Data science is among many individuals' most attractive and desired careers. One, it has endless opportunities in the job markets, ranging from technology, and production, to logistics, not to mention the high salaries pocketed by data scientists. The demand for data science professionals is high, and the gap between demand and supply is wide.
- Data science is at the forefront of technology, science, the economy, and many other sectors.
- Despite its lucrative nature, very few individuals succeed in data science, which widens the gap between demand and supply.
- Data Science programs are hard, and you'll need hard work and more commitment to get decent skills, as mechanisms can change with time.
A career in data science is becoming more popular as time goes by. Projections show the field will experience more growth than any other sector in ten years. With better institutionalization, it has redefined its scope and improved the technology it is based on. But is data science a good career for you? Let’s find out.
Is Data Science a Good Career Choice? - Reasons Why
There are several reasons why data science is regarded as a good career. These are some of the reasons:
Job Market Diversity
Unlike other careers, data science opens doors to countless opportunities in different fields, not only technology, meaning there are more data science jobs. Data science skills are essential in web retailing, marketing, planning, security, information technology, and healthcare. Nearly all professions require data science skills.
Data analysts pocket huge salaries that make the profession lucrative. According to labor statistics, on average, a data scientist’s salary ranges from $95,000 to$250,000 depending on education and experience. Other determinant factors are the size and profile of the company and the region. The significant roles they play are worth the high salaries. Also, the huge gap between demand and supply benefits even the inexperienced data scientists, who earn considerable salaries.
Data Science Courses Worth Studying
This course is a comprehensive guide to the use of Python in data analysis, visualization, and mastering machine learning algorithms. The course is well-suited for aspiring data scientists as well as experienced data scientists. However, beginners should have some background programming knowledge.
The program integrates 100 video lectures and code notebooks for learning. Among the skills you'll acquire are programming with Python, and data visualization, among others. Some of the topics the course covers are Programming with Python, Use matplotlib and seaborn for data visualizations, NumPy with Python, Pandas Data Frames applications in solving complex tasks, Linear Regression, and Neural Nets & Deep Learning.
This program integrates five courses that introduce learners to data science. It applies python programming language to teach students about data science. Among the essential python toolkits are networkx, scikit-learn, pandas, nltk, and matplotlib. However, you need background knowledge in Python for a smooth learning process.
Enrolling in his course will see you go through five essential courses, including Data Science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
At the end of the course, there is a certificate of completion. However, you'll need to complete all five courses to get the certificate.
This course aims to equip you with all skills relevant to help you become a data scientist in about six weeks.
In general, you'll learn Statistics essential for efficient interpretation and analysis of big data, visualizations using popular data tools, and application of Machine learning algorithms with Python in solving real-world challenges. You'll also learn to appreciate Cloud in data science, use it, and apply tools like SQL, Python, and Tableau to solve problems.
The course costs $13.99 only. The package gives you access to all learning resources for an unlimited period. Also, you can access the materials on your mobile phone or the TV.
This Specialization course is an ideal program for individuals with interest as it has all the fundamental skills one needs in this demanding career path. The program has five courses with well-organized content for effective learning. It also integrates prerequisites, including libraries, SQL, statistical analysis, relational databases, Python, and open-source tools.
The program uses hands-on projects to facilitate practical learning. Learners can interact with real data science tools and datasets through these projects. The efficiency, effectiveness and live interaction with real data science tools prepare learners to face real-world problems. Thus, you'll have the practical knowledge to apply to data science projects after completing the course.
This program is the ideal course if your dream job is a data engineer or a machine learning engineer. The course is taught by industry experts. An experienced data scientist will take you through the course, thus guaranteeing you quality training. Some have or are working for large companies, including IBM, Facebook, and many more. It is also known that many Complete Machine Learning & Data Science Bootcamp 2022 graduates are now working for leading tech companies, including Apple, Google, Tesla, and many more.
The program is project-based and involves learners in hands-on projects throughout the learning period. The course is updated regularly to ensure learners are up-to-date with the latest trends in data science.
Data Science Specialization helps learners generate relevant questions, manipulate data sets, and visualize data efficiently for a desirable result. The program integrates all the tools and concepts you'll need to become highly technical in your new career.
The program has ten essential topics: The Data Scientist's Toolbox, R Programming, Data Cleaning, Exploratory Data Analytics, Reproducible Research, and Statistical Inference. You'll also learn Regression Models, Practical Machine Learning, Developing Data Products, and Data Science Capstone.
The course also has hands-on projects, which you need to complete to earn the certificate of completion. The final Capstone Project requires learners to apply the acquired skills to create a data product using real-world data.
How to Become a Data Scientist
Data science entails several disciplines, computer science, engineering, math, etc. But no matter the discipline, you should follow the steps below to become a data scientist.
- Enroll for a data science degree.
Academic credentials in related fields assure prospective employers of your ability to handle data science tasks. It is not a must-have requirement, but some employers find it necessary. Therefore, you should consider a bachelor's degree in computer science, statistics, or data science. A master's in data science will be an added advantage.
Fortunately, the number of universities and related learning institutions offering data science courses has increased. Besides, online platforms have made bootcamps and online courses available at affordable rates.
- Improve your skills.
Being a data scientist needs you to be up-to-date with skills and the changing trends in technology. Therefore, you should keep on updating your skills. You can do so through the various bootcamps and online programs available for free or affordable rates.
You may need to improve the following skills.
- Machine learning. Machine learning skills will help you improve the data quality, hence accurate forecasting of future databases.
- Programming languages. You should update your skills with Python, SQL, R, and SAS programming languages.
- Data visualization. Update yourself with PowerBI, Tableau, and Excel to create graphs and charts for data analysis.
- Communication. Sharpen your verbal and written communication skills to communicate your data, findings, and thoughts effectively.
- Big data. You must familiarize yourself with software such as Apache Spark and Hadoop to process data.
3. Start with an entry-level job.
A Data science career is a long path, and you should start from below. An entry-level job as a data engineer, data analyst, or statistician should give you the platform to learn more about data analysis and help[ you build on your data science profession.
4. Be ready for interviews.
After working as an entry-level data analyst, you should be ready for a senior data science role. At this point, you should be ready for a data science interview. Data science is highly technical, so you should expect more technical questions.
Tips to Succeed in Data Science Career
Consider the following tips to succeed in a career in data science.
- Consider formal education
- Prioritize practical work
- Keep focus and persist
- Try numerous visualization techniques
- Develop self-confidence in solving tasks.
- Prepare yourself mentally
Is Data Science Really in Demand?
Yes, a data science career is in high demand. Many business models increasingly appreciate AI in running and managing business operations. Therefore, the demand for more data scientists will likely increase even further.
Is Data Science a Hard Career?
Data science is a complex field, making it a hard career. To complete a data science course, one must master a broad set of skills and knowledge. You'll need to be good in statistics, computer science, and math.
Why Is Data Science Stimulating?
Data science is stimulating because it has a wide range of opportunities for career choice. Its technical skills are applicable in almost every field, not only the data science field.
What Do I Need to Become a Data Scientist?
With the list of courses above, you can kick off your data science career. However, ensure to dig for more information and skills in the field to get a fighting chance.