Definition of the topic not universal, and there is no such agreement between companies; instead, the same title may require a different skill-set. But we can state that Data Scientists are new-generation data analysts who have good computer skills, programming, statistics, and mathematical skills and a great sense of unearthing valuable data lying under the data ocean.
According to Harvard Business Review, data scientist is “The Sexiest Job of the 21st Century” in October-2012. Of course, we can’t cover every potential data science job title that a company might use in this article. Still, we can talk about some of the significant roles like data analyst, data scientist, and data engineering in the data science universe, how they differ, and the progression in the field if you’re starting in that role.
1- Data Analyst
Data analyst jobs is typically considered entry-level data scientist job in the DS area. However, it is important to note that not all data analysts are junior; therefore, data analyst salaries vary widely.
As a data analyst, your primary job is to analyze a company or industry data and use it to answer business questions, interpreting those answers and communicating them to other teams in your company. For example, To assess the effectiveness of a recent marketing campaign and find strengths and weaknesses, as a data analyst, you might be asked to examine sales data from the campaign.
2- Data Scientist
Data scientist is another data science job that you can apply. Data scientists do the same things as data analysts, but they also use data to build models to predict target features. A data scientist is also supposed to search for new patterns, unseen trends such as management could not capture. This point brings data scientists more responsibility and more freedom.
A data scientist might be asked to evaluate how a change in marketing strategy could affect your company’s bottom line. First, of course, data analysis would be necessary (acquiring, cleaning, and visualizing data). Still, it would also probably require building and training a machine learning algorithm that can predict the future based on historical data.
Data scientists are responsible for exploring information from large amounts of organized and not organized data to develop a solution for business needs and targets. Accordingly, the data scientist’s duty is becoming increasingly important as businesses rely more heavily on data analytics to make a decision and lean on automation and machine learning as the main components of their IT strategies.
A data scientist’s main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientist’s analysis need to be easy enough for all shareholders and non-IT folk to understand.
A data scientist’s approach to data analysis varies depending on their business sector and the specific needs of the business or department they are working for. The business leader and department manager must clarify what they are looking for before a data scientist can find meaning inside the data. In order to achieve company or departmental goals, data scientists need enough domain expertise to turn data into information-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and so on.
General Duties of Data Scientists
- The gathering of large data sets and converting them into more helpful formats,
- Wrestling with different types of data, sources of data by using varied programming tools such as python, R, etc.,
- Mining the nuggets of information which is under the surface of big data, reveal the unseen trends, patterns and transform them to actionable information for company/organizations
- Using machine learning and deep learning techniques
- Contact and communicate with different shareholders regularly: IT departments, decision-makers, data source, and related executives.
3- Data Engineering
Data engineering is another data science job that you can apply for. A data engineer’s job is to identify patterns in data sets and develop algorithms to make raw data more useful for the enterprise. Several technical skills are needed for this IT role, including understanding SQL databases and comprehensive programming skills. As well as technical skills, data engineers must also be able to communicate across departments to understand what business leaders are trying to accomplish with the company’s large datasets.
Data engineers are often responsible for building algorithms to help give easier access to raw data, but to do this, they need to understand the company’s or client’s objectives. It’s important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases.
Data engineers also need to understand how to optimize data retrieval and develop dashboards, reports, and other visualizations for stakeholders. Depending on the organization, data engineers may also be responsible for communicating data trends. Larger organizations often have multiple data analysts or scientists to help understand data, while smaller companies might rely on a data engineer to work in both roles.
Although data engineers traditionally come from IT-based roles/education, IT bootcamps make it possible to change career paths for non-IT / non-technical professionals. It also helps build a convincing resume to land in data engineering or data science roles. Data engineers need high-level technical skills such as SQL, database design, python, Java.
4- Data Science Internship
Today, as mentioned above, data scientists and/or data analysts/data engineers are those who’ve changed their career path by attending a Bootcamp or certification program. So to eliminate their disadvantages against IT-origin professionals who have experience in the IT industry, it is very recommended to apply for data science Internship programs. By doing so, they will gain industry experience, enrich their resumes.
