As the demand for data scientists increases, the discipline presents an enticing career path for students and existing professionals. This includes those who are not data scientists but are obsessed with data and data science, which has left them asking about what data science skills and big data skills are needed to pursue careers in data science.
Leveraging the use of Big Data as an insight-generating engine has driven the demand for data scientists at the enterprise-level across all industry verticals. Whether it is to refine the process of product development, improve customer retention, or mine through data to find new business opportunities, organizations are increasingly relying on data scientist skills to sustain, grow, and stay one step ahead of the competition. Also, in this article, we will dive into technical and non-technical skills required for data scientists.
You can get started on your data science journey with our Data Science Bootcamp. Enhance your career prospects with Simplilearn's in-depth data visualization courses and gain proficiency in data storytelling.
Learn for free! Subscribe to our YouTube Channel & Be a Part of the 400k+ Happy Learners Community.
Skills Required For Data Scientists
The 2 types of important skills are:
- Technical
- Non-technical skills
- If you are an aspiring data scientist, the information in this article can help guide you on your path toward a lucrative career in this exciting and growing industry.
- If you are a data analytics director at an organization, you can leverage the information to train your existing team of data scientists with the top data science skills, which can make them more productive and efficient with their work.
Moving forward, let us discuss what are the technical skills required for a data scientist role.
Technical Skills Required For Data Scientists
Some of the most important technical data scientist skills are:
- Statistical analysis and computing
- Machine Learning
- Deep Learning
- Processing large data sets
- Data Visualization
- Data Wrangling
- Mathematics
- Programming
- Statistics
- Big Data
Some data scientists have a Ph.D. or Master’s degree in statistics, computer science, or engineering. This educational background provides a strong foundation for any aspiring data scientist and also teaches the essential data scientist skills and Big Data skills needed to succeed in the field, including:
There are some schools that now offer specialized programs tailored to the educational requirements for pursuing a career in data science, giving students the option to focus on the field of study they are most interested in, and in a shorter period of time.
Some of the many options available include Massive Open Online Courses (MOOCs) or bootcamps, such as Simplilearn’s Big Data & Analytics certification courses. These types of programs offer practical learning methods that you will not find in the confines of the textbook, including a hands-on approach to learning in-demand data science skills, Capstone projects, and other exercises that help prepare students to become data scientists.
Other technical data scientist skills required include:
1. Programming
You need to have knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles. These programming languages help data scientists organize unstructured data sets.
# Pro tip: To learn more about the Programming languages, click here!
2. Knowledge of SAS and Other Analytical Tools
Understanding analytical tools is one of the most helpful data scientist skills for extracting valuable information from an organized data set. SAS, Hadoop, Spark, Hive, Pig, and R are the most popular data analytical tools that data scientists use. Certifications can help you establish your expertise in these analytical tools and help you gain this valuable data science skill!
3. Adept at Working with Unstructured Data
Data scientists should have experience working with unstructured data that comes from different channels and sources. For example, if a data scientist is working on a project to help the marketing team provide insightful research, the professional should be well adept at handling social media as well.
Some of the other data scientist skills required are Machine Learning, Artificial intelligence, Deep learning, Probability and Statistics.
Moving forward, let's discuss the non-technical skills.
4. Web Scraping
Web scraping is the automated process of extracting data from webpages.
5. ML with AI and DL with NLP:
Deep learning (DL) with natural language processing (NLP) focuses on using neural networks to process and understand human language. Machine learning (ML) and artificial intelligence (AI) are both concerned with teaching computers to learn from data.
6. Problem-Solving Skills:
Skills for Solving Issues the capacity to evaluate challenging issues and develop workable answers.
7. Probability and Statistics:
Statistics and probability is the study of randomness and uncertainty in statistics, and the application of mathematical tools to decision-making.
8. Multivariate Calculus and Linear Algebra:
Advanced mathematical ideas used in machine learning and data analysis include multivariate calculus and linear algebra.
9. Database Management:
The procedure of arranging, saving, and accessing data in a database system is known as database management.
10. Cloud Computing:
Utilizing remote servers to store, control, and handle data and applications online is known as cloud computing.
11. Microsoft Excel:
Microsoft Excel is a spreadsheet program used for data display and analysis.
12. DevOps:
A technique of developing software that places a strong emphasis on teamwork and communication between the development and operations teams.
13. Data Extraction, Transformation, and Loading:
Data collection, cleansing, and preparation for analysis is known as data extraction, transformation, and loading.
