Most In Demand Data Science Technical Skills
Learn what data science technical skills are in highest demand and what technical skills you should have as a data scientist.
As a fresh graduate, you might be wondering what data science technical skills are needed to be a data scientist. In this blog, we'll outline what capabilities and techniques you should possess to be a part of a data science team.
What Technical Skills You Should Have to be a Data Scientist
A data scientist is one of the biggest assets in a company because they can help extract, synthesize, and guide the company by extracting true business value from the data. So what does it take to be a successful data scientist? Here are some most in-demand data science technical skills and important attributes.
1. Programming Skills
A programming language consists of a set of instructions that produces various output. In data science, there are several languages and to become a data scientist, you have to learn at least one language as an essential data science technical skill. Data scientists have several tools in their toolkit like SQL, Python or R.
Python is considered as a full-fledged language and used widely by many organizations. On the other hand, R is a statistical programming language but python can also be used for statistics and to build models. Both are important for statistics and model building, but python can help with automation which gives it a step up from R.
SQL has become more popular data science technical skill for managing data. SQL is extremely useful and easy to store, manipulate, and retrieve your data in relational databases. It's also used for basic to intermediate analytics.
2. Data Wrangling, Modeling, and Machine Learning
Data wrangling as one of the data science technical skills involves the ability to write complex SQL queries, manipulation using Python scripts, data collection, accessing various databases, data cleaning, and reports.
Model building and deployment means understanding multiple modeling techniques like regression and classification. As a data scientist, you should have an understanding of how to interpret the results and validate the model.
If you are at a large company with a huge amount of data, machine learning is indispensable data science technical skill. Through machine learning algorithms, a data scientist can help predict actions and better understand users. A few of the machine learning algorithms are Regression Algorithms, Linear model, K-nearest neighbors, Neural Networks, Decision Trees, and K means clustering.
3. BI Tools
Data scientists do use BI tools or business intelligence tools like tableau, Qlik, and powerBI. There are tons of different options when we have to implement a BI tool. BI tools are extremely useful to understand trends and deriving insights from the data you have. Business intelligence tools help you with making the right decision for your business. Tableau is the skill that we can consider is used widely.
4. Understanding of Relational Databases
As SQL is a requisite to become a data scientist, you should learn one of the main databases for a career in data science. Most data scientists have access to a database. There is a very less chance you will work through an API or Library. These databases provide the required support and ability to data scientists to work with big data repositories. So, knowledge of any main database like PostgreSQL, MySQL, SQL Server, Teradata, BigQuery, Oracle, or Snowflake is one of the strongest data science technical skills.
5. Ability to Host Dashboards
Dashboard means a tool that provides an interactive, centralized, measuring, analyzing, and means of monitoring business insights from different datasets. It displays information interactively and visually. Most data scientists need to set up a dashboard with using their preferred BI tools like Tableau. Occasionally, you have to set up dashboards for the clients as well.
In this blog, we have discussed and covered most in-demand data science technical skills, yet some many other skills and tools could be required based on a data scientist particular role. It is also indispensable to possess technical as well as soft skills to be a data scientist.