We are often confused by these three roles and that confusion is understandable. We will explore these job roles and know about them in detail. We will also go through an example to understand better. Considering the overlapping skills and sometimes under defined job roles and responsibilities, we get clouded by the actual roles and the responsibilities.
Enough is said on the new oil of today’s generation. Data is here to thrive and the people involved in and around creating this system and algorithms have a way to go. With the hottest skills in the current market to one of the high paying jobs, data comes in all shapes and sizes!
Let us now look at the difference between data science and data analyst , and Big data professional.
What is Data Science?
It is a combination of different phases like Mathematics, Statistics and programming, Machine learning and other such related concepts to understand and analyse the business and business events concerning data. It also involves solving the problem in various ways in arriving at a solution. It has an ingenious way of capturing data that was not done before and it involves cleansing, preparing to analyse the data and at the last the ability to look at things differently.
What is Big Data?
It refers to a large amount of data from various sources and different formats like audio files, video files, jpeg files, text files and many more. Traditional data processing systems are incapable of dealing with such large amounts of unstructured data. It is further expressed in terms of volume variety, veracity and value.
What Is Data Analytics
Fantastic about discovering useful data to support decision making? It involves various aspects like inspecting, cleansing, transforming and modelling data. To do so it uses qualitative and quantitative techniques.
What does a data scientist do?
Data scientists perform analysis to get various insights from the data. They apply machine learning algorithms to predict various occurrences of a particular event in the future.
They examine and explore the data from multiple data sets and also identify new business questions which add value to the business market.
They are also involved in finding hidden patterns and co-relation and other useful business information from the data sets which adds meaning to the overall analysis.
What does a big data professional do?
They architect the distributed systems for all the data that is collected and they provide scalability, security and concurrency. And they build a large-scale data processing system to process huge amounts of unstructured data. This is later processed with various big data tools like Hadoop, Kafka and Spark and ensures the network connectivity is up and running.
What is the role of a Data analyst?
To put it simply a data analyst put up the data into a plain language to be understood by all. The job of a data analyst is to take the data and use it to help the company make better decisions.
Firstly they acquire, analyse and process the data.
Then this is used to find the insights. Lastly, this is converted into the creation of data reports using various reporting tools like Tableau, MS Excel etc.
A bit confusing right? Let us break it down.
A data analyst will do the day-to-day analysis of the data, but a data scientist is more involved in providing the solution to ‘what ifs?’ like what if there is inflation in the market or what if there is a financial crisis. These kinds of situations are dealt with by data scientists and not analysts.
In addition to this Data Scientist, explores and examines the data from multiple disconnected sources. Whereas, data analysts usually look for data from a single source like a Customer relationship management system.
Lastly, the data analyst will solve the question given by the business, unlike the data scientist who is responsible for curating questions that will benefit the business.
Now as we are clear with the roles and responsibilities to a certain extent, let us now understand the skills required for each of these roles.
Skills required for a data scientist:
- Statistics and Analytical skills
- Programming knowledge
- Data Manipulation and Analysis
- Data Visualization
- Machine Learning
- Deep Learning
- Big Data
- Software Engineering
- Model Deployment
- Communication Skills
- Storytelling Skills
- Structured Thinking
To become a data scientist, you must have several skills. To find insights into the data captured you need Statistics and analytical skills. To predict the future based on past patterns you need data mining activities and co-relational skills which come under data manipulation and analysis. You should understand what machine learning is and advanced concepts like deep learning. You should have an idea about different machine learning techniques like supervised learning, unsupervised learning and reinforcement learning. As programming is a based tool for data science it is expected to have a piece of in-depth knowledge about it. Python and R programming languages are widely used in the industry. Apart from all these, you must also know big data tools, data visualisation and reporting tools like Tableau, Power BI.
In the soft skills part, you must have Communication skills & Storytelling skills where you can tell the findings in a form of a story that will captivate the focus of the listener.
Skills required for big data Professional:
- Data Warehousing
- Computational frameworks
- Quantitative Aptitude and Statistics
- Business Knowledge
- Data Visualization
As a lot of customisations might require in the handling of data, big data professionals need to be comfortable with coding. In data, warehousing comes experience with relational and non-relational databases. MySQL, MongoDB and Cassandra are some of the tools of non-relational database tools. A good understanding of the frameworks such as apache-spark, apache-storm along Hadoop help In Big data processing, which can be scaled to a great extent.
