, Data warehouse architect which have their specific skills requirements but the ones mentioned above are vital to almost all the Data Science jobs. 

We hope this guide was helpful. 

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