The data science world has become one of the most competitive spaces around the globe. Unlike other fields, data scientists have to adapt at all times. Nurturing your data science education is an ongoing process.
Technologies are evolving at a rapid pace. You must keep yourself up to date and continuously develop your skillset, or else your skills may become obsolete. You can find several specialized online courses that can build up your proficiency in specific tech niches. These credentials can portray a better image of yourself in the eyes of an employer.
You must continue to refine your data-related skills over time. Non-statistics graduates must establish their statistical literacy. You should understand concepts like statistical inference, probability, regression, Bayesian modeling, and other significant topics in statistics.
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Next, you must build your programming skillset and become capable enough to reach an intermediate proficiency. While theoretical education is necessary, you cannot just read books or solve problems on paper alone. You must have an applied experience to create fully-working applications and programs on your PC. Hands-on experience accelerates your learning and clears many of your doubts in a short time.
Make sure that you don’t only focus on statistical and programming details. As a student of data science, you must always strive to accelerate your analytical, mathematical, and scientific skills. Your logic-building skills are going to be integral in those scenarios where you can’t find a working solution on the Web.
The “Most Wanted” List of Data Science Skills
As an aspiring data scientist, the following skills can set you on the right path.
You should not go into advanced data science until you know databases in detail. You can develop basic and advanced desktop and web applications and work on your database designs. Try to increase the efficiency of your database architecture by implementing concepts like normalization. Similarly, make sure that your queries are optimized. When you finally carry a significant working experience in databases, you can touch other skills in this list.
2. Programming Languages
Master programming languages like Java, Python, and SQL. You can test your grip on a language by practicing on sites like LeetCode and HackerRank and solve programming challenges. After getting a good grasp of the fundamentals, move towards creating programs and software. For instance, you can use Python to build a recommendation system for an e-commerce site where customers can receive product suggestions based on their previous shopping industry.
3. Database Management Tools
You need an advanced knowledge of programming and database management tools like SAS, Tableau, Spark, and Hadoop. When you become familiar with one tool, it is easy to adopt others. For instance, if you can make data visualizations in Tableau to understand a trend in the finance industry, it will not take much time to do the same thing with Spark.
4. Data Analytics and Visualizations
Next, you have to gain expertise in data analytics. Analytics is more like a mindset. You have to dive deep into correlation and aggregate analysis. Data visualization is another useful skill that can help you convert raw data into meaningful data.
5. Operating Systems
This is perhaps one of the most underrated skills in data science. Sometimes, data scientists have to work with the hardware where OS skills can come in handy. OS knowledge can show you a lot of valuable insights that happen on the lower level. By knowing your way around the OS layer, you can address many issues that other data scientists can’t handle due to their limitations on the application layer.
6. Fundamentals of AI and Machine Learning Processes
You don’t have to explore AI and machine learning to advanced levels. It is recommended that you get a hang around the fundamentals of unsupervised and supervised learning models like logic regression, linear regression, K-NN, pipeline, dimensionality reduction, and clustering.
Unlike some other fields, data science requires an extremely strong educational background. The following observations have been revealed about the qualifications of working data scientists.
- 88% of them hold a Master’s degree.
- 46% of them hold a Ph.D.’s degree.
While the minimum qualification in job descriptions of data science positions is a bachelor’s degree, employers mostly hire a candidate with a master’s degree.
Types of Careers
A Master’s in Data Science can open doors to the following opportunities:
Data AnalystData analysts review company/industry data and generate a useful response to improve productivity.
Data ScientistThey are more into machine learning algorithms and models. They are tasked to compute accurate predictions.
Data EngineerThey manage the data infrastructure of an organization.
Machine LearningThis Machine Learning role is often overlapped with a data scientist and a data engineer. The exact role varies according to the organization. Some companies use a machine learning engineer to engage more in software engineering and further the output of their data scientists.
Quantitative AnalystThey are more focused on statistical analyses for making predictions. This job role often revolves around risk and finance.
Data Warehouse ArchitectThey use SQL and database knowledge to work on data storage systems.
Business Intelligence AnalystThey work on data analysis tools to analyze trends.
Which Data Science Program Should You Choose?
If you have developed an interest in data science, consider enrolling in the following programs in data science:
A bachelor’s program facilitates students to get foundational training, so they can study mathematical and statistical analysis. Other than statistics, they will learn algorithms, information visualization, data structures, and other CS disciplines.
With a master’s degree, students gain strong programming, computational, mathematical, and statistical skills. At the end of this program, they can either enter the industry in any data science role or study further with a Ph.D. program.
A Ph.D. degree focuses on highly-advanced research and the study of computational science. Students go through advanced topics of high-performance computing and data mining.
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Shaping Your Career Path and Development
Today, organizations are actively hunting for specialized talent in data science. These professionals can help them to innovate without falling behind in these industries: finance, e-commerce, consultancy, healthcare, tech companies, industrial and manufacturing, energy, automotive, academia and education, government, and R&D.
Your career path will be shaped by your interests, skills, and experiences. By getting into data science, you can always build up your analytics skills and resolve real-world problems. In this way, your career can have an organic, natural growth.
Nowadays, employers are looking for data scientists with skills like logical thinking, analytics, mathematics, critical thinking, neural networks, project management, deep learning, AI, data engineering, machine learning, natural language processing, software engineering, and creative problem-solving. One unique skill that particularly stands out is the ability to work on unstructured data. Since companies obtain raw data from a wide range of sources, a proven experience of dealing with unstructured data is hard-to-ignore.
For soft skills, organizations look for job candidates who:
- Have sharp business acumen to understand and work with complex scenarios.
- Posses hunger to drive innovation.
- Extract easy-to-understand insights from complex datasets, so non-technical stakeholders can take suitable actions and decisions.
- Have the ability to convince others with captivating storytelling.
- Communicate and collaborate well with others.
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There is a reason why data scientists are continuously ranked as one of the hottest professionals in the future; companies can’t innovate and achieve digital transformation without them.
If you have decided to become a data scientist, consider the Data Analyst Course by Simplilearn. It includes both fundamental and advanced analytics concepts with tools like Tableau, PowerBI, Excel, R, Python, and a special focus on data visualization.
On the other hand, the Data Engineering course can introduce you to data engineering with PySpark, MongoDB Developer, Spark Developer, Hadoop, Apache Cassandra, and Scala. Regardless of which program you will enroll in, expect to have a lucrative job offer in hand soon at the end of your program.