If you’re a student and want to learn data science, one of the highly in-demand skills in today’s career environments, this article is for you.
Also, if you’re through with your academic pursuits but wish to add data science skills to your skill set toolkit, you’ll find this write-up helpful.
One thing is indisputable in today’s social discourse.
The world is constantly evolving.
Businesses are going global, and there have been considerable breakthroughs in technology. Things that organizations and individuals hitherto considered impossible, they now achieve effortlessly.
Many organizations now rely on information from data experts to analyze situations and make informed business decisions.
All these have made the need to learn data science relevant more than ever before.
What is Data Science?
Data science is a field that deals with large volumes of data by exploring, analyzing, and drawing out meaningful conclusions using scientific methods and processes.
As we have pointed out earlier, data science is an essential part of many businesses today, given the enormous data that finds its way into the business space.
In most business organizations, the business manager, IT manager, and data science manager manage most data-related issues. However, the data science manager seems to be almost indispensable.
Who Should Learn Data Science?
I want you to erase this thought from your mind. Your intention to learn data science should not always be so you can become a data scientist. If you’re passionate about working with data, you can set it as a goal to learn the basics of data science.
With this, you can deal with elementary data issues in your business without recourse to any expert.
However, if you intend to become a data scientist, nothing stops you from learning all it takes to get the relevant certifications that will qualify you as a data scientist.
My point here is this.
With the right effort, passion, and willingness to learn, anybody can learn data science.
And in case you’re not sure if data science is what you can learn. I will give you this clue with the belief it will help you make your decision.
The clue is:
If you’re a student and enjoy mathematics, statistics, or economics, you’re likely to do well as a data scientist. Those who like playing around with numbers may also find data science interesting.
Read Also: Top 10 Digital Skills for Students
What Does a Data Scientist Do?
By and large, a data scientist extracts meaning from data and interprets the same, which requires tools and methods from statistics and machine learning, as well as being human.
They spend a lot of time collecting, cleaning, and munging data. Because data is never clean, the data scientist needs patience, information, and software engineering skills in data collection. These skills help them understand salient biases in the data and for debugging logging output from codes.
Simply put, a data scientist analyzes data for actionable insights. Specifically, the data scientist:
- identifies the data-analytics problems that offer the most significant opportunities to the organization
- determines the correct data sets and variables
- collects large sets of structured and unstructured data from disparate sources
- cleans and validates the data to ensure accuracy, completeness, and uniformity
- devises and applies models and algorithms to mine the stores of big data
- analyzes the data to identify patterns and trends
- interprets the data to discover solutions and opportunities
- communicates findings to stakeholders using visualization and other means.
Skills You Need to Learn Data Science.
These skill sets help you to be the best you can be as a data scientist.
1. Cloud computing
Cloud computing is the process of storing data in the cloud, which is a group of internet storage resources. Companies use cloud computing because cloud storage is typically inexpensive and secure compared to other data storage options.
2. Statistics and probability
With skills in statistics and probability, data scientists can predict trends and develop forecasts, discover anomalies in the data set, establish a relationship between two points in the data, and interpolate missing data points.
3. Advanced mathematics
When working with advanced mathematics, data scientists can use tools to help them perform calculations, so it becomes more crucial that they know the principles of calculus and algebra and how they can affect their reports.
4. Machine learning
Machine learning is the artificial intelligence that data scientists use to find patterns in data and perform complex calculations. Many data scientists learn how to use machine learning through advanced graduate courses or certification programs.
5. Data visualization tools
Data scientists use data visualization tools to translate information and data into visuals like relationship maps, 3D plots, bar charts, histograms, line plots, and pie charts. With data visualization tools, it is easier for data scientists to see any trends and patterns and outline points of data in a set.
6. Query languages
A query language is a computer language that data scientists use to ask about databases and the information they hold. The most common query language is a structured query language (SQL). Using this language allows data scientists to quickly retrieve data and use it to form solutions for some known issues or answer specific questions.
7. Database management
Knowing basic database management concepts can help a data scientist retrieve information from company databases more quickly.
8. Python coding
Python is an open-source programming language that data scientists use to manipulate data to understand it more. It syncs well with machine learning and other artificial intelligence tools to provide data in a digestible format that even a starter can use.
9. Microsoft Excel
While there are more complex ways to form data listings, Microsoft Excel is a basic program that data scientists can use. Microsoft Excel helps data scientists create a database with custom labels, sort and filter data, and form tables with calculation functions. They can also use Excel to export data to many other programs.
