The Top 20+ Data Science Skills You Need by 2024

The right data science course can help you develop all the necessary skills you need to become a data scientist. With the continuously increasing significance of data in businesses, the demand for skilled professionals is also rising rapidly. Organizations need data scientists to analyze and extract meaning from the massive amounts of data they generate every day. As data science is a constantly evolving field, data scientists also need to upgrade their skills accordingly. From technical skills like mathematics and programming to non-technical ones like problem-solving skills, you must have a proper skill set to secure your dream job in the data science industry. If you are aspiring to become a data scientist in 2024, you must learn the following most important skills:

1. Mathematics

Data scientists need to have a strong foundation in mathematics in order to deal properly with organizational data. Linear algebra and multivariate calculus are among the most important mathematical subjects to learn. Practical knowledge of these subjects is useful in building out-of-the-box machine-learning models. A matrix is often used to represent a dataset and Ml models are also written in the form of matrices.

2. Programming

Programming is another crucial skill that you must possess in order to make a career in data science. You should be proficient in programming languages like Python, R, SQL, Hadoop, etc. A strong command of these programming languages and an ability to update your skills with evolving technology is very important to become a successful data scientist. Along with writing code, you should have knowledge of how to use programming environments for analyzing data.

3. Statistical Analysis and Probability

Statistics is one of the core subjects that you can not avoid if you aspire to a career in data science. Data scientists often use various statistical methods and concepts to analyze data. To make accurate ML models, you should be aware of statistical analysis and probability methods. Knowledge of probability distribution, descriptive statistics, sampling techniques, hypothesis testing, etc, is required to work efficiently as a data scientist.

4. Quantitative Analysis

Data scientists should be very comfortable with quantitative analysis. It refers to the process of using statistical and computational methods for numerical or mathematical analysis of the data. It is useful in making predictions, assessing financial instruments, and evaluating performances. Quantitative analysis uses three important techniques for measuring data, including data mining, linear programming, and regression analysis.

5. Data Wrangling & Cleaning

Data wrangling or cleaning refers to the process of converting raw data into a form that is suitable to be used for analysis. The process includes removing duplicate data points, dealing with missing values, handling inconsistent data, and making it ready for analysis. Wrangling is just another word for shaping your data to make it analyzable. It is quite difficult to get the most accurate from bad (unclean) data and hence, data cleaning becomes important. It improves the overall quality of data, which helps in better decision-making.

6. Big Data

Big data is a term used for large and extremely complex data sets. These data sets are too large to be analyzed using traditional software. Hence, it becomes important for data scientists in the 21st century to be aware of important tools and algorithms that they can use to deal with big data. Frameworks like Hadoop and Apache Spark are suitable for managing such large structured and unstructured data sets.

7. Data Processing

Data processing refers to collecting and transforming data in an easy-to-handle format. Different stages included in data processing are data collection, data preparation, data input, processing, data output, and data storage. Data scientists should be very well-versed in data processing. They should be very careful while performing data processing as any inaccuracies in data processing can affect the end result negatively.

8. Data Visualization

Data visualization is the process of representing textual or numerical data in the form of visuals to make it interesting and easier to understand for the human mind. As a data scientist, you should have knowledge of important data visualization techniques and tools like Tableau. You must be aware of different types of charts, graphs, dashboards, etc, and their right applications. It helps you understand data in a better way and makes it easier to identify patterns in data.

9. Microsoft Excel

MS Excel has many great features that help data scientists in analyzing data in the best possible way. It is one of the most basic yet important skills to learn if you aspire to become a data scientist. Excel allows you to arrange unstructured data sets in a manner that is simpler to be used for analysis. You can make calculations easily and even modify the functions when working with highly complex data. MS Excel helps in better analysis by allowing you to filter, clean, sort, or merge data whenever required.

10. Cloud Computing

Cloud computing is a necessary skill required for almost every job in data science. It refers to the usage of cloud-based platforms and technologies, such as Azure, Google Cloud, AWS, etc, for storing data. Instead of storing resources and data on local servers, you can access them through the Internet. Cloud computing allows organizations to create virtual storage rooms for their data and access it anytime and from anywhere. Also Read: 87 Must-Know Data Science Interview Questions

11. Machine Learning and Deep Learning

Machine Learning enables devices to learn from data on their own and hence, helps with the automation of processes. It is an important skill for data scientists as they often need to build ML models to make predictions on the basis of available data. Deep learning, which is a sub-domain of ML, is also a crucial skill for data science professionals. It is useful in creating algorithms that can recognize patterns in data by studying multiple layers of artificial neural networks.

