As the world is now in a computer era and the use of artificial intelligence is in Vogue, the need for storing more data is then necessary but the need for professionals who can help to crack these data and bring out useful info from them is also paramount. As technology continues to develop at a rapid rate, the skills needed to work with that technology need to evolve faster with the continuous change or they will become obsolete.
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Data science is said to be a field of science that makes use of algorithms, various scientific tools, and processes to extract information from both structured and unstructured data. Often times, data science is compared with BI (Business Intelligence), although there are similarities between the two as they are both involved with data.
Business Intelligence (BI) deals with the analysis of previous data on business problems that is, it allows you to take data from both internal and external sources then prepare it and runs it in a way to create dashboards to answer business questions and problems. While, Data Science has a more forward approach as it focuses on analyzing the past and current data, predicting the outcomes with the sole aim of making information.
A data scientist is someone who basically deals with data. A data scientist job description includes the extracting and interpreting of data to get information. This is done by the use of both tools and methods from statistics and machine learning. They work with several disciplines related to mathematics, statistics, and computer science. Data scientists make use of the latest technologies in finding solutions and they crack complex data and present them in a more useful form instead of the raw data thereby, reaching conclusions that are important for an organization’s development.
Starting a career in data science encompasses a lot of skills and discipline. With the major set of disciplines been mathematics, statistics, and computer science. Aside from educational qualification, there also other skills required of a data scientist and some of them are outlined below:
Data science is a very broad field with varied career roles. As an aspiring data scientist, it is important to figure out what role suit you best and how you can function efficiently when employed. If you are a data scientist and looking for a career change, this is also for you as you can check out more options for a career scientist and make your choice. There are three main job titles in data science and they are; Data Analyst, Data Engineer, and Data Scientist.
This is like an entry-level position in the data science field. A data analyst’s job description is to search for a company or industry data and use it to answer business questions, and then communicate those answers to other teams in the company to be acted upon. Over time, data analysts often work with a variety of different teams within a company; you may work on marketing analytics one month, then help the CEO use data to find reasons the company has grown the next. You will typically be given business questions to answer rather than asked to find interesting trends on your own, as data scientists often are, and you will generally be tasked with mining insights from data rather than predicting future results with machine learning.
Skills required: Specifics vary from position to position, but in general, if you are looking for data analyst roles, you will want to be comfortable with:
Career prospects: Data analyst is a broad term that encompasses a wide variety of positions, so your career path is fairly open-ended. One common next step is to continue building your data science skills often with a focus on machine learning and work toward a role as a data scientist. Alternatively, if you are more interested in software development, data infrastructure, and helping build a complete data pipeline, you could work toward a position as a data engineer. Some data analysts also use their programming skills to transition into more general developer roles. If you stick with data analysis, many companies hire senior data analysts. At larger companies with data teams, you can also think about working toward management roles if you are interested in developing management skills.
Are in charge of building machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about. As a data scientist, you might be asked to assess how a change in marketing strategy could affect your company’s bottom line. This would entail a lot of data analysis work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make reliable future predictions based on past data.
Skills required: All of the skills required of a data analyst, plus:
Career prospects: If you are working as a data scientist, your next job title may well be a senior data scientist, a position that will earn you about $20,000 more per year on average. You might also choose to specialize further in machine learning as a machine learning engineer, which would also bring a pay raise. Or, you can look more toward management with roles like a lead data scientist. If you want to maximize earnings, your ultimate goal might be a C-suite role in data such as chief data officer although these roles require management skills and may not involve a lot of actual day-to-day work with data.
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They are also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
Skills required: The skills required for data engineer positions tend to be more focused on software development. Depending on the company you are looking at, they may also be quite dependent on familiarity with specific technologies that are already part of the company’s stock. But in general, a data engineer needs:
Career prospects: Data engineers can move into more senior engineering positions through continue, or use their skills to transition into a variety of other software development specialties. Outside of specialization, there is also the potential to move into management roles, either as the leader of an engineering or data team (or both, although only very large companies are likely to have a sizable data engineering team).
Below are the other seven different career roles in data science which we have explained in order to help you make your decision.
As the world continues to move globally, starting a career in data science is a good option. Data science is currently rated as the best of the 21st-century job. With more and more data being used every day, the need for data scientists keeps rising. If you then want to start a career in data science what are the necessary things to out in place. In this article, we have compiled some of the essential tips needed for you to kick start your career in data science.
Educational qualification: for you to become a data scientist, the minimum educational qualification required is a bachelor’s degree in a related field of computer science, pure sciences, and so on. Obtaining a master’s or PhD degree is an added advantage but not compulsory to start your career. Once these qualifications are met, the next step to becoming a data scientist is to start applying for an entry-level job in the data science field and gradually move up the career ladder.
Technical skills (SQL coding and Python coding): Python is one of the most popular languages used for coding in data science. Data scientists make use of python language coding more because it is versatile and you can find any of its datasets you need on Google. It is also possible to import SQL tables into your code. SQL (Structured Query Language) is also used by data scientists as it helps to carry out operations such as delete, add or extract from a database. It is also useful in carrying out analytical functions and help to transform database structures too. You need to be proficient in the use of both Python and SQL coding languages, there are also other coding languages which you will need, but these are the most common. Learning these languages and putting them to use would help you to understand relational databases and also boost your work profile as a data scientist.
Machine learning and AI: as a data scientist and you want your work to stand out, you have to be proficient in machine learning techniques such as; decision trees, logistic regression supervised machine learning, and so on. Learning these skills would enable you to be able to solve different data science problems that are based on organizational outcomes. As a data scientist works with a lot of data, you would need to be conversant with machine learning for you to excel in your field.
Communication skills: as companies and different organizations are in search of a data scientists to help them translate their data from its natural form into less complex terms for understanding, therefore as an aspiring data scientist, you need to possess good communication skills in order to interpret your findings in clear language. Most business owners are not interested in your analysis or how you crack the data, all they want is how to use that data in which you have analyzed and explained for business purposes.
Team player: as a data scientist, you have to be a team player, you can never work alone, therefore you have to learn to carry others along in whatever you do. Most of the time, you may have to work with people who do not understand what you are doing but the ability to be able to explain and carry them along is very important to your work. Make use of clear terms when dealing or working with other members of your organization, you have to show good communication skills and other interpersonal relationship skills to be able to perform your duties well. Above all, learn to communicate properly to the best understanding of your colleagues in the work place.
If after assessing your Educational qualifications and other necessary skills needed by a data scientist to perform well and you are set on starting your career in the data science field, below are 5 essential tips for you to follow in attaining success in your choose field.
In conclusion, the future definitely belongs to data scientists because more data are been used and we are in constant need of data scientists to help us interpret these big data in useable forms for our business and personal needs. Starting a career in data science does not require you to have a lot of qualifications with the minimum being a bachelor’s degree. Other skill sets needed by a data scientist are an innovative mindset, the ability to communicate and create answers for tough problems. In as much as people continue to make use of data, definitely taking up a data scientist career is a way of staying relevant in the workforce for years to come.
This post was last modified on August 1, 2022 6:59 pm