Data science, data analytics and Big data are three terms that have been doing rounds in the technology world. These 3 have become important and knowing the right difference between them can be essential to choosing your career path.
You could be a big data expert, a data analyst or a data scientist depending on your sub-area of interest.
While the comparison is not necessarily a head-to-head one, you will have a fair idea of what you can expect from each of these fields – the technical and non-technical skills required, core responsibilities and the average salary paid to each. We will also be discussing the real-time applications of each in various domains.
At a high-level, we can say that data science is an umbrella that covers data analytics and big data.
The Basic Picture
We use a lot of social media channels every day. Let us say, you use Facebook to catch up with what your friends are doing. You see a page (brand) on Facebook that offers some awesome discounts when you shop with them online. Since most apps are integrated with Google or Facebook nowadays, you just login with your Facebook account and do some shopping. While you are at it, you forget that you have to cook something. Now, it’s almost lunch time and you are hungry. You search for nearby restaurants, and choose to get your food delivered at home. Well, again you login through your Facebook account and place your order. While you wait for your food to arrive, you decide on checking out some new games – and start playing. You get so engrossed you want others to join, send them requests, get more points and go on. You then purchase some groceries and other essential items online when your friend calls to make plans for the evening. You guys then decide to dine out and go for a movie. You check for online recommendations for a nice dine-in and book movie tickets online.
All this while, there is so much data that gets collected about you – the things you did today, what you searched, what you found, what you liked, what you did not like etc… All this data is collected at different sources. Some of it might be useful, some not. Think of similar or bigger magnitude of data collected for every such user.All this data – structured or unstructured, collected from different sources has to be collectively stored somewhere to be processed and shared with appropriate channels to be analysed for obtaining actionable insights.
These raw datasets that are complex and have multiple points are what the industry calls Big Data.
What should be done with these complex datasets is the next question you would think! Many companies – not just the websites you directly open – get data from these sites and try to analyse it from various perspectives. However, the data collected cannot be used as such. It should be cleaned, organized and transformed so that some patterns and behaviors can be identified by applying some algorithms on the data. This process of analysing the data using various tools and techniques is called Data Analytics.
So, now, we have the analysis conducted on the big data. The next step is to draw some conclusions based on the analysis. Based on the conclusions, we need to derive insights and apply them to the business model to achieve desired results.
For example, the next day, when you check out Facebook, you will be shown pages of similar interests – that you shopped, ordered your food from or played on – shopping, dining, gaming, clothing, movies and so on – this gives companies chance to promote themselves and also helps you experiment various new options.
This not only requires tools and techniques, but also business skills, visualization skills and creativity to make complex business decisions. This entire process of gathering data, transforming it, applying algorithms, creating models based on patterns, arriving at insights, visualizing them, creating new business use cases and predicting future targets – is called Data Science.
As we see, data science comprises big data and data analytics amongst other aspects. Let’s get into the details now –
Big Data Expert
Big data is defined with the 3V’s – Volume, Velocity and Variety. The volume of data is humongous (Terabytes, records, tables, files and transactions), the velocity is in streams, batches or real-time and the variety is unstructured, structured or semi-structured type of data.
Structured data can be obtained from excel, oracle, db2 or SQL database, text files etc… Unstructured data is mostly from social media platforms like twitter, Instagram, Facebook, YouTube etc… Semi-structured data includes data from cloud, xml data and json files amongst others.
Skills Required For A Big Data Expert
The most important technical skills required are –
- Apache Hadoop
- Apache Spark
- NoSQL & SQL
- Problem solving and analytical skills
- Statistics and mathematics
- Basic programming skills (Python/Java)
Roles And Responsibilities
- Collect data from various sources and store them in proper format.
- Process the raw data by writing sql queries, scripts, calling various APIs and web scraping
- Understand various data formats
- Prepare and transform data into usable and more suitable for analysis
- Work with cross-functional teams to perform high-level data analysis
- Understand and ensure completeness and accuracy of data including geospatial data
Tools And Techniques Used By Big Data Expert
There are many more, but the above are the most popular ones. Check more about tools in Data Science tools.
Applications of Big Data
There are many applications of big data with the most popular domains being finance, telecommunication and retail.
Finance – From insurance companies to banks, credit card companies and other investment banks all have to deal with large amounts of unstructured and structured (rather mixed) type of data. This data can be dealt with only through big data analytics. Processing such data can be helpful in detecting frauds, maintaining customer records, operational analytics and much more.
Telecom – The telecom sector is ever booming and humongous amount of data is generated every second. Such big data can be used to maintain better customer relationships, create custom offers and value-added services and expanding customer base.
Retail – One of the most common and competitive industries – retail – uses big data to serve their customers better by knowing their preferences. Companies collect data from unrelated data sources about a customer’s transactions, browsing data, social media preferences etc… to create specific offers and recommend products.
A big data expert, or specialist or engineer is paid a fat salary anywhere between $62,070 – $106,785 per annum. Although the salary varies based on multiple factors, this is the average range.
Data Analytics Expert
A little more in-depth job, data analyst takes the big data transformed by the big data specialist to the next stage – analysis. Data analysis is performed on the correct set of data by using many tools and techniques. Data analysis involves clearly defining a problem, analyzing why it happened, taking corrective measures and preparing reports and visualizations to take further business decisions.
