As the world has ushered in the Digital era in the late 90s, nobody would have thought we would come this far in just 2 decades. The analogue to the digital world journey has been nothing short of a miracle.
And at the centre of this grand revolution, there is one force that’s driving the whole system, DATA.
A human generates around 2MB of data on an average per report every second! Imagine the amount of data generated by the global population every year.
This comprehensive data can be extremely helpful in understanding users’ behavioural patterns, their likes & dislikes, interests, wants, needs, and goals. This Data is utilized by multiple organizations & businesses across the globe to develop their business.
Industries like FMCG, eCommerce, Fashion, Finance, Medical & even governments are tapping this huge amount of data for various goals. This is where Data Science comes into play.
In this blog, I have tried to answer multiple questions related to Data science like What is Data Science, What is the future of Data Science & what its scopes are.
So, without much adieu, let’s get started.
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What is Data Science?
Data Science or Data analytics is a study & analysis of a vast amount of data collected by multiple means through cutting-edge tools. This analysis is done to find behavioural patterns, interests, or any other meaningful information that can help a business improve its sales.
The Data collected & analyzed in the process can belong to an individual or a group of people. The rising competition in the market & demand for big data demands a constant analysis of data by professionals across the industries.
Now, as you have understood What Data Science is, let’s understand its cycle.
The Cycle of Data Science
- Data collection: This step includes capturing data through multiple methods. The methods can include data-capturing, data extraction, signal reception, or good old data entry. The data collected in this step can either be structured & unstructured.
- Data Maintenance: This is the next step where data is stored for current & future use in a structured way. The process includes various steps like Data Cleansing, Data Staging, Data Processing, & Data Architecture.
- Data Processing: Once the data is stored properly, the next step covers the Processing of stored data. These steps include data summarization, clustering, classification, Data Mining & Data Modelling.
- Data Analysis: This is where the real game begins. The whole data is analyzed across multiple metrics using advanced tools & techniques. The analysis includes Predictive Analysis, regression, qualitative analysis, Exploratory/Confirmatory & Predictive analysis.
- Data Communication: This is the final step where the Data scientists & analysts share the analyzed data with the organization. The form of data can be Tables, Charts & Graphs. The steps include data visualization, Business Intelligence, & Decision Making.
What is the future of Data Science?
As I have mentioned earlier, Big Data is playing a critical role in developing the digital economy. So it’s obvious you might be wondering about the future of Data Science & its applications.
Here is the list of some future applications of Data Science:
The IT sector is booming like a raging fire right now. And Data Science has played a major role in the phenomenal growth of this sector. Data drives everything in the IT industry & that’s why I believe Data Science will have a game-changing impact.
Every decision made by organizations, like marketing a particular product, altering a website, or targeting a new audience, depends on the data. Data Science can be very helpful in understanding the needs of potential customers and assisting businesses to offer better products & services.
This is another sector where Data Science will play a key role in the future. With a wide range of patient data available, it will become comparatively easy to identify various ailments early.
It will also offer a great opportunity for the global medical experts to share their vital knowledge about & assisting ailing patients from any corner of the world.
Tesla & Google’s self-driving cars are leading examples of how data will be playing a critical role in the development of self-driving cars. Services like Uber are also the result of a huge leap in data science.
The massive collection of data & their scientific analysis will help these organizations build a better & safer world in terms of transportation.
I believe Data Science will change the way people spend or invest their money. As you know, we tend to invest in the instruments that have performed well in the past. And with massive data at our disposal, we will be able to make more reliable financial decisions.
So Data Science will be changing the way we invest today in a much better way.
As the global population is exploding, energy consumption has sky-rocketed too. The world needs a way to analyze the generation & consumption of energy. Data Science can offer a detailed picture of the whole scenario, resulting in better planning & execution.
What are the career options in Data Analytics?
Now, if you are wondering what data analytics career opportunities are, here is a list of a few roles you can play as a Data Analyst:
- Machine Learning Analytics
- Corporate Strategy analysis experts
- Business Analyst
- Compensation Analysts
- Budget Analyst
- Sales Analytics
- Customer Analyst
- Credit analytics
- Fraud Analytics
What are the best Data Analytics tools?
- SAS: Widely utilized for Business Intelligence, Data Management & Predictive Analysis. SAS is one of the top Data Analytics tools available in the market.
Global firms like Google, Apple, Novartis, Delloitte, Netflix & Twitter are the leading users of SAS.
- Python: Python OOPL (Object Oriented Programming Language) is a powerful data analysis tool equipped with the data analysis library, Pandas. Pandas is n extremely powerful tool that can perform data visualizing, masking, merging, indexing and grouping data, data cleaning, and many more.
- Excel: Microsoft’s simple yet powerful tool can be an extremely helpful data analytics tool if utilized efficiently. No matter how big or small, almost every organization uses Excel to analyze critical data.
- Apache Spark: Designed for developers & data scientists, Apache Spark is a powerful tool for detailed data analysis.
- Power BI: Another excellent offering by Microsoft, Power BI, is an exceptional data analytics tool utilized by leading industry experts.
Apart from this, experts use several data analytics tools. You can check the whole list here.
Data Science is changing the world in a way we have never witnessed before. It surely holds a bright future for anyone who wants to enter the realm of Data Analysis. If you, too, plan to build a career in Data Science, you’ll have a wide range of opportunities.
Does Data Science have some future?
Yes, it has a bright future as every technology in the future will be data-focused.
Can I use excel for data analysis?
Yes, Excel is a great tool to start your Data Analysis journey.