Choosing a career path is one of the most crucial decisions you will make in life. Depending on your interests and overall goals, you can pursue a career in just about any field, from medical to technology to business and beyond.
One of the most lucrative career paths you might want to explore is data science. Data science is one of the few careers that are well-suited to keep you relevant in the rapidly changing work environment. Thanks to technological advancements, many businesses have shifted operations to the internet. The digital migration of business operations provides fresh ground for data scientists to begin and grow their careers.
In the modern world, a lot of information is exchanged between various people, whether through business transactions or social interactions. A large amount of information available on the internet has generated demand for data scientists. Many corporations that are using online platforms to operate need data scientists who can analyze the available data to generate meaningful information. The information obtained from online interactions is then used to make critical decisions concerning business operations, such as marketing and production. Some companies specifically focus on the collection and analysis of data. They then sell the data to clients that might need it to improve their business.
Data scientists play an essential role not only in business corporations but in government institutions as well. To better understand data science and its importance as a career path, we will explore the following topics in this article.
- How data science works
- Industries that involve data science
- Types of data science jobs
- Benefits of a data science degree
How Data Science Works
Data science is an inter-disciplinary profession that deals with algorithms, systems, scientific methods, and processes aimed at extracting information from presented data. The essence of data science is to deduce meaning from all forms of data, whether structured or unstructured. Although they seem close, data science is very different from statistics. Statistics focus on predicting a certain phenomenon from given data. On the other hand, data science focuses on explaining the current phenomenon represented by the collected data.
If you are looking to pursue a career in data science, you must be asking yourself how it works. By studying data science, you become a data scientist. Industries have different roles for data scientists depending on the kind of data available and information systems used.
Here are some elements of data science that you might specialize in as a data scientist.
Data mining involves finding anomalies and patterns within large data sets to predict phenomena and explain the existing conditions. In data mining, you have to employ a wide range of techniques to identify patterns used to make essential decisions in organizations. Data mining plays an essential role in the daily running of business corporations in the modern world. Companies can create product promotion campaigns for specific audiences that could be potential clients and customers through data science. By mining online interaction data, companies can anticipate future changes in the product market and make necessary adjustments in preparation.
Machine learning is one of the most crucial elements of data science in commerce and one that is sure to become more important in the future. Machine learning involves the programming of computers and systems to predict a phenomenon based on previous operations. There are many uses for a machine learning algorithm, but one of the most common is generating personalized adverts by companies engaging in online trade. The machine algorithms created generate adverts for products you could be interested in by following your previous purchases or even things you previously searched for or browsed online.
Other than predicting future phenomena, machine learning can predict patterns and relationships in expansive data sets. Artificial intelligence algorithms can tell similarities in data sets once they are keyed into the computer systems.
Predictive and Prescriptive Analytics
Predictive and prescriptive data analytics is finding possibilities that certain phenomena will occur in the future and how you can benefit from them. Predictive casual analytics is used to tell how likely certain phenomena are to occur. With predictive analytics, you can build models that are easy to understand but give important information about the future.
Prescriptive analytics involves systems with artificial intelligence that can make their own decisions based on previous data. Such systems can adjust themselves depending on the available parameters. Predictive analytics systems not only predict occurrences but also suggest potential solutions. An example of prescriptive analytics is self-driving cars.
Data visualization is the visual representation of different types of data for easy understanding and identification of patterns. Data visualization could be in the form of charts, graphs, maps, and other forms. Visualization is among the top subjects of data science, and it’s how data scientists communicate their findings with other parties that might be important but don’t have a background in data science. Thanks to data visualization, one can tell trends in data changes over time and predict the possibility of certain phenomena. In commerce, data visualization explains changes in consumer behavior of a certain product.
Although studying data science sets you in a better place for employment, some factors determine whether you get employed or not. Most of these factors are related to the skillset that you might have acquired in school.