12. Neural Networks

Neural networks are artificial structures with various interconnected nodes that resemble the neuron system of a human brain. These networks are used to enable machines to make decisions like humans. Neural networks can be used to categorize raw data and recognize hidden patterns in it. These artificial neurons learn continuously and improve themselves over time. Data Scientists must be aware of what neural networks are and how they are used.

13. Data Warehousing

Data warehousing is the process of integrating information and data collected from different sources into one database. Many professionals confuse data warehouses with databases. However, the two are not the same. A database only stores current data whereas a data warehouse stores current as well as historical data. Data scientists should have a good knowledge of data warehousing and ETL (Extract, Transform, and Load).

14. SQL and Database Management

SQL is a programming language that data scientists use to deal with SQL databases and perform data storage tasks. SQL allows you to access structured data and manipulate it. It is closely related to database management, which is the process of organizing, storing, and fetching data from a particular place. SQL databases are one of the most in-demand technical skills in the field of data science today.

15. Data Modeling and Management

Data scientists need to be well-versed in creating mathematical models of how to organize and store data in a system. Data modeling refers to the process of creating mathematical models for data representation and management means maintaining the quality, accuracy, and usefulness of data. Data modeling and management is also one of the essential data science skills you should learn in 2024.

16. Business Intelligence

Business Intelligence is a field that combines different technologies and strategies that help organizations in making better data-driven business decisions. It includes data mining, business analytics, data visualization, and various other processes. But these technical skills are not everything that you need to master the field of data science. You should possess business expertise and knowledge of how data can be used to improve business performance.

17. Data Storytelling

Data storytelling is a very sought-after skill in almost every industry today. It is the skill of communicating data insights with other team members or non-technical stakeholders of your business. As a data scientist, you should be able to present data and its analysis in the form of a story that everyone can understand. Data visualization tools like Tableau allow you to do this and hence, you must be familiar with the usage of such tools.

18. Critical Thinking

Apart from all the mathematical, technical, and statistical skills, you need to have several soft skills like critical thinking in order to crack a data science interview. Data scientists play a significant role in making important business decisions and hence, it becomes important for them to be good thinkers. To make a career in this field, you must be able to investigate and interpret facts to arrive at practical judgments.

19. Problem-Solving

As a data scientist, you should be able to understand business problems and come up with relevant solutions. You must know how to analyze data to identify organizational risks and how to overcome the same. Data scientists should have the capability to face challenging issues and be answerable to their organizations.

20. Communication Skills

Your communication skills matter a lot when it comes to building a career in data science. As a data scientist, you need to communicate with business leaders, stakeholders, and other team managers to make recommendations on the basis of data. You will often need to advise your co-workers and for that, good communication skills become essential.

21. Decision Making

Decision-making is one of the most important skills you need to develop before getting a job as a data scientist in 2024. You should be very clear about the techniques and tools you are using to analyze different types of data. Moreover, you need to make sure that you communicate very well with the business leaders to help them make the most relevant conclusions.

Learn These Skills With Data Science Courses at Edvancer

Edvancer’s data science courses are among the top online certifications for data science available in India. With these courses, you get a complete understanding of how the data science field works and how you can use its techniques to solve real-world problems. You can find these four data science courses at Edvancer:

• IBM Professional Certificate in Data Science
• Certified Data Science Specialist
• PG program in Data Science – UPES
• Executive Program in Data Science for Managers

With these courses, you get complete theoretical and practical coverage of all the important subjects required in data science. Moreover, you can choose your learning style at your convenience as Edvancer gives you two learning options, including self-paced learning and live online classes. If you are a beginner, you can start with the IBM certification or the Certified data science specialist course by Edvancer. However, experienced professionals can enhance their skillset by enrolling in the PG Program or Executive Program in data science.

FAQs

1. Is there a demand for data scientists in 2024? Ans. Yes, data scientists are among the most highly in-demand professionals in 2024. Due to the requirement for data professionals in every industry and a shortage of skilled workers, the demand for data scientists is still on the rise. 2. Is data science a good career in 2024? Ans. Data Science is one of the best career fields to consider in 2024. Data scientists are highly valued, highly paid, and play a significant role in the success of their organizations. 3. Which skill is best for a data scientist?> Ans. Data scientists do not need to have one or two skills, but a diverse skill set, which includes mathematics, programming, statistics, cloud computing, data cleaning, data processing, data warehousing, problem-solving, decision making, and many more.Share this on


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