Data science is an interdisciplinary field dealing with information, statistics, and other forms of data. The main focus of this scientific field is to bring out relevant and essential information from the most unstructured and confusing data sources.
The study of data science involves dealing with massive data sets, data mining or extraction, and most importantly, understanding software and machine learning.
In recent years, the importance of data science has grown because the different strategies of data science are now used for industry management, the banking sector, tech start-ups, and others.
It is also predicted that data science will create more than 11 million jobs by 2026. Moreover, jobs relating to data science have increased by 650% since 2012.
Continue reading to gather more details on data science and why it is one of the most exciting careers in 2025.
Who are Data Scientists? What Do They Do?
Data scientists analyse and extract meaningful data insights from raw data sources. They are also known as a data expert. They deal with the data in five different steps as outlined below:
- Capturing or gathering data, which can be raw or unstructured.
- Maintaining the data captured by different processes such as data warehousing, data cleansing, and much more.
- Processing the raw data to make it easy to understand.
- Analysing the processed data using predictive or qualitative analysis.
- Communicating the data in readable or understandable forms like texts, charts, or others.
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How to Start Learning Data Science?
If one is still unsure about how to learn data science from scratch, then follow the steps outlined below:
1. Practising maths and statistics regularly
The discipline of data science is about statistics, math, and science. This is why it is necessary to keep practicing these subjects because algorithms and data models demand statistical question-solving approaches.
Thus, to begin learning data science, it is essential to first build a strong foundation in statistics and math. For instance, one must acquire basic knowledge of probability, the Bayes theorem, correlations, variance, and related concepts. Adequate knowledge of these concepts will help a better and quick understanding of data science.
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2. Define your goals
Data science is an extensive subject that involves various smaller concepts like data mining, data warehousing, predictive analysis, and much more.
Unsurprisingly, the broad scope of data science often creates confusion among beginners because they fail to decide which field they want to head towards.
Therefore, it is imperative to decide on what one wishes to choose and then proceed with the learning. This would avoid confusion and waste of time and resources.
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3. Give importance to communication
Data science is not just about collecting or analysing data; a significant part also deals with communication and presentation. Moreover, scientists must also communicate the data processed and analysed.
This is why an essential feature of a data scientist is being a good communicator. This is because the decoded data is understood only by the data scientist and the people associated with it.
The right way to communicate data is an essential step in learning data science. It doesn’t matter how great the analysis is unless scientists can explain it to other non-experts.
To upskill in data science, the data experts must understand the raw data, organise it and present it in a more straightforward form for customers to understand.
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4. Get comfortable with data
As the name suggests, data science is a lot about the analysis and interpretation of data. To ace this course, getting comfortable with data and working with unstructured and raw data is imperative.
Moreover, the provided data can be very confusing, but learning how to simplify it and work on it is essential. Once a person gets comfortable with data in all its forms, the task of further dealing with it becomes easy.
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5. Use data science tools
Another essential requirement in the process of becoming a data scientist is practicing the use of data science tools. Since we all know that data science is a vast field, each category falling under it has specific tools.
For instance, practicing tools like Tableau, Excel, Apache Spark, RapidMiner, TensorFlow, Apache Hadoop, Python, and more will make them easy to apply later for industrial purposes.
6. Sign up for an extensive data science course
The internet today is filled with several data science certification courses. Signing up for a data science course helps people learn about the field better.
Learning from industry experts and understanding basic concepts thoroughly is an integral step in the process of learning data science.
However, it can be challenging to filter out the right course as there are plenty of them. It is therefore advisable to first look for the system's level and requirements before signing up.
Reading online reviews and online counseling for a better understanding of the course is also useful. Moreover, you can go for classes that cover all the dimensions of data science.
While learning data science offers exciting opportunities, some might also be interested in pursuing roles like data engineers, whose role is crucial in building systems for data management. To explore the salary expectations for data engineers, visit the data engineer salary page.
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The Bottom Line
Extraction of data and making it tangible to generate business value might sound difficult or confusing, but data science makes it easy.
Sectors like health care, eConsultancy, banking, commerce, and many more use the best application of modern technology in their sectors in order to make their work more efficient.
Data science is much in need, and this is why the knowledge of this discipline becomes important. Data science can be taken up as a full-time career option or pursued as a side course. In all ways, it is known to be beneficial.
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Frequently Asked Questions
- Does data science require coding?
Yes, data science requires some knowledge of coding. It is always advisable to be thorough with some programming languages like Python, C/C++, Perl, SQL, and Java. The most commonly used data science language is Python.
- Can a beginner learn data science?
Yes, beginners can opt for data science too. For beginners, the data science course with Python language is considered to be the best. This course offers many things, such as machine learning, data analysis, and much more.
- Can a non-engineer student become a data scientist?
Yes, a non-engineer student can become a data scientist. In this case, the person opting for the course must first understand the fundamentals of statistics and coding. It will help them understand the subject better.
- How long does it take to become a data scientist?
For a person with no prior knowledge, it takes around 7 to 12 months to complete the data science course. The time taken to study data science may vary from one system to another.
- Is data scientist an IT job?
Yes, being a data scientist is an IT-enabled job. It focuses on helping different companies achieve their technological goals and handle unstructured data.
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