Fundamental of all the analysis is good knowledge of statistics and linear algebra.
To keep the work of analysis focused, to validate and evaluate the data, one of the most critical Big data skills is to have a good knowledge of the domain one is working in. So, it becomes a tedious task to find someone who has both good domain knowledge and expert programming skill to know how to put the programs in the context of the business goal.
To get your message over and engage your audience, you must be able to create an engaging tale with facts. If your insights aren’t simply and quickly found, you’ll have a hard time persuading others. Data Visualization may have a significant influence on the impact of your data for this goal. Analysts utilise high-quality graphs and charts to show their findings clearly and straightforwardly.
Skills required for a data analyst:
- Data Visualization
- Data Cleaning
- Programming language: Python or R
- SQL and NoSQL
- Machine Learning
- Linear Algebra and Calculus
- Microsoft Excel
- Critical Thinking
You need to have a thorough understanding of data warehousing concepts like SQL and NoSQL. You need to be familiar with computer software which includes scripting, query language, knowledge of spreadsheets and basics of statistical language. An idea about programming and big data tools will be of great advantage. As you can make out there are skills that are overlapping. But to clear some of the air here, a data analyst is required to present the data well whereas programming and machine learning concepts are good to know concepts. If you want to learn and get skilled about the Data analyst you can get enrolled in the Data analyst course.
In the following part, we will take a real-world example.
What is the role of a Big data professional?
Consider streaming service Netflix. Here there is a huge number of data generated by Netflix which is unstructured. And if we try to process this data using the traditional data processing system, the processing will not happen. Due to this reason, Big data professionals create an environment using various big data ecosystem tools.
What is the role of a Data Scientist?
Their main aim is to work on how the Netflix service can be optimised for user experience. Understanding the impact of quality of user behaviour. User behaviour refers to the way he reacts when he is using the platform. And data science revolves around predicting the user behaviour in the future based on his current habits. Because you know habits are a recurring event! Now, a data scientist will work on two factors in collecting the data one is buffer rate, other is the bit rate. This translates to how often the playback is interrupted and the quality of the picture served or seen. Based on this behaviour the quality of user experience is predicted. Consider your watching money heist and are pausing at action scenes and replaying them.
Then the data is processed that a certain timestamp has more play than others based on this algorithm. The genre of your interest is learnt by the machine without even you choosing it. Isn’t that amazing? Now the data scientist is responsible for coming up with such a detailed and improvised model, where the genre’s taste is decided just by you not typing a word.
Not just this, even the content which comes live on Netflix goes through various processes beforehand. The content from the studio lies as the raw digital asset with Netflix. This is later added with captions optimised for the quality, text mining techniques and feedback from the initial viewers on some scenes. And for all these the data scientists came up with a machine-learning algorithm for the final encoding of the video.
What is the role of data analytics?
Data analytics is one of the important subsets of data science. It is a helper to data science. Let us now see with an example. Numerous users in Netflix have their own set of choices. So, what does a data analyst do? Data analyst orders a Netflix collection to create a personalised profile for each member. So, each of the members has an entirely different set of video streams. And then the data analyst brings out the top personalised recommendations from the entire catalogue title that are top on ranking. By capturing all the activities by the users, further recommendations are curated. This also sorts if the user will continue to watch, rewatch and pause.
And, this is how data scientists, big data professionals and data analysts have an important role to play. This is how the three join forces together to increase the revenue of the company. I hope the differences and similarities are now a bit clear.
In this article, we saw what is data science, data analyst and big data professional. As you might have seen some of the skills are overlapping and this is true in the Data industry. Sometimes the job roles of smaller companies are overlapping as well.
The future prospectus of all of these roles is important and are in high demand. If you are more of a tech guy it is suggested to look at the data science roles. If you are leaning toward the business decision then you have a data analyst on your way. If you are good at getting the data and producing a system for processing a humongous amount of data then a big data professional is for you.
Yes, this is a quite generalised way of saying things but, to understand things clearly, you have to look at the bigger picture as well as magnify it to the smallest possible detail.
I hope this will help you know about these paths and also choose one if you are interested in making a career out of it.
It will be a Datanstic! Journey.