You may be surprised at how Netflix can show you highly targeted recommendations. Spotify knows what song you like best. This is data science for you. Data can influence everything. Data is the key to any organization’s success. Organizations that can leverage their databases to identify consumer preferences and user patterns are always ahead of the rest. Data science is so deeply embedded in our lives that we don’t realize it. Data science skills are used in various ways to make simple things like weather forecasts, health diagnoses, and navigation decisions.
Data Science can also be used in business, such as Banking & Finance. Banks can reduce fraud by identifying defaulter patterns, and analyzing the risk probability. Every industry and every business can use the power of Data Science in order to create something new or improve customer experiences. Data science skills are essential for organizations to succeed because of its wide range of industry-specific applications and use-cases.
This article will focus on the core data science skills that are essential for data scientists in all industries. Let’s look at the entire range of data science skills to get a better understanding.
Key Data Science Skills for Individuals and Organizations in 2023
SQL and NoSQL are interchangeably used to query and manipulate unstructured data in relational/non-relational databases. Both SQL and NoSQL databases are used in data science applications. It all depends on the organization or data professional which language they prefer depending on their specific challenges and use cases.
Data scientists need to be able to create data pipelines using SQL/NoSQL queries. Data scientists are often faced with unstructured data, including text, video, audio, and biometric data. A data scientist can sort and organize this data by being able to write powerful queries, and then using these queries on a workflow management platform. It is a valuable data science skill that every company should strive to have.
Pro Tip: The SQL Database course is designed to help data scientists manage an SQL database infrastructure.
2. Python
According to Kaggle, an online community of data scientists and machine-learning professionals, Python is the most popular programming language for data scientists. This programming language is capable of performing data mining, website development, and even running embedded systems. This programming language is an excellent choice for anyone looking to improve their data science skills. There are many libraries, including some AI-adjacent ones, available for Python. When hiring data scientists, companies also look for Python skills.
Pro Tip: You can also check out the to Python data science language to see if it is your favorite!
3. Programming in R
The R programming language is great for statistical analysis and computing graphics. It is therefore an essential skill set for data scientists. If you are still wondering why data scientists need programming skills, the simple answer is that data scientists must be able to organize unstructured data. Programming languages enable data professionals to query data without the need for third-party applications or tools. Data scientists with programming skills can be self-sufficient as they have the ability to manipulate, calculate, and display data. R is more popular in academic environments than Python. R offers the advantage of machine learning algorithms that are easy to use and a few statistical approaches such as clustering and non-linear modeling, and classical statistical modelling for data analysis.
Pro Tip: Learn the intricacies of data science with this Practical Data Science course with R.
4. Machine Learning
Machine learning is a fundamental skill required for data scientists to start a career in this field. Machine learning is used by data scientists to create predictive insights and forecast trends using historical data. Machine Learning covers the core skills of data science, including data mining, pattern recognition and statistical analysis.
Pro Tip: Neural Nets and SVM are key concepts in machine learning that every data scientist must master. Decision Trees and Clustering are also important concepts. This Machine Learning Fundamentals training covers all these concepts.
5. Data Wrangling
Data cleaning or data wrangling is the process of removing unwanted data.