10 Skills You Must Need to Become Data Science Specialist

With the rapid growth of data science and analytics, there is a high demand for Data Science and Analytics professionals around the globe.

Not only organisations but also individuals are preferring to learn data science to get higher ROI on Big Data.

You all know that the market will pay any price for skills they require. So if you want to build your career as a data scientist, you should be aware of the following skills.

This article is an effort to help you learn from experts who have decided to share their hard-earned knowledge at no cost. 

Here are 10 Skills that will certainly turn you into a Data Science Specialist:

1) Descriptive Statistics

The art of drawing statistical graphs is so important for Data Scientists. For example, you should be able to draw pie charts or scatter plots to communicate your findings. You must also know how to draw histograms and boxplots with ease.

When it comes to descriptive statistics, you need not only strong theoretical knowledge but also immense practice-based skills.

2) Probability and Statistics

Probability is a must-have subject for any data science aspirant. In fact, many of the data science courses teach statistics and probability together as it helps you understand various concepts simultaneously.

The basics of probability can be learned in a few weeks, but to master the same you need many months and years.

As statistics is an important part of data science, you need to have solid concepts in probability. It will not only give you an edge as a data scientist but also help you with the analytical approach while solving complex problems.

3) Integration and Differentiation

These two are the most fundamental mathematical operations to handle data science problems when it comes to machine learning.

Data scientists are often required to work with various kinds of data sets, which need different types of numerical operations.

It would be a challenging task if you don’t have a strong understanding of integration and differentiation. For example, when you face a new problem then it’s always better to start with the simple one and gradually move towards the complicated ones to gain experience.

4) Machine Learning Fundamentals Algorithms

Machine learning is the main engine of data science. So if you are a machine learner then data science certainly becomes simpler.

The choice of algorithm depends upon the nature of the problem and the type of data available. You should be able to choose the right algorithm for a specific task, which can provide higher accuracy and speed up your results.

Naïve Bayes, Logistic Regression, Decision Trees, etc. form the basis of Machine Learning algorithms.

It’s the era of machines and everything is being done by machines. For data science aspirants, it’s very important to understand a few basic Machine Learning algorithms.

You should know which algorithm can handle which kind of data sets or problems. You should also keep an eye on the latest developments in machine learning. 

5) R or Python programming languages

You should be familiar with either R or Python. While SQL will come in handy in data preparation; you need to know how to code for building predictive models using machine learning algorithms on your own.

Both R and Python are open-source programming languages, which you can start learning right away. In short, if you want to excel in data science then there is no way you can avoid these two.

6) Enterprise Architecture

While most of the data science projects are about building applications for data analysis, data visualization; you also need to pay attention to how the final solution will be deployed in an enterprise environment.

It’s important because it reduces the risk of failure to a great extent. Here are some questions you need to answer before building and deploying an end-to-end solution:

  • How will you collect data? How would you deploy your data science application?
  • How would you host this application?
  • Who needs access to this application, and how will you protect user privacy?
  • How will you secure this application?

You must also be aware of how to handle databases in a real-world environment, which means you need to have good knowledge of RDBMS and NoSQL databases like MongoDB or Hadoop.

7) Data Preparation

Data preparation has become a key focus area now due to the growing volumes of diverse structured as well as unstructured data. So, if you want to become a successful data analyst, you must possess strong data preparation skills.

Data preparation is a challenging task because you need to have a keen eye for examining the data relationships and patterns. For example, knowing what algorithmic method works best on which kind of data set is an important aspect of data preparation.

8) Communication Skills

Data Science is teamwork. You should be a good communicator and always ensure that everyone in the team is aware of your plans or progress. You must also hire people to supplement your skill set if required. After all, building an end-to-end data science application is not a one-man job.

Data Science is no longer about silos wherein different teams generate data, analyse and finally present the findings. Data scientists now play a vital role in communicating what they find out to C-level management through presentations or reports. So if you want to become a successful data scientist, you need to develop excellent communication skills.

9) Data Visualization

Data visualization is an important part of data analysis. The quintessential skill in data science is the art of turning unstructured data into visually appealing charts to help people understand the insights better. For example, a bar chart for showing team performance or a pie chart for collaboration among various teams will prove helpful in telling a story using data.

10) Statistical Inference

While probability and statistics form the basic mathematical foundation of data science, statistical inference takes it to the next level. For example, you should be able to calculate Bayesian inference for making predictions or A/B Testing to measure the impact of changes in different scenarios.  

It’s not that one must have solid knowledge in all these areas and more, but the main thing which will make you a successful data scientist is how well you understand your role in the team and how effectively you communicate with others to deliver excellent solutions.


Data science is the new buzz word and every other company in today’s competitive world wants to have a data scientist for their organisation; Not only, there are skill shortages in this field as well because right now, understanding customer behavior, the business impact of analytics, or how AI algorithms work is considered sufficient for the job.

The skills which are mentioned above should be considered as mandatory skill sets for a data science aspirant. There is something more about data science, that the job nature itself is changing rapidly and new technologies are entering this field every other day. So it’s better to do some kind of training or certification on the emerging tools/technologies even if you are a fresher.