Data Scientists and Data Analysts are one of the world’s most “sought-after” IT professionals.
these careers are different and also similar to each other.
While writing this article, I asked some professionals what are the “differences between Data Science and Data Analysis“. There was one unique answer all of them gave me, “data analytics falls under data science.”
Is that right?
Is that what you know or what you’ve heard about?
Let’s find out… If you want to learn data analytics from scratch or you want to learn data science from scratch, you might want to focus really well on this article. This article will help shed more light on these career paths.
What is Data Analysis?
Data is everywhere and analysis in layman’s terms is the process of answering the how and why. You break a complex topic or concept down to get a better understanding of it.
Data analysis has to do with exploring data and visualizing it using various tools. You get insights from the data by figuring out the trends in the data, like, what the data is saying and make informed decisions.
It involves processing the data to find out why and how things happened. Collect the data, filter the data, get the finer data set of the large chunk and you get insights from it.
data analysis answers the questions,
- why is this happening (Diagnostic analysis, is usually based on analysis from past data or facts),
- how can we make this happen (Prescriptive analysis, based on past data, provides possible remedies to problems)
- when will this happen (Predictive analysis, also usually drawn from past data, to analyze data to check the likelihood of events happening)
- and what happened (Descriptive analysis, analyzes data to understand the situation or problem at hand).
What Is Data Analysis Used For?
- Data Analysis helps businesses make more informed decisions leading to increased profit.
- Data Analytics can also help businesses better understand and find their target audience, also leading to increased profit and a larger consumer base.
- Also, data analysis helps to personalize the customer experience through insights derived from customer data.
- It helps to understand the consumer needs, and wants and helps to supply products accordingly.
- Data analytics can also help businesses mitigate risks and handle setbacks
- Data analysis will also help you to measure the performance of your product and know the areas where the product had a huge success rate. It’ll help you know the areas you should focus on and the ones you shouldn’t in the future
Skills Needed To Be A Data Analyst.
Learning Data Analysis from scratch can be quite tasking and interesting. If you want to be a data analyst, here are the skills that will be very essential in your career.
SQL (Structured Query Language): Learning SQL is extremely important. It is an expected skill in any data analyst’s resume. SQL is needed to retrieve and query data.
It is one of the most commonly used and flexible languages, as it combines a surprisingly accessible learning curve with a complex depth that lets users create advanced tools and dashboards for data analytics.
Tableau: This is an example of a data visualization tool. We also have Power Bi. They are the most popular tools for visualization in the industry. Tableau helps to create interactive graphs and charts in the form of dashboards and worksheets to gain business insights. And all of this is made possible with gestures as simple as drag and drop, funny right? Let’s move on.
Microsoft Excel: Microsoft Excel is one of the most popular applications for data analysis. Equipped with built-in pivot tables, they are without a doubt the most sought-after analytic tool available. It is an all-in-one data management software that allows you to easily import, explore, clean, analyze, and visualize your data. We have various methods of data analysis in Excel. This makes data analysis way easier because you do not need to write code, just memorize formulas.
Communication Skills: Working as a data analyst, you must have good communication skills. You will need to communicate by presenting your findings to your team and the other technical or non-technical audience. Communication is one of the most important skills for anyone who works in tech. Communicating your findings, code, insights, solutions, etc, is very essential.
Python or R: Python is a popular multi-purpose programming language widely used for its flexibility, as well as its extensive collection of libraries, which are valuable for analytics and complex calculations. Python’s extensibility means that it has thousands of libraries dedicated to analytics, including the widely used Python Data Analysis Library (also known as Pandas). For the most part, data analytics libraries in Python are at least somewhat derived from the NumPy library, which includes hundreds of mathematical calculations, operations, and functions
Problem Solving: Problem-solving is a very essential skill in the workplace and even in our personal lives. You should be able to proffer solutions to any given problem. Every data analyst is a problem solver.
Data Analysis Tools
These are application programs needed to analyze data in order to solve any given problem. Below are some popular ones:
- Microsoft Excel
Data Analysis Training for Beginners.
You have now known a bit more about Data Analysis.
With the detailed information, you have now, if you feel data analysis is the one for you. Then, go for it!
You can join a data analysis training online, or take one data analysis course or the other.
Or maybe you’re in Akure and you’re looking to learn Data Analysis in Akure, and you’re looking for Data Analysis Training in Akure, where you will get to build real-life projects around different types of data.
You can check out our data analysis training in Akure. This course is offered full-time and part-time. The course will help you learn data analysis from scratch/beginning till you become an expert.
What is Data Science?
Data science is one of the most in-demand jobs in the 21st century. Data scientists are necessary and invaluable strengths found in all sectors.
Even Harvard Business Review called it the “Sexiest job in the 21st century” Data scientists have top in-demand skills that every organization needs.
Hai Varian, Chief Economist at Google and UC Berkeley defined data science as “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades.”
Just like the name implies, you study data. The Journal of Data Science described data science as almost everything that has to do with data. Collecting, analyzing, and modelling; yet the most important part of its applications.
Data scientists make discoveries and solve problems while swimming in data. Data scientists are problem solvers. They understand a problem and find ways to tackle it using data.
When a company comes to a data scientist with a problem, he tries to understand the problem by asking a series of questions and going deeper than just the surface of the problem.
The most basic need in a data-driven organization is the collection of data. Then, he proceeds to collect data from various sources, and organize and turn the results into solutions.
These skills are needed in almost every industry making data science get a relative increase in demand in companies today. LinkedIn listed data scientist as one of the most promising jobs in 2021.
Data science encompasses computer science, data analysis, machine learning and statistics. You don’t have to be a pro at everything.
Difference between Data Science and Data Analysis.
While both of them deal with big data, they are quite different and have their caveats as well, here are some differences;
- Data science focuses more on finding meaningful correlations between large datasets while data analysis as a branch of data science focuses on more specific answers to questions that data science brings forth.
- Data science aims at discovering new and unique questions that can drive business innovation while data analysis aims to find solutions to these questions and determines how they can be implemented in an organization to foster data-driven innovation.
- Data scientists use a combination of mathematics, statistics and machine learning techniques to clean, process and interpret data to extract insights from it. They design advanced data modelling processes using the prototype machine learning algorithms, predictive models and custom analysis while data analysis examines data to identify trends and draw conclusions. They collect a large volume of data to organize it to identify relevant data.
- Data science isn’t concerned with answering specific queries, instead parsing through massive datasets in sometimes unstructured ways to expose insights. Data analysis works better when it is focused, having questions in mind that need answers based on existing data.
- Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.
Data Science or Data Analysis; Which One Pays More?
Data science and data analysis are one of the most lucrative jobs in the tech industry. Every job in tech is lucrative though 🙂
According to Glassdoor job listing, the average pay of a data analyst is $63, 865 while the average pay of a data scientist is $99,007.
Wow. Quite okay! It is advisable not to learn just because of the money!
This is the end of the article and I hope this article gave you enough insight.