# 12 Top Data Science Skills – Want to be a Data Scientist in 2023?

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In our last session, we talked about **Data Scientist Salary**. Today, we will discuss 12 most demanding Data Science Skills in the IT Market. By knowing this important skills for Data Science, you can prepare yourself for the Job.

Now, let’s find out what Data Science Skills you can put on your *resume* to boost your chances of a better career and a higher salary.

### What Does a Data Scientist do?

**Who is Data Scientist?**

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

Before looking at what Data Science Skills you will need to know what exactly a data scientist do? So, let’s find out what are the roles and responsibilities of data scientist. A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

- Determines correct datasets and variables.
- Identifies the most challenging data-analytics problems.
- Collects large sets of data- structured and unstructured, from different sources.
- Cleans and validates data ensuring accuracy, completeness, and uniformity.
- Builds and applies models and algorithms to mine stores of big data.
- Analyzes data to recognize patterns and trends.
- Interprets data to find solutions.
- Communicates findings to stakeholders using tools like visualization.

### A domain of Important Skills for Data Scientists

We can divide the required set of Data Science skills into 3 domains

- Analytics
- Programming
- Domain Knowledge

This is on a very abstract level in the *taxonomy*. Below, we are discussing some *Data Science Skills in demand*–

- Statistics
- Programming skills
- Critical thinking
- Knowledge of AI, ML, and Deep Learning
- Comfort with math
- Good Knowledge of Python, R, SAS, and Scala
- Communication
- Data Wrangling
- Data Visualization
- Ability to understand analytical functions
- Experience with SQL
- Ability to work with unstructured data

#### a. Statistics

As a data scientist, you should be capable of working with tools like statistical tests, distributions, and maximum likelihood estimators.

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A good data scientist will realize what technique is a valid approach to her/his problem. With statistics, you can help stakeholders take decisions and design and evaluate experiments.

#### b. Programming Skills

Good skills in tools like **Python** or **R** and a database querying language like **SQL** will be expected of you as a data scientist. You should be comfortable carrying out different tasks of programming activities. You will be expected to deal with both computational and statistical aspects of it.

#### c. Critical Thinking

Can you apply an objective analysis of facts to a problem or do you render opinions without it? A data scientist should be able to abstract the paydirt of the problem and ignore irrelevant details.

#### d. Knowledge of Machine Learning, Deep Learning, and AI

**Machine Learning** is a subset of **Artificial Intelligence** that uses statistical methods to make computers capable of learning with data. For this, they shouldn’t need to be explicitly programmed.

With Machine Learning, things like self-driving cars, practical speech recognition, effective web search, and understanding of the human genome are made possible.

**Deep Learning** is a part of a family of machine learning methods. It is based on learning data representations; learning can be unsupervised, semi-supervised, or supervised.

#### e. Comfort With Math

The foundation of data science is mathematics, and a data scientist has to be proficient in arithmetic for a variety of reasons. To begin with, statistics, a fundamental area of mathematics, is essential for deriving insights from data. Data scientists analyse data, look for trends, and make predictions using statistical methods. For appropriate data interpretation, it is crucial to comprehend ideas like regression analysis, probability, and hypothesis testing.

Second, a fundamental aspect of data science known as machine learning significantly depends on mathematical concepts. Algorithms are used by data scientists to train models and generate predictions in response to data. Algorithms used in machine learning, such linear regression, decision trees, and neural networks, have their roots in calculus and linear algebra. Data scientists can more successfully choose, optimise, and comprehend machine learning models when they have a strong math foundation.

The final phase in data preparation, data cleaning and preprocessing, is where maths plays a critical role. Dealing with missing values, outliers, and normalisation is a common part of working with real-world data. To guarantee that the data is good and appropriate for analysis, mathematical techniques are employed to alter and change the data.

#### f. Good knowledge of Python, R, SAS, and Scala

Working as a data scientist, a good knowledge of the languages Python, **SAS**, R, and **Scala** will help you a long way.

#### g. Communication

Skilful communication- both verbal and written, is key. As a data scientist, you should be able to use data to communicate effectively with stakeholders. A data scientist stands at the intersection of business, technology, and data.

Qualities like eloquence and storytelling abilities help the scientist dilute complex technical information into something simple and accurate to the audience. Another task with data science is to communicate to business leaders how an algorithm arrives at a prediction.

#### h. Data Wrangling

We have seen this with **Python Data Wrangling**. A lot of data you will be working on will be messy. Values could be missing, there could be inconsistent formatting with dates and strings. You will need to clean and wrangle your data.

#### i. Data Visualization

This is an essential part of data science, of course, as it lets the scientist describe and communicate their findings to technical and non-technical audiences. Tools like **Matplotlib**, ggplot, or d3.js let us do just that. Another good tool for this is **Tableau**.

#### j. Ability to Understand Analytical Functions

Such functions are locally represented by a convergent power series. An analytic function has its Taylor series about x0 for every x0 in its domain converge to the function in a neighbourhood.

These are of types real and complex- both infinitely differentiable. A good understanding of these helps with data science.

#### k. Experience with SQL

SQL is a fourth-generation language; a domain-specific language designed to manage data stored in an **RDMS** (Relational Database Management System) and for steam processing in an RDSMS (Relational Data Stream Management System).

We can use it to handle structured data in situations where variables of data relate to each other.

#### l. Ability To Work With Unstructured Data

If you are comfortable with unstructured data from sources like video and social media and can wrangle it, it is a plus for your journey with data science.

So, this was all in **General and Demanding Data Science Skills**. Hope you like our explanation.

### Conclusion

Hence, in this article of **Data Science** skills, we discussed some important skills for Data Scientists and also the skills in demand. Now, you are prepared to make a Data Science skills resume and **crack data science interview**.

Still, if you want to add any Data Science skills, you can tell us by commenting below.

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