Data Science vs Data Analytics
With significant technological breakthroughs and a proliferating business environment, there is a dire need to understand what lies behind it. It is ‘data.’ Big data is already a major component of businesses these days. This is because of the insights it provides and how it leads to a profitable business. But the question is, who will process this data? Who will analyse it? Who will drive out conclusions and solutions out of it? This is the work of either a Data Analyst or a Data Scientist!
Even after so much evolution in the data field, there is a considerable gap in understanding these two job roles. In this era of big data, fundamental data literacy is a valuable skill to possess. By data literacy, we mean the potential to interpret, analyse and question data. And this is where the words Data Science and Data Analytics come into the picture.
Going with the trend and grabbing some futuristic job options, Data Science and Data Analytics are the most lucrative ones. With the increasing integration of Machine learning and Artificial Intelligence in our lives, Data Science and Data Analytics will continue to hold long-term career potential benefits. Data will continue to drive businesses, and some of the best jobs offers in the market too. For this very reason, leading educational platforms to offer many combination courses on Data Science and Business Analytics, which will help establish the usefulness of both separately and as a unit.
Are you someone looking for some upskilling and confused between courses on Data Science and data analytics? This write-up will help you move forward and answer all your questions about learning Data Science or opt for data analytics. So, let us first understand what goes behind Data Science and data analytics, although they both play with data!
What is Data Analytics?
Data Analytics concentrates on processing and performing statistical analysis on existing datasets. The analysts focus on creating methods to process, capture, and organise data to find solutions to current problems and find ways to present data. This field produces results to implement immediate improvements. It also analyses and mines the business data.
For example: Finding why a particular marketing strategy did not prove fruitful? Finding why the sales dropped for a specific quarter?
What is Data Science?
This field focuses on finding insights from large sets of structured as well as raw data. It finds answers to problems that have not been discovered yet. It uses several techniques like prototypes, predictive models, algorithms, and statistical models to shape the raw data into more meaningful data. It is used to discover the right business questions and find answers to them, leading to innovations.
Interestingly both the disciplines work on data. What differentiates each from the other is the scope of work. Let us put it in simple points of understanding:
- Data Analytics analyses structured data to find answers to issues at the current moment. In contrast, Data Science works on raw and structured data to create new questions that might lead to future innovations. It is used to re-model large datasets. You can say data analytics is a part of Data Science.
- The major fields where Data Analytics is applied are gaming, healthcare, travel, and industries with immediate data requirements. In comparison, Data Science is applied in Artificial Intelligence, Machine Learning, Corporate Analysis, and Search engine engineering.
Skill sets for Data Science vs Data Analytics
Here we will be explaining the Data Science prerequisite skills as well as data analytics prerequisite skills:
Data Analytics- A person looking for a job in this field should be well-versed with:
- Intermediate Statistical knowledge.
- Excellent problem-solving skills
- Good knowledge of Excel and SQL databases to find suitable dataset.
- Knowledge of Statistics tools like R or SAS, Python.
- Must have the experience to work on BI tools like Power BI.
The good news here is that the analyst doesn’t need to belong to an engineering background.
Data Science- The job applicant must be well-versed with Math, Predictive Modelling, Advanced Statistics, Programming, Machine Learning, and a few more:
- Expert in SQL and NoSQL databases like MongoDB and Cassandra.
- Proficient in using big data tools like Spark and Hadoop.
- Possess experience using data visualisation tools like D3.js, QlikView, and Tableau.
- Proficient in programming languages like R, Python, and Scala.
Job Roles for Data Science vs Data Analytics
The Data Analyst Job description demands proficiency in:
- Data Cleansing
- Exploratory data analysis
- Develop KPI’s and visualisations
- Track new patterns with the help of different statistical tools
The basic skills a Data Scientist should hold are:
- Exploratory Data Analysis
- Cleansing, processing, and verifying data integrity
- Obtaining business insights using algorithms and machine learning techniques
- Obtain new data trends to make appropriate future predictions
Which one to pick?
There is a multitude of courses on Data Science and data analytics available online. An individual looking for a job in the big data world should consider opting for such a course. To more appropriately understand the difference between both the disciplines, an aspiring candidate should consider learning the technologies and tools used in the respective fields. A practical or hands-on experience of database and analytical tools is the secret behind earning a good name in the data industry.
If we talk specifically about the best data analytics courses online: these provide comprehensive training on tools like SQL and Excel to analyse and manipulate large datasets. The course uses Tableau and Power BI to generate dashboards and displays to reveal analysis results. Data Analytics is an amazing career option, even if you are from a non-engineering background.
The online Data Science courses are usually taught in the Python programming language. Python has become popular in the Data Science field because of its comprehensive repository around statistics, analytics applications, and machine learning.
According to the career perspective, the data analytics course is for individuals with 2-5 years of experience and interested in building data models. It is for those who can use SQL, Excel, Python, Tableau, and Power BI to perform data analysis and build dashboards. While to become a Data Scientist, you should have 1-10 years of experience. It would help if you were interested in learning Python to execute Data Science projects.
The Data Science course is generally opted by individuals working as Business analysts, BI engineers, Architects, IT application engineers, and Data Analysts who further want to upskill their analytical skills. The data analytics course is beneficial for people working as data warehousing professionals, database administrators, QA engineers, and individuals in Marketing, Sales, Finance, SCM, and Ops. These people want to learn better data manipulation and processing skills using SQL or Excel and work with Tableau, Python, and Power BI to create reports, dashboards, and visualisations.
There is a significant difference between these two disciplines. But what is common apart from the fact that they both use data is that both fields generate the most sought-after job profiles. It is because of the tons of data we create every day through various devices. And to stay competitive in the market, companies want individuals who can efficiently process, manipulate and re-model this data. So, if you love everyday adventures at the office, love challenges, crazy programmers, hunger for data analysis, and most importantly, are creative, you should definitely look out for such career options! Make sure to choose the perfect course for you with many options to help kickstart your Data Science training.
If this write-up has given you a boost to start a career in the Data Science industry, sign up for an online business analytics course today!
Read more: Get Complete Data Science Course In 2021