As the name implies, data science is the area of study that investigates enormous volumes of information using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions based on that information. Predictive models are built using complex machine learning algorithms in data science. The data used for analysis can come from many different sources and be presented in various formats.
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Are you a budding Data Science professional? Does the thought of getting rejected by hiring managers keep you up at night? If yes, you are certainly not alone. A hiring manager goes through hundreds of Data Science profiles every day. The only way to make it to your dream job role is by making yourself stand out in the job market. This is where your Data Science portfolio steps in and saves the day.
Suppose you are working as an analyst, and your manager asks you very specific questions like “what is the current sales trend?”, “what are my customers buying?”, “is my customer buying a mobile? which is the most likely product he may buy along with it?”, “how much do we need to produce to meet the market demand?” how would you answer them?
To answer all the questions above, one needs to start with mining data. It also helps machine learning engineers in processing the data. Keep reading to...
Power BI is recognized as one of the most in-demand skills in the domain of Data Science. It is used to visualize any data using the unified, scalable platform for self-service and enterprise business intelligence (BI) that’s easy to use and helps you gain deeper data insight.
In this article, you will learn the following:
- The fundamentals of Power BI
- Types of visualizations in Power BI
- Features of Power BI
- Components of Power BI
Let us dive into a situation where you are driving a vehicle;...
As the amount of data generated every day increases exponentially, creating a logical, simplified database has become the need of the hour. This is why data modelling has become one of the most crucial aspects of Data Science. In this article, understand:
Businesses need data to thrive. They scrounge through petabytes of data they are surrounded by to unearth business intelligence. But not everyone in an organization is data-savvy enough to comprehend the information extracted using data.
As the name implies, data science is the area of study that investigates enormous volumes of information by using modern tools and techniques in order to find unseen patterns, derive meaningful information, and make business decisions based on that information. Predictive models are built using complex machine learning algorithms in data science. The data used for analysis can come from many different sources and be presented in a variety of formats.
Data is becoming increasingly crucial for making critical business decisions. All types of businesses use data to determine which new products to create, which markets to enter, which investments to make, and which potential clients to pursue.
Engineers, for sure, make a unique breed. Their consequential acts of leveraging the rules of our world to build things that nature readily didn't give us are what led to the advancement of humankind in the first place. Due to the very same reason, the field of engineering is never going out of demand.
''Data Analyst' is one of the most in-demand and heavily-relied upon roles in 2022 and will continue to be so for the foreseeable future. If you have a strong foothold in Statistics, Math, and Computer Science there is no question that you have what it takes to become a data analyst!