To be a desirable data scientist, here are some skills you should have:
- Computer Programing: To be considered a top data scientist, you must be proficient in at least one computer programming language. Data analysis requires writing and handling computer programs that could help identify patterns or predict outcomes from the data provided. Computer programming languages include python, java, Perl, and many others. The language you choose depends on the kind of data you need to analyze and the nature of output required. However, most data scientists prefer using python due to its versatility.
- Academic Prowess: Data scientists are subjected to a lot of critical thinking throughout their career life. For this reason, you must have an outstanding academics record to handle problems at your workplace. Being excellent in subjects like mathematics and statistics makes it easier for you to analyze data and give meaningful information and actionable insight. These skills are the main reason most employers have a minimum requirement of a bachelor’s degree in related courses. It is a guarantee that you can think critically and make rational decisions.
- Data Visualization: Analyzing data is the initial step of finding a solution to a certain problem in the industry you work in. However, you need to present the information you obtain from data to relevant people who can make decisions. Data visualization is an essential skill since it helps you pass critical information to people who might not have a background in data science.
- Communication Skills: After analyzing data and preparing necessary visual representations, communicating to relevant people is essential in making decisions. As a data scientist, you might have to present your findings before boards of directors in the company or field you work in. Such situations require you to be efficient in communicating for people to understand you. Additionally, good communication skills make it easier for you to network and learn from other data scientists.
Industries That Involve Data Science
Data science is a discipline that cuts across all industries. However, some industries rely extensively on information collection and analysis. Such industries use data science to a greater extent than others. Manufacturing is one of the sectors that extensively relies on data science. Here are some of how data science is used in the manufacturing sector:
- Real-time performance data and quality: Data obtained from machines and their operators can set performance indicators. These indicators are important in improving the efficiency of equipment and workers. As a result, manufacturers can minimize downtime in the manufacturing process and maintain high-quality production.
- Fault prediction and preventive maintenance: Manufacturing in the modern world revolves around computerized systems. Data collected for machine performance analysis is analyzed to predict when the equipment is likely to break down, allowing the manufacturer to carry out preventive maintenance or timely repairs in case of a breakdown.
- Demand forecasting: In the modern world, delivering customer orders on time is a key for business success. Manufacturers use data science to predict the possibility of increases and decreases in demand for certain products. When the demand is likely to rise, the manufacturer increases the rate of production. Manufacturers also use data science to monitor their inventory, preventing cases of over-ordering raw materials or over-producing certain products.
- Supply chain and price optimization: Supplying products to customers could be costly. Manufacturers incur huge costs in fuel and shipping charges, tariffs, among others. Through data science, manufacturers can use information relating to consumer purchasing patterns and locations to supply products at a lesser cost. This step is crucial in setting the price for produced items since the manufacturers have to compensate for distribution costs.
- Robotics, automation, and smart factory design: Engineers and investors rely on information technology to determine how manufacturing can be mechanized. Data collected from the manufacturing process determines the rate of production and quality needed. Additionally, data science is essential in telling what return on investment should be expected after mechanization.
Due to technological advancements, people’s lifestyles are constantly changing. Unlike years ago, communication and travel have greatly improved. To match the trends, industries have been consistently upgrading their operations to incorporate modern technology.
Here are some industries that use data science in the most unusual and innovative ways.
The Manufacturing Industry
Manufacturing is one of the industries that rely on human behavior and data collection in almost all aspects. To speed up production to meet growing demands, manufacturers need data science to optimize and mechanize the production plants. Collected data on consumer behaviors predicts changes in demand and scheduling manufacturing operations. Additionally, data science tracks inventory by use of techniques such as barcodes.
Since the introduction of the internet and the mechanization of industries, commerce has seen a wide range of changes. Commerce involves buying and selling items, usually in large quantities within a region or across continents. Most suppliers have shifted operations to online platforms to reach a wider market. In commerce, data collected from purchases made in a certain region is analyzed and used to predict future demands. Information from such data is used to create adverts for specific regions based on what buyers in the region order the most. In modern marketing, your social interactions on social media platforms are used to generate personalized adverts. An example of commerce industry players using data science is Amazon. Amazon analyzes your previous purchases and likes on their website then suggests items that you might be interested in.