Do not forget to Study Data Science Interview Questions
Once you are ready to apply for a data science-related job, it is time to look at data science interviews questions. Data science interview questions can be the most challenging part of any job interview. During a data science interview, an interviewer will typically ask questions ranging from broad industry-related questions to more specific, industry-specific questions.
In addition, depending on the company’s data science department, questions might be given to deal with theoretical concepts. The questions chosen are to assess whether the candidate is well-versed in the theoretical concepts of data science and how they interact with real-life examples. Candidates that successfully pass the first round of interviews are often given the lead role in the data-science department.
Before the interviewee even gets to begin the data-science interview process, it’s important to get a few things in order. For starters, it’s crucial to find a data-science company with which candidates have a good rapport. This means it’s important to be aware of the typical responsibilities and job descriptions of the company and have an idea about the types of questions they will likely be asked. If candidates don’t feel at ease in the office environment of a data-science company, or if they are unsure how to perform in an interview based on their resume, their chances of getting hired in that company are slim to none.
Data scientists must be able to demonstrate that they can not only master the theory behind the data of science but can execute it in real-life examples. In addition, data scientists must show that they can solve problems presented to them. Candidates that come unprepared will be unsuccessful throughout the entire interview process. Therefore, data scientists must practice interview questions until they’re confident that they’ve adequately grasped all the essential information necessary for the position.
Data Science vs Data Analytics
It might be a good idea to take a quick look at Data Analytics while talking about data science and data science jobs. Data science deals mainly with studying large sets of unprocessed data; this is why it is so commonly used in scientific research, such as statistics, machine learning, and the internet. On the other hand, data analysis is an application that uses tools to analyze large sets of unprocessed data and draw conclusions based on the information they contain. So while there may be some similarities between data science and analytics, they are two completely different concepts, and there is no reason to think they should be the same.
Data science deals mainly with the use of mathematical techniques to solve problems and come up with solutions. On the other hand, data analytics is more concerned with how the business or company uses the data. Data science is heavily used in the field of computers. It deals largely with mathematical techniques and algorithmically taking pieces of code and translating them into actual solutions. While there may be some similarities between Data Science and Analytics, they are two entirely different concepts, and there is no reason to think they should be the same.
There are plenty of books and websites out there about the strengths and weaknesses of Data Science vs Data Analytics. However, if you have business problems that need to be solved using big data techniques, it would be wise to take a Data Science certification course before starting your research. Not only will it show you the basics of this technology, it will also put you on the right track to success. Data Science certification courses are widely available online; simply type in “data science certification” into any search engine, and you’ll find plenty of options to choose from.
Begin A Rewarding Career with IT Bootcamps
IT bootcamps are one of the fastest-growing fields in information technology, with more companies joining every year. Similarly, data science bootcamps have become increasingly popular among IT professionals because of their low cost and emphasis on applied data science and analytics.
IT professionals looking to get into IT boot camps can be sure that data science and analytics can be taught efficiently at IT bootcamps. But they will also need to take business development and leadership courses to prepare themselves for data science and data analytics jobs. Bootcamps for data science training typically teach the basic principles and technology behind Hadoop, Map-Reduce, and the new streaming frameworks like Spark.
Data analytics and business intelligence can be defined as the process of discovering business patterns from massive amounts of data. Common examples of this data include customer data, sales figures, social networks, and healthcare data, to name a few. These large volumes of data can allow an analyst to discover relationships and trends that would otherwise have been difficult to uncover without more in-depth research. Many large-scale companies employ data scientists and analysts to provide IT companies with this advanced capability, allowing them to leverage existing data and rapidly analyze it to discover business problems and opportunities.
IT bootcamps for data science can be taken by individuals already employed in the IT industry. Bootcamps are designed to give students hands-on experience using cutting-edge technologies, and many companies offer paid internships at various IT companies upon completion of the course. Some bootcamps even offer paid industry-exclusive courses on topics related to big data and analytics. Other courses teach students how to use databases, web applications, and visualization technologies to analyze and visualize data.
If you’re already in the IT industry or planning to enter the IT industry to shift your career, you can enroll in Clarusway’s data science course, which helps you to get data science or data analytics jobs.