Big data has become a key component of the tech industry converting insight into action and driving data for taking action. However, creating an enormous database requires careful consideration and hands-on experience acting on proper tools for collecting only those data that matter the foremost for any business. These reasons contribute to the rapid climb of the info science field, both within the market; and online and offline modes of teaching.
However, it can always be confusing to tell apart between data analytics and data science, but they’re always connected. The results they produce are different from one another, and that they need a distinct approach for showing particular results. to grasp the differences and similarities of the concepts, let’s have a look at the definitions, differences and similarities, and career opportunities in both fields.
What is data science?
Data science is an interdisciplinary field that concentrates on gaining actionable insights from large datasets of raw and structured data. And different processes are involved in finding the most probable and exact answers to the search queries which we don’t know or we think that we may think of getting the best version of what we already have.
Data scientists’ primary goal is to ask questions and determine hidden opportunities, with less concern for specific answers and more emphasis on finding the right question to ask. Experts accomplish this by forecasting trends, analyzing data, visualizing data, and finding better ways to research information.
Skills needed for Data Science
Proficiency in Advanced Statistics, Mathematics, Machine Learning, Predictive Modelling, Programming along with –
- Proficiency in using tools like Spark and Hadoop.
- The knack of SQL and NoSQL databases like MongoDB and Cassandra.
- Proficiency with data visualization tools like Tableau, D3.js, and QlikView.
- Hands-on knowledge of programming languages like R, Python, and Scala.
Data Scientist’s Roles and Responsibilities include–
- Processing, cleansing, and verifying the probity of data.
- Exploratory Data Analysis
- Obtaining business insights using machine learning algorithms and techniques from the data set.
- Identifying new trends from the data to make predictions and strategies for the future.
- Also presenting the insights to the team and management in an easy manner.
What is data analytics?
Data analytics refers to using different techniques and processes to capture, organize, and process the data to discover useful information and study trends and patterns in emerging markets. This further helps to get a competitive advantage over competitors.
More importantly, it supports in producing results that may cause immediate improvements. Data analytics includes various branches of broader statistics and analysis, which combine diverse sources of information while locating connections and simplifying the results.
Skills needed for Data Analytics
Good knowledge of Statistics and excellent problem-solving skills along with
- Know hows of Excel and SQL databases to slice and dice data.
- Hands-on experience of working with Business Intelligence tools like Power BI.
To become a data analyst, you don’t necessarily need to hail from an engineering background. Having strong skills in statistics, databases, modeling, and predictive analytics is all you need to become a data analyst.
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Data Analyst’s Roles and Responsibilities include
- Collecting, storing, and analyzing the data
- Cleansing of data
- Discovering new patterns and trends in data using statistical tools.
- Develop visualizations and KPI’s from the data.
Differences between data science and data analytics?
While many of us use the terms interchangeably, data science and data analytics have a lot of differences based on the kind of work done.
Data science is an umbrella term for a gaggle of fields that get accustomed to mine large datasets.
Whereas Data Analytics is to discover actionable insights that will be applied immediately to support existing queries.
Choosing a Career between Data Analytics and Data Science
To decide which one of them is best aligned with your professional goals, you should consider these three key factors.
Consider your personal background- As mentioned above, data analysts analyze big data sets to identify trends, develop charts, and create visual presentations to help organizations make more data-backed decisions and strategies. Analysts typically seek an undergraduate degree in technology, science, engineering, or math, and sometimes even an advanced degree in analytics and experience in related fields like programming, modeling, databases, and predictive analytics.
While Data scientists are more focused on constructing and designing new processes for data production and modeling. They use different techniques like machine learning and data mining to scour through data. An advanced degree is essential for professional advancement in data science. Data science is much more technical and mathematical [than data analysts] in nature.
Consider your interests- If you are excited by statistics and numbers, or passionate about computer science and business then data analytics is the perfect choice for you. Data analysts being the gatekeepers for their organization’s data, work with databases to unveil data points from complex sources. As an aspiring data analyst, you should also have a thorough understanding of the industry you would like to work in.
On the other hand, data scientists are required to have a blend of computer science, math, and statistics and as well as the knowledge of the business world.
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Data scientists and data analysts both look quite similar from the outer shell but have many differences in roles and responsibilities, educational requirements, and career trajectory.
No matter how you look at it, qualified individuals for data-focused careers are in profound demand in today’s job market, thanks to the internet and businesses’ strong need to make sense of their data.
Both data scientists and data analysts are amongst the most in-demand job roles as organizations embrace these professionals to lead the path towards competitive advantage and technological change. If you are creative, love challenges and numbers, it’s time you think about these career options. And once you have considered factors like personal interests and your background, you can easily decide which career path is the right fit for you and get started on your path to success.