The Entertainment Industry
Entertainment is one of the most diverse industries today. It covers smaller industries such as the film industry, music production, among others. One of the biggest benefits of the internet is that it has made entertainment accessible to many people.
Entertainment platforms such as YouTube, Netflix, among others, use data science extensively. While streaming movies and music from online platforms, your data is collected by algorithms. The data collected is analyzed and used to suggest new music, movies, and any other form of entertainment material for you.
Additionally, data, such as the number of people viewing certain content on such platforms, is used to pay the content creators. Most entertainment platforms such as TV stations make money from advertising. Therefore, they collect data about viewers of their programs to determine the kind of advertisements should show during the programs. The data could be age, region, gender, among others.
The Education Industry
Education has been revolutionized by the introduction of the internet and technological advancements. Today, education programs have shifted to online platforms for easy access by students from different parts of the world. Data science plays an important role in monitoring the performance of tutors and students. Academic institutions also use the data about the number of students enrolling in certain programs to determine the number of facilities to install. For efficient running of the institutions, administrations conduct surveys among students. Data scientists analyze the data collected from students to determine the exact situation and areas that need improvement.
Types of Data Science Jobs
Data scientists are key contributors to business operations in various industries. There are many job opportunities that you can get absorbed into as a data scientist. While some of these jobs are specific for certain industries, some cut across more than one industry. Here are some unique jobs you might land as a data scientist:
- Machine learning engineer: Machine learning engineers create data funnels and algorithms that analyze data and provide relevant information. This job requires expertise in statistics and computer programming. The qualifications are essential in designing machine learning programs to perform functions based on previous data records. Machine learning is responsible for running tests and experiments to monitor the efficiency and suitability of information systems. An average machine learning engineer earns up to $114,000 as salaries per year.
- Data architects: Data architects mainly deal with the preparation of raw data before the analysis process. As a data architect, your role is to modify existing databases and create new ones that are easy to analyze. Data architects are essential since they provide data to be used by machine learning algorithms to perform different functions. Other jobs related to data architects are data engineer and infrastructure architect. Data engineers process the available data. A data engineer is also responsible for the creation of data pipelines. A data pipeline refers to a robust network in an organizational ecosystem through which data scientists access information. Infrastructure architects oversee the data and information systems in organizations for efficient data flow. In most cases, they are required to develop systems through which data can be collected, stored, and analyzed.
- Statistician: Statisticians collect data, analyze, and interpret it to identify trends and potential relationships between the data sets. The trends and relationships presented by statisticians help make decisions in different organizations. As a statistician, your main roles will be designing data collection systems and communicating your findings to stakeholders. Statisticians also advise on steps to be taken regarding the information obtained from the collected data.
- Data analyst: Data analysts transform and manipulate huge sets of collected data to give meaningful information. The transformed and manipulated data is then taken for specialized analysis to deduce any hidden patterns and relationships. In many companies, the role of a data analyst includes tracking web analytics. Additionally, a data analyst is required to give meaningful insights to the people responsible for decision-making. The decisions made concerning organizational operations are determined by the data presented by a data analyst.
- Business intelligence developer: Business intelligence, BI, developers help business people identify data sets that are essential for business operations. They develop strategies for businesses to collect data that can be used to make critical decisions. Business intelligence developers play a pivotal role between data science and commerce. If you become a business intelligence developer, you will be tasked with developing programs or using existing ones to help business owners collect and understand data.
Being employed as a data scientist could be rewarding. However, the benefits you receive depend on your job description at your workplace. Before signing a contract to work for an employer, you need to ensure the roles you are given match a data scientist’s job description to protect yourself from risks that might obstruct your career growth in data science. Here are some of the elements that make up a typical data scientist job description:
- Identifying valuable data sources and devising collection techniques through creating programs and algorithms that facilitate the collection of data
- Processing both structured and unstructured data in readiness for analysis
- Analyzing huge data sets to establish trends and relationships between variables and how they affect business operations
- Building and combining models and machine learning algorithms.
