Data Scientist Top Skills

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Published on September 1st, 2022

Table of Contents [show]

Table of Contents

Introduction

Given the increasing need for data scientists, this field offers both aspiring professionals and experienced workers an appealing career path. This includes those who aren’t data scientists but are intrigued with data and the field, leading them to wonder what big data and data science abilities are required for a career in the field.

Big Data as a tool to generate insights has created a demand for enterprise-level data scientists across all industry verticals. Whether it’s improving a product development process, improving customer retention, or mining data to find new business opportunities, organizations increasingly rely on the skills of data scientists to sustain, grow, and stay one step ahead of the competition. In this blog, we also dive into data scientists’ technical and non-technical skills.

Skills Needed To Become A Data Scientist

There Are Two Types Of Essential Skills:

Technical skills
Non-technical skills

The knowledge in this blog can assist you if you are a budding data scientist on your path to a rewarding career in this dynamic and expanding field. Suppose you are the director of data analytics in an organization. In that case, you can use the information to train your existing data scientists with cutting-edge data science skills, making them more productive and efficient in their work. Now let’s discuss what technical skills are required for a data scientist.

Technical Skills Needed To Become A Data Scientist

1. Mathematics and Statistics

Any good data scientist will have a strong foundation in mathematics and statistics. Any business, especially a data-driven one, will expect a data scientist to understand various statistical approachesincluding maximum likelihood estimation, distributions, and statistical teststo help make recommendations and decisions. Calculus and linear algebra are both keys because they are associated with machine learning algorithms.

2. Web Scraping

Technically speaking, any data that exists on the internet can be deleted if needed. Companies use this method to extract valuable data such as text, images, videos, and other helpful information to increase productivity. The details can be customer reviews, surveys, polls, etc. Companies of all levels (from small to large) actively practice this method (within the limitations of the law). Using specific tools and software for this method can simplify the process by making data on a large scale. Web scraping is in high demand among data scientists when it’s all about data.

Our Learners Also Read: The Data Science Toolkit: 20+ free data science tools

The Following are Some of the Most often used Tools for Data Scraping:

BeautifulSoup: To extract and analyze data from web pages and save it immediately in a local database. You must install this library through the terminal to use it.

Scrapy: Frequently employed for data mining and gathering pertinent content from any specific website as needed. Today, it is commonly used for data extraction utilizing APIs after being introduced back in 2008 for web scraping (such as AWS).

Pandas: A Python module that may be used to extract data, alter it, and export it as an Excel or CSV file.

3. Analysis and Modeling

Data is only as good as the people analyzing and modeling it, so an experienced data scientist is expected to have the latest data science training and a high level of expertise. Based on both critical thinking and communication skills, a data scientist should be able to analyze data, run tests, and build models to gain new insights and predict possible outcomes.

4. Machine Learning Methods

Although specialist-level knowledge in this area is not always required, a certain level of expertise will be expected. Decision trees, logistic regression, and more are crucial elements that machine learning enables, and potential employers will be looking for these skills.

5. Programming

A data scientist needs strong programming skills to move from theoretical to building practical applications. Most employers anticipate that you are familiar with R, Python, and other programming languages. This includes libraries and documentation, fundamental syntax and functions, flow control instructions, object-oriented programming, and more.

6. Data Visualization

Being a data scientist requires being able to effectively express critical messages and obtain estimates for suggested solutions, which calls for the usage of data visualization. One of the talents each data scientist needs to have to develop their profession is the ability to divide complex data into manageable parts and to employ a range of visual aids (charts, charts, and more). Check out our post Creating Data Visualizations with Tableau to learn more about Tableau and why data visualization is so important.

7. Big Data

As we discussed above, a large amount of data is generated daily. Big data is primarily used to capture, store, extract, process and analyze useful information from various data sets.

Those who have already worked with big data can understand that handling such data is not feasible due to many limitations (both physical and computational) and solving such problems requires special tools and algorithms to achieve these goals. Some of them are:

KNIME: A data preparation platform used to create specific datasets by aligning design and workflows
RapidMiner: An automated tool designed with a visual workflow for data mining.
Integrate.io: It is a platform used to integrate, process, and prepare various datasets for analysis in the cloud.
Hadoop: An open-source platform that stores and processes large data sets ranging from gigabytes to petabytes.
Spark: It is one of the best and most prevalent tools used to process large datasets quickly and is widely used by telecom, gaming companies, etc.

Non-Technical Skills Needed To Become A Data Scientist

In addition to their technical data scientist abilities, we will now focus on the non-technical skills required to become a data scientist. These relate to personal mastery and can be challenging to assess by looking at educational qualifications, certifications, etc. They contain:

1. Strong Commercial Sense

The best use of technical expertise is when it is matched with wise business judgment. Without it, a nascent data scientist might not be able to recognize the problems and upcoming challenges that need to be resolved for the company to advance. This is crucial to assisting the company you work for in identifying fresh business prospects.

2. Effective Communication Abilities

The ability to communicate is the next most important talent for data scientists. Data Scientists are skilled at the gathering, understanding, and analyzing data. To succeed in your role and support your business, you must be able to communicate your outcomes to team members who lack your professional experience.

3. Ability To Solve Problems

The foundation for building your career as a data science professional will require you to be able to handle complexity. The ability to identify and develop creative and effective solutions as and when needed must be ensured. You can face challenges in finding ways to create any solution that can be clear in data science concepts by breaking problems into multiple parts to align them in a structured way.

Conclusion

Data scientists must be skilled in handling data and have the ability to translate and communicate insights across the enterprise because they are responsible for sharing their results with important stakeholders.