Roles in the Data Space
In today's digital age, data is ubiquitous and has become an invaluable asset for businesses and organizations of all sizes. As a result, the demand for individuals with the skills and expertise to manage, analyze, and interpret data has grown exponentially in recent years.
From data analysts to data scientists, the field of data has numerous roles that require different skillsets and knowledge. In this blog, we will explore some of in-demand roles and the skills required.
🧑💻 Data Analyst
A data analyst collects, cleans, and interprets data to discover trends and answer business questions. Data Analyst draw business insights from data through complex analysis, mining, and visualization.
Skills required:
Technical skills: Excel, SQL, Power BI, Tableau.
Data storytelling and reporting skills
Strong technical skills and creative thinking to solve problems and make real-time decisions.
Effective verbal and written communication skills
Analytical thinking skills
🧑💻 Business Intelligence Analyst
Business Intelligence Analysts leverage various data tools to analyze an organization’s data in order to provide insights that can be used improve processes, services, and performance, and to help businesses to make data-driven decisions.
Skills required:
Data visualization and reporting skills.
Experience in BI tools such as Power BI, Tableau etc.
Querying and analyzing data with SQL
Knowledge in data wrangling and modelling
🧑💻 Business Analyst
Business analysts use data to form business insights and maximize a business's effectiveness through data-driven decisions. Business analytics helps business to make confident data-driven decisions informed by real-time insights, forecasts, data analytics, and BI to drive business outcomes.
Skills required:
Advanced skills in Microsoft Excel
Experience in writing SQL queries and mining databases
Data visualizations in Tableau, Power BI or Looker Studio.
🧑💻 Data Engineers
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale.
Data engineers build systems and pipelines that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.
Skills required:
Experience with big data tools: Hadoop and Spark, etc.
Experience with data pipeline and workflow management tools
Strong programming language like Java, Python, or Scala
Good knowledge of relational databases or NoSQL databases like MongoDB or Cassandra
🧑💻 Data Scientist
Data Scientists perform deeper analysis and develop predictive models to solve complex data problems. Data scientists create and implement models for extracting knowledge, which is further analyzed and becomes the basis for forecasting and decision-making.
🧑💻Data Analysts vs 👩💻Scientist
Data Analysts find meaningful insights from data, effectively finding answers to a given set of questions from data.
Data Scientists, on the other hand, are often expected to form their own questions about the data and seek to develop models and algorithms to predict the future using past patterns and trends.
🧑💻 AI Engineer
AI Engineers use AI and machine learning techniques to develop applications and systems that can help organizations increase efficiency, cut costs, increase profits, and make better business decisions.
Skills required:
Programming languages such as Python, R, Java, and C++.
Probability, statistics, and linear algebra: These are needed to implement different AI and machine learning models.
Big data technologies: Apache Spark, Hadoop, and MongoDB.
Algorithms and frameworks.
Thanks for reading. I believe this post will help you to navigate the Data Space especially if you are new to the field of Data Science and analytics.