- Present visualized data to relevant parties for the decision-making process
- Propose solutions to problems identified from the data trends and relationships to relevant parties for decision making
As a data scientist, you can work in any industry since most of the concepts are statistical and related. However, you can choose to work as a freelance analyst since the skill acquired guarantee freedom of movement between different industries. Freelance data scientists can work remotely through platforms that permit online coding. allowing the client to monitor and evaluate the coding process to ensure it’s performing certain functions according to the provided data. Commerce is the industry that hires most freelance data scientists because most commerce companies greatly rely on data science for business success.
Benefits of a Data Science Degree
To be a certified data scientist, there are certain academic requirements you should meet. One of them is having a minimum of a bachelor’s degree. Advancing your studies to higher levels improves your expertise and chances of rising through ranks in your career. Proficient data scientists around the world today have at least a master’s degree in data science, statistics, or any other related field. If you wish to major in business analytics, you should have extra academic qualifications in commerce or any other business-related field.
Here are some benefits you will get by attaining a master’s degree in data science:
- Competitive advantage: Having a master’s degree in data science places you in a better place for employment than other people in the job market. With a master’s in data science, employers look at you as an expert in whatever field is your specialty. Employers want data scientists who can contribute to critical decision-making processes, which is a trait of most people with a master’s degree in data science.
- Increases your chance of being promoted: Climbing through ranks at the workplace is not an easy task. It could take many years of dedicated service before the organization you work in recognizes you for promotion. Attaining a master’s degree in data science indicates that you can influence a lot of policies and processes at your workplace. Therefore, you are likely to be promoted to supervisory and managerial positions.
- Increased income: Promotions at workplaces mean you earn higher than other people. Therefore, one of the returns for having a master’s degree is higher salaries that come with higher positions.
- Increased credibility: By having a master’s degree in data science, you gain the respect of your peers and other stakeholders in the industry you work in. A master’s degree implies that you can engage with people at higher intellectual levels and make meaningful conversations and connections.
With a degree in data science, you can start a career in almost any industry. A BI analyst can work in all environments, including government and non-governmental organizations. The manufacturing sector has constantly increasing career opportunities for data science experts as manufacturers seek to mechanize production processes.
The salary you receive as a data scientist depends on several factors. In most cases, your salary will be determined by the organization you work in. Working in government organizations pays a little lower than in private firms. However, job security is guaranteed for data scientists working in government organizations. Your job description and position at your workplace also determine the salaries you receive. Administrative and managerial positions attract higher salaries. To earn more from data science, you can break from employment to form your own data science company.
Data science is a career that sets you up for independence. However, the freedom you get depends on personal efforts. Once you have mastered data science skills, you can easily leave employment to work as a freelance data scientist. Freedom from employment allows you to focus on other aspects, such as family, that make life meaningful. Networking skills will set you apart from most job seekers in the job market.
Networking helps you establish connections with potential employers and partners, allowing you to share and explore ideas and opportunities within the data science field. While at school, you need to focus on important aspects that contribute to academic excellence. One of the main qualifications employers look at is how good you are at academics. Your success helps them gauge how effective your contributions could be towards solving business problems.
To ensure you are retained in the job market, you need to update your skills as technology changes. Modern equipment requires new computer programing and machine learning model techniques to make use of collected data. Additionally, trends in data keep changing as people’s lifestyles change. For instance, data science has been revolutionized with the increased use of social media and other internet platforms for business. Data scientists require advanced technical and analytics skills to collect, process, and analyze data from these platforms to make sound business decisions.
Before enrolling in a teaching institution for a data science degree, you need to consider several factors. These include your abilities to handle the pressures that come with the course and the general cost of studying. Having these considerations in mind makes your study life and future in the job market bearable.