- Analytical and problem-solving skills
- Intuitive about data
- Programming languages like Python/R, SQL
- Statistics and mathematics knowledge
- Ability to clearly define and break problem into sub parts and analyse each part in-depth
- Attention to details
- Creating algorithms and models to train machines (Machine learning)
- Create reports and visualize data, suggest future course of action for the business problem in hand
Roles And Responsibilities
A data analyst should have good knowledge of various tools and techniques other than being analytically and statistically strong. He/she is expected to interpret data using statistical tools and deducing patterns and trends to solve complex business problems.
Tools And Techniques Used For Data Analytics
There are loads of tools and techniques used by a data analyst starting from simple tools like Excel and SQL, to more advanced tools like MATLAB, SAS etc… Here are some more popular tools used by data analysts –
- Python & R
- Power BI
Applications of Data Analytics
Data Analytics finds use in a lot of domains like healthcare, travel, logistics management, Energy management, gaming etc…
Healthcare – With data analytics, machines can be used to track patient treatment and recovery, providing better care, better management of resources and equipment. This also leads to reduced cost and ability to treat more patients in a less time. Machines are less erroneous as they are completely based on data and history, leading to better and faster diagnosis of diseases.
Travel – Based on customer preferences and interest, travel guides can suggest new places of travel and related products can be shown to customers for purchase. Travel recommendations, ticket bookings etc… can be done in a more efficient way with more data in hand.
Logistics – Live tracking leads to better management of shipments. Also, delivery services can analyse data to find the shortest route, routes with least traffic based on various factors like rain, road conditions etc…
Energy management – Many companies are adopting energy optimization techniques, thanks to data analytics. Managing service outages, smart-grid management, building automated systems are some of the major projects that companies are looking at, to optimize both cost and performance.
Gaming – Data analytics helps gaming companies know the preferences of users, the time they spend on various platforms, their friends, relationships and more public details of users, enabling them to give a customized gaming experience.
Needless to say, with the kind of skills required for a data analyst, a fat package is inevitable. Data analyst’s salary depends on various factors like level of experience, roles and responsibilities, location and type of industry. The typical salary of a data analyst can range from $34,450 to $115000.
Data Science Expert
You might have seen many websites describing data science as an “interdisciplinary” field. Well, yes, it uses a lot of disciplines like science, statistics, mathematics, computing, tools and techniques, algorithms and other systems to obtain insights from the input data and form rules and algorithms by detecting similarities and patterns. Data science, as we discussed earlier, encompasses both data analytics and big data.
Essentially, if you are a data scientist, you can also take up the role of a big data expert or a data analyst. But, to be a data scientist requires a lot of technical as well as non-technical skills. Most of the time, big data experts and data analysts have in-depth technical knowledge, however do not understand the bigger picture or the entire data science lifecycle. Here are the skills required to be a data scientist –
- Knowledge of programming languages like R/Python/Java/C/C++
- Apache Hadoop/Spark
- Hands-on knowledge of SQL programming
- Statistics and machine learning
- Knowledge of data visualization tools like excel, tableau, power BI etc…
- Domain and industry knowledge
- Story telling skills – creativity and out of the box thinking
- Cloud computing
- Data blending and data mining skills
- Ability to make complex business decisions and suggest the next course of action based on the result of data analysis.
- Effective communication with business analysts as well technical teams by using the right jargons
Well that seems a lot at first, but most of it can be obtained just by some experience. You wouldn’t need all of these skills at any entry-level.
Roles And Responsibilities
A data scientist is responsible for –
- Defining weak and problematic business areas
- Coordinate with data teams to collect data from various sources based on relevancy and business requirements
- Design and evaluate statistical models and methods for complex business issues, like projections, clustering, classification, sampling, pattern analysis etc…
- Find new ways to predict customer behaviour and model, summarize and visualise the same
- Data mining and exploratory data analysis – initial (high-level) analysis before the actual analysis is started by data analysts
- Effectively communicate with various teams to ensure end to end completion of data science lifecycle
- Adept in learning new tools and technologies based on market demand and trends
- Analytical, problem solving and interpersonal (people) skills
Tools And Techniques Used For Data Science
There are loads of tools and techniques available for data science. Each tool has its own unique capabilities and companies choose them based on their specific business objectives. Some common tools and techniques used are –
- R and Python (these are on top of the list for any data related designations!)
- Apache Hadoop/Spark
Applications Of Data Science
Data science is widely used in almost all industries. Some typical applications of data science include –
- Recommender systems
- Healthcare systems like medical imaging, drug discovery, keeping track of patient health records etc…
- Fraud detection and other financial industries for surveillance, improving secure payment systems, predictive modelling for risk detection and monitoring etc…
- Logistics and data management
- Speech, image and face recognition
- Digital marketing
- Product comparisons and price suggestions
- AI systems like robots, cryptography, self-driving cars
Salary Of A Data Scientist
Data scientist is currently one of the highest paying titles worldwide. Even an entry level data scientist earns a fat package. For example, the average starting salary for data scientist is about $95,000 and based on the level of experience and other factors, can go up to $185,000.
Now that you know the difference between data science, data analytics and big data, and the associated details, you can easily make a decision on which line you want to start or continue your career with. If the job of a data scientist seems overwhelming for you, you can start by being a big data expert or a data analyst to gain technical expertise as well as strong interpersonal skills. With experience, you can eventually get into core data science jobs. On the other hand, since a data scientist needs both technical and business skills, you can always stick to being data analyst or big data expert, if you are more of a technical and analytical kind of person.