10. R programming
R is an open-source programming language with many statistical applications, making it particularly valuable in the work of a data scientist. Like other programming languages, R is a feature in computer science degree programs, but a data scientist can also learn to use this language by taking a certification course.
11. Data wrangling
Another skill many data scientists have is data wrangling, which involves cleaning raw data, removing outliers, changing null values, and turning the data into a format they can use in different programs. Knowing how to wrangle data allows a data scientist to use information from various sources, even if the data points have other formats.
One of the essential soft skills for data scientists is the ability to work with minimal supervision. Many of the tasks of the data scientist involve a lot of individual work. Data scientists who consistently meet their deadlines independently might find it easier to advance in their careers, as independence is an indispensable leadership trait in many industries.
While data scientists often complete their data collection and analysis tasks independently, they also interact with colleagues and people. The information they collect and analyze often helps other departments run more efficiently, so the data scientist typically shares their findings with employees in the departments they support.
For example, a data scientist for a fulfillment company might analyze data about order trends to predict spikes in online business. After completing their analysis, they might meet with the operations team to explain what the data suggests about future ordering trends.
14. Project management
Data scientists might use their project management skills to lead strategic projects that involve data management. Project management skills ensure that project teams achieve their goals, meet deadlines and stay within their budgets. Depending on their background, data scientists might develop these skills through work experience or by taking a course in project management.
15. Analytical thinking
The need for the data scientist to have analytical thinking goes beyond evaluating data. They might use their ability to analyze critically to create data management systems and choose appropriate software products for their needs. Analytical thinking can also allow a data scientist to solve problems in the data collection and analysis processes while allowing them to collect more pertinent data and identify trends accurately.
Why You Should Learn Data Science
Now that you understand what it is, here are some great reasons why data science might be the right choice for you to study at college. According to Tech Jury, 95% of businesses cite the need to manage unstructured data as a problem for their business. Also, Glassdoor, a job recruitment agency, observed The average base salary for Data Scientists in the United States is $117,212 per year. All these statistics point to the fact that there’s a need for data scientists globally.
So, specifically, you can learn data science for the following reasons:
a. Earning Potential
Learning data science can lead to a high-income career. A July 2019 data from Higher Education and Outcomes Report reveals that ICT graduates receive the highest weekly earnings five years after graduating compared to other sectors (based on the analysis of the destinations of students who graduated between 2010 and 2016).
However, data science is one of the top-paid technology jobs above other IT and management roles, with an average base pay of over €50K in Ireland, according to the job search site Glassdoor. So, if you’re willing to put the work in, it will be worth it.
b. Employment Opportunities
If you have a BSc or its equivalent in Data Science, you will have the opportunity to complete a six-month work placement before completion of the course. This work placement allows students to gain valuable experience while still studying, ensuring they can hit the ground running when they graduate.
NCI computing graduates are in high demand after they complete their work experience in established companies such as Microsoft, Intel, Citi, SAP, Irish Life, Vodafone, and Workday.
c. Free Fees Initiative
The National College of Ireland BSc (Honours) in Data Science and Higher Certificate in Data Science courses qualify students under the Free Fees Initiative and the Student Grant Scheme (SUSI).
These initiatives allow students only pay the contribution fee, which covers administration costs along with the gym charge.
Nonetheless, students eligible for the SUSI grant do not pay the student contribution fee. These initiatives ensure that pursuing third-level education is a viable pathway for students who may not consider college an option due to financial concerns.
d.Learn Cutting-Edge Technologies
Data science graduates develop technical expertise providing them with the skills to guarantee they will be ready to jump straight into the workplace when they leave college. Students will be encouraged to build an understanding of new technologies such as data modeling, machine learning, and artificial intelligence as they progress through the course.
e. Global Demand for Data Scientists
There is an acute shortage of qualified data scientists worldwide. A recent report from a job site, Indeed, revealed a 29% increase in the demand for applicants for roles in data science in recent years. Again, there has been a staggering 344% increase since 2013, with searches by job seekers growing at 14%, suggesting a gap between supply and demand.
f. Work Across Various Sectors
One of the best things about being involved in this area is that data scientists can work across industries and sectors. Some of the top industries for data science professionals include healthcare and pharmaceutical, financial services, manufacturing, logistics, telecommunications, and the automotive industry.