14. Business Intelligence:
Business intelligence is the process of using tools and techniques for data analysis to acquire knowledge and guide business decisions.
15. Neural Networks:
A data scientist should possess skills in designing, training, and fine-tuning neural networks for various use cases, as well as knowledge of different neural network architectures and frameworks.
16. Model Deployment:
Data scientists need expertise in model deployment, which involves making trained machine-learning models available for use in production environments.
17. Data Structures and Algorithms:
The fundamental ideas in computer science that underpin effective data storage, retrieval, and computational problems are known as data structures and algorithms.
Non-Technical Skills Required For Data Scientists
Along with the technical data scientist skills, we will now shift our focus on non-technical skills that are required to become a data scientist. These refer to personal skills and can be difficult to assess simply by looking at educational qualifications, certifications, and so on. They include:
18. A Strong Business Acumen
The best way to productively channel technical skills is to have strong business acumen. Without it, an aspiring data scientist may not be able to discern the problems and potential challenges that need to be solved in order for an organization to grow. This is essential for helping the organization you’re working for explore new business opportunities.
19. Strong Communication Skills
Next on the list of top data scientist skills is communication. Data scientists clearly understand how to extract, understand, and analyze data. However, for you to be successful in your role, and for your organization to benefit from your services, you should be able to successfully communicate your findings with team members who don’t have the same professional background as you.
#pro tip, have a look at these resources:
20. Great Data Intuition
This is perhaps one of the most significant non-technical data scientist skills. Valuable data insights are not always apparent in large data sets, and a knowledgeable data scientist has intuition and knows when to look beyond the surface for insightful information. This makes data scientists more efficient in their work, and gaining this skill comes from experience and the right training. However, this data scientist skill comes with experience and bootcamps are a great way of polishing it.
21. Analytical Mindset:
The capacity to dissect complicated issues into their component parts, analyze those parts, and derive conclusions from the data.
22. "Out-of-the-Box” Thinking:
Using creative and innovative thinking to generate novel ideas and unconventional answers.
23. Critical Thinking:
The process of evaluating and analyzing data in order to make a judgment or choice is known as critical thinking.
24. Decision Making:
Making decisions entails choosing the best course of action from a range of alternatives after carefully weighing all pertinent information.
How to Become a Data Scientist?
To become a data scientist, you should acquire a strong foundation in mathematics, statistics, and programming. Gain expertise in data manipulation, analysis, and visualization. Master machine learning techniques and algorithms. Build a portfolio of projects showcasing your skills. Continuous learning and staying updated with industry trends are also essential for success in this field. You can follow this step-by-step guide to thoroughly understand the process of becoming a data scientist.
Choose the Right Program For Your Career Growth
To assist you in making an informed decision for your data science career, we have provided a comprehensive comparison of all our courses. This detailed comparison will help you select the program that best aligns with your goals and aspirations, ensuring you stay ahead in the dynamic field of data science.
Program Name Data Scientist Master's Program Post Graduate Program In Data Science Post Graduate Program In Data Science Geo All Geos All Geos Not Applicable in US University Simplilearn Purdue Caltech Course Duration 11 Months 11 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more 8+ skills including
Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more8+ skills including
Supervised & Unsupervised Learning
Deep Learning
Data Visualization, and moreAdditional Benefits Applied Learning via Capstone and 25+ Data Science Projects Purdue Alumni Association Membership
Free IIMJobs Pro-Membership of 6 months
Resume Building AssistanceUpto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program
Take the first step toward reaching your career goals and enroll in an accredited Caltech Post Graduate Program in Data Science program today.
FAQs
1. Does Data Science Require Coding?
Yes, data science needs coding because it uses languages like Python and R to create machine-learning models and deal with large datasets.
2. What Programming Language Should I Learn First To Become a Data Scientist?
Depending on the precise work requirements, Python is a popular language for data science because of its ease of use and extensive library support.
3. Can I Become a Data Scientist Without a Degree?
Yes, becoming a data scientist does not always necessitate a degree. Using projects and internships to demonstrate abilities and expertise can also result in job openings.
4. Can I Become a Data Scientist Without Any Experience?
You can become a data scientist even if you have no previous experience. One can acquire the required skills by developing a strong portfolio of individual projects and learning from online resources.
5. Is it hard to become a data scientist?
Becoming a data scientist can be challenging, as it requires a combination of technical skills, domain knowledge, and practical experience. The field encompasses various disciplines such as mathematics, statistics, programming, and machine learning. However, with dedication, continuous learning, and hands-on practice, it is possible to acquire the necessary skills and succeed as a data scientist.
