Data science can be referred to as an advanced technology which aims to reduce the effort made by human beings in analyzing large quantities of technical data and make things easier for customers and end users. Data Science involves heavy use of coding, mathematics, statistics, as well as techniques like data mining, visualization, and machine learning to help business make informed data driven decisions. The types of business data a data scientists looks at can be categorized into two main types, namely, structured as well as unstructured data. While structured data consists of numbers and dates, unstructured data usually contains text, mobile activity, and video. Through the work done by data scientists businesses are better equipped to lead the direction of the business by having better awareness of metrics that directly impact their customers.
Read on to get more insight into the education and job expectations of a data scientists. Also we will go into more detail on ways data science is being used by businesses to generate value for their customers.
How do you become a data scientist?
Data science has turned out to be among the most promising career options in the present century. Data scientists are professionals who are accountable for handling big data and allow their employees to know the proper answers to their queries irrespective of whether it is for developing a marketing strategy or targeting the correct demographics for a particular product.
Although data scientists have different types of educational backgrounds, the majority of them have some kind of technical schooling. Being a diverse field, data science requires programming knowledge together with a comprehension of mathematics. In the following paragraphs, we have mentioned the essential skills required for becoming a data scientist at present.
1. Competence in Programming
Coding is required for analyzing and also processing information. Consequently, it is important for a data scientist to be competent in at least one programming language. It will be better to be comfortable in several programming languages.
2. Clear vision
It is important for the data scientists to design algorithms which are fast and effective. Therefore, creativity is extremely important for doing so. The purpose of data science is not only regarding why it ought to be done but also how it must be done.
3. Mathematical aptitude
Data science needs to churn out raw numbers and data, and therefore, mathematical talent is a must.
4. Determination
At times, it can be frustrating to work with a continuous influx of information. Therefore, it is important to have the required determination to become successful at these times in the best possible way.
5. Analytical ability as well as sharp focus is some of the other essential skills required to become an accomplished data scientist.
Here is a step-by-step guide on how you can become a data scientist.
Step one – Make sure that it is actually meant for you
It is vital to verify whether the role of a data scientist is actually meant for you. Data science can be considered to be an extensive branch of studies, and therefore, you have to ensure that you will be capable of taking the heavy burden on your shoulders. You’ll come across a number of preliminary courses on data science on the Internet some of which are completely free to access. You’ll also be able to take the help of YouTube videos. You can go for the subsequent step after becoming sanguine regarding pursuing data science.
Step two – Obtain a relevant bachelor’s degree
Some degrees which will be useful for the data scientists consist of the following:
• Economics
• Applied mathematics
• Data management
• Information technology
• Physics
• Statistics
• Mathematics
You can opt for boot camps which will be a fantastic way of accelerating things apart from your main degree. One more useful activity which you can go for along with your main academic course will be to enroll for MOOCs (Massive Open Online Courses). These courses permit unlimited participation as well as open access to education materials via the web.
Step three – Choose an area of interest
A variety of paths converge to a successful career as a data scientist. In most cases, data scientists begin from an undergraduate level in Mathematics, Computer Science, Statistics, and so on. These are ideal for procuring jobs like that of a management analyst, data visualization specialist, and so on. Nevertheless, some receive specialized concentrations by means of masters degree programs like machine learning, artificial intelligence, applied science, or data science. Many of them are also pursuing a doctorate in the likes of enterprise science analytics and business solutions.
Step four – Get certified
Certifications are an essential part of the curriculum vitae of any contemporary profession particularly one which belongs to the IT sector. You’ll come across plenty of certifications which are obtainable for all those who have an interest in data science. Apart from this, one will be able to get these certifications from a number of great places as well.
Step five – Procure a role
After completing all the educational and academic requisites, it is important for you to procure a lucrative role in the data science field. At present, data science is an extremely varying field and you will be able to go for a number of specialized roles. Moreover, with the current demand it is also feasible for any beginner with the necessary education to become a data scientist.
Why do companies hire data scientists and what do they do?
At present, an increasing number of companies are hiring data scientists who are aware of drawing valuable insights from a large amount of data. Below, we have mentioned the essential reasons why data scientists are being hired by companies these days.
1. Mitigates the risk of losing clients
One common task handled by data scientists will be to analyze consumer churn patterns which enable the business to react proactively rather than reactively in case it observes a trend of clients favoring its rival companies. This will help it to send out lucrative deals for winning back leads. In this way, it will be possible for the company to bring back the drifting consumers into the fold once more.
2. Promotes the expansion of the business
It might be possible for any company to discover fresh business markets that is interested in its products or services with the help of a data scientist. Although the marketing campaign of a company might be robust, a data scientist will be capable of reviewing it and figuring out the type of new clients gained from a particular initiative, thus helping the company to fine-tune its campaigns in the future.
3. Identifies the target audience
Virtually every business makes use of different methods for collecting customer data right now. However, in case this data is not properly used, it will not be able to offer any value. A data scientist will help the business to take available information and combine it with other data points for drawing insights and learning more regarding the target audience along with the customers.
4. Helps the management to take important business decisions
The arrival of the data scientists and their capability of handling information have eliminated the need of taking high stake risks. Data scientists will be able to develop models by making use of existing information which can trigger lots of potential actions helping the management to get maximum profit. It is important to take important decisions and also implement effective changes in the competitive business landscape of today. A data scientist can play a significant role in this regard.
Next, we will mention what is being done by a data scientist at present.
This might be an extremely loaded question given that it is possible for a data scientist to do several different things at present. In a nutshell, we can assert that a data scientist is going to analyze business data for influencing business as well as technical decisions to improve the way in which the business operates for the customers. However, it is vital to note that all data science jobs are different and it is actually going to depend on the team of which you are a part. Below, we have provided some examples of data scientist jobs at present:
• Data cleaning, preprocessing, and ETL
• Data modeling, testing several models and finding out the best hyperparameters for all these models
• Interfacing with DevOps engineers or software developers for taking the built models and using the outputs as automatic decision-makers
• Interpreting a business issue to a statistical or mathematical problem which is finite and can be solved
Example day of a data scientist’s work
The typical day of a data scientist will be starting in the morning at approximately 9 AM with a half an hour-long meeting with his team where they would be discussing the advancements of the ongoing projects and also how to overcome any difficulty. Following this, the data scientist will be going through the emails which he received at night and react accordingly. After that, he will work on his current project out there.
In the afternoon, he will be helping his colleagues in improving their models by testing the present model on the genuine data, recognizing the false negatives/positives and also developing fresh training examples for solving the problem.
His day is going to end at approximately 5:30 PM with a half an hour catching up on the tech blogging/news and latest trends in his field to stay current on new techniques and technologies to apply to his trade.
Is data science harder than software development?
In simpler terms, data science indicates extracting or converting the information in different forms to knowledge. The company will be able to make use of this knowledge for making sensible decisions for improving the business. Data science helps to make the companies intelligent enough for pushing and selling their products.
On the other hand, software development can be considered to be a structured approach to design, development, and maintenance of the software so as to avoid the software product’s low quality. Software development helps to make the requirements clear such that it will be easier to proceed with the development.
Thus it is evident that data science and software development are different skill sets with several common ones. While overall data science will be harder for any software developer, software development is going to be tougher for any data scientist out there.
Going deeper into our tech driven lives, both Data Science and Software development are only going to continue to be more important in society. Data scientists and Software developers and engineers will often be found working side by side to create large scale technologies such as self driving cars and digital voice assistants.
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