As a data scientist, you can impact your role by examining the performance of an area of a business, such as sales and marketing, operations, and customer services, and driving positive changes based on evidence.
g. Future-Oriented Role
Data is the driving force behind industries in the 21st Century. Anyone planning to develop their knowledge of the data science field is placing themselves in a strong position for a successful future career.
Forward-looking enterprises who understand that data fuels the future are hiring data scientists now. Data science is the career for tomorrow and promises to deliver a future-proofed job for anyone who equips themselves with knowledge of technologies such as machine learning and artificial intelligence, which will help them become valuable assets to their employer.
h.Contribute to Society
With your knowledge of data science, you can help to make the world a better place. Several good news has come from professionals who creatively and intelligently work with data. These stories are mind-blowing.
The most current instance is a group of data scientists who used a mathematical model to create a more effective contact tracing process during the COVID-19 pandemic.
j. Best of Both Worlds
If you think you have a flair for business, a desire to develop your technical skills, and a passion for interpreting big data, then studying data science is the right choice.
What are the Job Opportunities for those who learn Data Science, and What are Their Likely Earnings?
As a data scientist, you can earn up to $139,840 a year. Your job requirements will include analyzing large amounts of complex raw and processing information to find patterns that will develop organizations and help steer vital business decisions.
Machine Learning Engineer
This specialty requires you to create data funnels and work on software solutions. It often requires robust statistics and programming skills and software engineering knowledge. And as a machine learning engineer, you can earn up to $114,826 per annum.
Machine Learning Scientist(Research Scientist or Engineer)
People who work as machine learning scientists(Research Scientists or engineers) research new data approaches and algorithms that apply in an adaptive system. In doing this, they can earn take home $114,121 annually.
This data science specialty can earn a minimum of $113,757 a year.
Application architects concentrate on designing the architecture of applications and building components. An example is the user interface and infrastructure. They monitor the behavior of applications used within a business and their interaction with one another and the users.
The enterprise architect demands that the expert aligns the company’s ideas with the right technology to help actualize these goals and ideas optimally. The average take-home pay is $110,663.
They create new database systems, develop ways of improving the performance and functionality of the systems, provide access to database administrators and analysts, and can make around $108,278 yearly.
Infrastructure Architect(Cloud Infrastructure Architect)
Infrastructure architect ensures optimum functionality of all business systems and supports the development of new technologies and system requirements. This function can fetch as much as $107,309 per annum.
A data engineer earns up to $102,864 yearly. His job requirements include building and maintaining data pipelines that make information accessible for data scientists.
Business Intelligence(BI) developer
With an average yearly salary of $81,514, a business intelligence developer designs and develops methods that enable business users to find the necessary information to make better business decisions.
Statisticians work to collect, analyze and interpret data to enable them to identify trends and relationships that organizations use to make more informed organizational decisions. The average pay of a statistician is $76,884
Data analysts transform and manipulate large data sets to suit the desired analysis of companies. They also aid in the decision-making process. The likely take-home pay of a data analyst is $62, 453
How to Start Your Journey of Becoming a Data Scientist
Becoming a data scientist generally requires some formal training. Here are some steps to consider.
Earn a data science degree.
Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.
Sharpen relevant skills.
If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant Bootcamp. Here are some of the skills you’ll want to have under your belt.
Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include Python, R, SQL, and SAS.
Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work: Tableau, PowerBI, and Excel.
Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.
Big data: Some employers may want to see that you are familiar with big data and how to use them. Some software frameworks used to process big data include Hadoop and Apache Spark.
Communication: The most brilliant data scientists won’t be able to affect any change if they cannot communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill for data scientists.
Get an entry-level data analytics job.
Though there are many paths to becoming a data scientist, starting a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, you can work to become a scientist as you expand your knowledge and skills.
Prepare for data science interviews.
With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions.
Data scientist positions can be highly technical so you may encounter technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Preparing examples from your past work or academic experiences can help you appear confident and knowledgeable to interviewers.
Becoming a data scientist is not a walk in the park, but it’s achievable. All you need to succeed is dedication and willingness to learn.
Available facts show more and more companies are hiring data science practitioners.
Guess you already know the reason.
Data science is a proven disciple for business growth.
It extracts meaningful insights from data. Those who wish to work with data require domain expertise, programming skills, and a knowledge of mathematics and statistics.
You, too, can queue into this life-changing career opportunity by learning the ropes. With dedication and hard work, you can gain the requisite knowledge and certification that will qualify you to become a data scientist.
Is this what you wish to do?
Give it a try!