Objective
- Describe the different entities that form the best data ecosystem
- Describe and differentiate between the different types of data professionals, their roles and responsibilities in the data ecosystem
Introduction
In todays’ world, regardless of your professional background, you must have definitely come across the terms like “Data Science”, “Data Engineering”, “Business Analyst”, and more which are driving decision making in today’s professional world. But just like me, I am sure, you must have been bamboozled by what these terms mean and whether there really any difference between them? As it turns out, all of the jobs in the data field have a well-defined objective and in this article, I will tell you the difference between these job roles and the skills required for these roles.
Table of Contents
- Who are Data engineers and their responsibilities?
- What are the skills required to become a Data Engineer?
- Who are Data Analyst and their responsibilities?
- What are the skills required to become a Data Analyst?
- Who are Data Scientists and their responsibilities?
- What are the skills required to become a Data Scientist?
- Who are Business Analyst and their responsibilities?
- What are the skills required to become a Business Analyst?
Who are Data engineers and what are their responsibilities?
These are the people who develop and maintain the data architecture, data infrastructure and make data available for business operations and analysis. This data infrastructure includes databases, big data repositories and data pipelines for transforming and moving data between the data systems. Their goal is to provide quality data available for fact-finding and data-driven decision making. Data Analysts and Data scientists make use of data that data engineers provide. They work with other data professionals to ensure that the data matches their needs. When we look deep into data engineering, it’s all about selecting the right:
- Databases
- Storage system
- Cloud infrastructure or cloud platform
When we put together all these things, the data flow inside the organization is seamless. Data Engineers works within the data ecosystem. The main responsibilities of a data engineer are:
- Collecting source data: This includes extracting, integrating, organising data and data acquisition from multiple sources.
- Processing data: This includes cleaning, transforming and preparing data to make it valuable. It also includes maintaining distributed systems for processing data.
- Storing data: Storing data for reliability and easy availability of data. Data Engineer has to make sure that they should use proper and best data stores for the storage of procured data. They also have to ensure data privacy, security, compliance, monitoring, backup and recovery of the data.
- Making data available to users securely: Data Engineer has to make sure the APIs, services and programs for retrieving data for end-users.
Skills required to become a Data Engineer
- Good knowledge of programming languages
- In-depth understanding of relational databases and non-relational databases
- comfortable with command line
- Understands the working of the Hadoop ecosystem especially Map-Reduce and YARN
- Understands the concept of data warehousing
- Practical knowledge of working with Apache Spark
- Data ingestion with Apache Kafka
- Scheduling jobs with Apache Airflow
- Fundamentals of cloud computing
Who are Data Analysts and what are their responsibilities?
A Data Analyst is a data professional who transforms numbers into plain language, so organisations can make good decisions. These are the people who answer questions such as “Are the user’s search experience is good or bad on our website?” or “Is there a correlation between sales”. The role of a data analyst is to inspect and clean data for delivering insights. Their main task is to identify correlations, find patterns and apply statistical methods to analyse and mine data. Their day-to-day activities also include visualizing data to interpret and present the findings.
Skills required to become Data Analyst
- Good knowledge of spreadsheets
- Proficient in writing queries and using statistical tools to create charts and dashboards
- Should have a good understanding of programming language
- Should be able to convert business problems into a data problem
- Strong analytical skills and storytelling
- Good command in Data Visualization
Who are Data Scientists and what are their responsibilities?
A Data Scientist analyses data for actionable insights and create predictive models using Machine learning and Deep Learning techniques. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations. These are the people who answer questions such as “How many new social media followers am I likely to get in the next quarter?”
Skills required to become a Data Scientist
- Knowledge of a programming language
- Good hands-on experience of data visualization tools
- Good understanding of statistics, linear algebra and probability
- In-depth understanding of machine learning
- Basic knowledge of recommendation engines
- Practical knowledge of working with Time Series data
- Understanding of Deep Learning, Computer Vision
- Good hands-on knowledge of Natural Language processing
- Basic of Model Deployment
- Strong communication skills and presentation skills
Who are Business Analysts and what are their responsibilities?
Let me explain this term by taking an example. Let us suppose that there is a business whose sales are declining and now to find the answer on why sales are declining and what can be done, two parties are involved in this process. The first person is a data analyst, the role of a data analyst is to work with software developers within the same organization and to connect to different databases where they can find data related to sales etc. The role of a data analyst is they connect with the databases, they will build power BI or tableau dashboards or they will do data analytics in Python or excel and their job is to generate insights. So basically, data analysts produce insights and the business analyst role is to consume these insights and to make business decisions. Business analysts are not technical as data analysts but they have soft skills – they are critical thinkers, they are good at communication, they understand the business very well. So, if the data analyst is saying that in the southwest region, the company’s sales are declining and the customer churn out is higher maybe the business analyst will based on his knowledge say that in the southwest region the income level people are less so let’s run a special discount so that the sales will increase. Business Analyst is more focused on soft skills, critical thinking, problem-solving and domain knowledge whereas data analyst is more focused on technical skills.
Skills needed to become a Business Analyst
- Good analytical skills
- Good logical skills and leadership skills
- Proficient in Microsoft Excel
- Good negotiation skills
- Knowledge of Python and R
- Good hands-on experience of Tableau, Power BI or other dashboarding tools
- Strong presentation and communication skills
End Notes
In this article, we’ve learnt the different entities that play a crucial role in the data ecosystem. In a nutshell, Data Engineer converts raw data into usable data. Data Analysts use this data to generate insights. Data Scientists use Data Analytics and Data Engineering to predict the future using data from the past. Business Analysts and Business Intelligence Analysts use these insights and predictions to drive business decisions that benefit and grow their businesses.
Hope you enjoyed reading. If you would like to read more on Data Science then, head on to our blog.
