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Ritika Singh
Ritika Singh

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Data Science Certification

Data Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data Science is related to data mining and big data.

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building frame work and solutions to store data. Now when the frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science Certification is the secret sauce here. Data Science is the future of artificial intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business. In this blog, I will be covering the following topics.

Need for Data Science.
What is Data Science?
How is it different from Business Intelligence (BI) and Data Analysis?
Advantages and Disadvantages of Data Science
Needs for Data Science
We hear a lot about the trends and applications of Data Science these days. One real life example is

Tesla’s self-driving cars. These cars collect real time data from the surroundings with the help of camera, sensors, and ultrasonic sensors. After collecting the real time data, they process the data, visualize it, and use software algorithms to receive the best suitable actions to follow while navigating themselves on a safe drive in the coming years, these self-driving cars would revolutionize the automobile industry.
Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools, unlike data in the traditional systems which was mostly structured, today most if the data is unstructured or semi-structured. Data trends in 2020 shows that more than 80% of the data will be unstructured
How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income
What is Data Science

Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is difference between BI and Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

To understand the essence of Data Science, here’s the Data science life cycle

How is it different from Business Intelligence (BI) and Data Analysis
Business Intelligence: BI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboard to answer the questions like quarterly revenue analysis or business problems. BL can evaluate the impact of certain events in the near future.
Data Science: is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open ended questions as to “what” and “how” events occur.
Advantages and Disadvantages of Data Science

It’s in demand
Abundance of Positions
A highly paid career
Data Science is versatile
Data Science makes Data better
Data Scientists are highly prestigious
No more boring tasks
Data science is blurry term
Mastering data science is near to impossible
Large amount of domain knowledge required
Arbitrary data may yield unexpected results
Problems of data privacy

Top comments (1)

iteducationcentre1 profile image

Data science certifications often provide networking opportunities within the data science community. Many certifications offer access to exclusive forums, events, and communities where professionals can connect with peers, share ideas, and collaborate on projects. This network can be invaluable for career growth and staying abreast of industry trends.

However, it's important to note that while certifications are valuable, they should not be seen as a substitute for practical experience. Hands-on experience and real-world projects carry significant weight in the field of data science.
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