9 Must-Have Skills You Need To Become A Data Scientist

It would be fair to say that today’s world is a world of technology and data. The rise of technology has given birth to data. Therefore, it is justified to say that we live in a digital age surrounded by the bulk of data. Like IT professionals and geeks who have played a role in the development of technology, people who can extract meaningful information are required in the data science field.

Data science has progressed a lot in recent years and will continue to grow in the coming days. This has provided with several job opportunities for young people who wanted a career in this industry. Whether its statistics, machine learning, data visualization, deep learning, cloud computing, cybersecurity, big data, IoT or data analysis, the demand for professionals who can dig deeper into the “analytics of things” is constantly growing.

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Because you’re here, reading this article, we are sure that you’re interested in data science, so you might be interested in pursuing your career as a data scientist as well. Before delaying any further, let’s go through the list of 9 must-have skills needed to become a data scientist:

1. Educational Background

A strong background in Computer science is a pre-requisite for data scientists. Having a bachelor’s degree in Social science, statistics, physical science, and mathematics is also preferred. Majority data scientists have a master’s degree or Ph.D., they have also enrolled in different data science bootcamps. The skills which are learned from these bootcamps and master’s program boost the career.

2. Machine Learning

Machine learning skills are a must-have. You must be familiar with its method if you’re working at a company who provides data-driven products (e.g. Google Maps, Amazon, Netflix, Southwest Airlines, Uber). ML methods are generally structured in R and Python and data scientists can use the pre-structured libraries. Different machine learning algorithms are:

  • Linear and Logistic Regression
  • Support Vector Machine
  • Naive Bayes
  • KNN
  • Random Forest
  • Dimensionality Reduction Algorithms

3. Quantitative Skills

Quantitative skills involve the core understanding of mathematics and statistics, play a significant role in data science. In the domain of data science, we deal with real-world problems and quantify them into data from which useful insights can be extracted. Proper analysis of data is only possible when you have a better understanding of statistical analysis techniques. Familiarity with statistical tests, probability, distributions, maximum likelihood estimators, etc is important. Statistical analysis is part of many businesses having a data-driven approach.

4. R Programming

R has been specially designed for data science applications like data mining, data extraction and etc. It is primarily designed for data analysis and statistical computing. Many statistical problems are solved using R language which has made it one of the most popular languages in data science, the other one is Python. 43% of the data scientists are using R. However, it is difficult to learn and its learning curve is a bit steep.

5. Data Wrangling

Data Wrangling or data munging is a process of mapping and transforming data from a single raw data form in different formats with the intent of making it more useful. Often, the data is difficult to handle and it is necessary to convert it into a form that is manageable and can provide insights. The data may have noise, which means that it has so much to discard and so less to use. It is one of the most sought skills in the industry and it comes with experience.

6. Problem-solving ability

Problem-solving quality is good-have for anyone, it doesn’t have to be associated with data science but without this, it would be hard for a data scientist to deal with bulk of data. Being a data scientist, you are not only required to know the solution to a problem that’s defined for you, but you also need to define and evaluate the problems. If you’re good at problem-solving, then you can solve any problem … all you need is confidence and experience.

7. Certifications And Bootcamps

Certifications are a great add-on in your portfolio. Many reputable organizations prefer professionals with the best data science certification. Their game comes into play when higher degrees come to a halt, which means you can no longer earn a degree higher than that. The organizations recruit the professionals on this basis of certifications because the majority of them have the same degrees and this will help you gain an edge over your peers. Here is a list of a few certifications:

  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Cloudera Certified Associate: Data Analyst
  • Cloudera Certified Professional: CCP Data Engineer
  • Certified Analytics Professional (CAP)
  • Data Science Council of America (DASCA)
  • Microsoft Certified Azure Data Scientist Associate
  • Dell Technologies Data Scientist Associate (DCA-DS)
  • Dell Technologies Data Scientist Advanced Analytics Specialist (DCS-DS)

Data science bootcamps are equally effective for gaining an edge, some of the popular ones are mentioned below:

  • NYC Data Science Academy
  • The Dev Masters
  • Springboard
  • General Assembly
  • Metis
  • Dataquest
  • Ubiqum Code Academy
  • RMOTR
  • Data Science Dojo
  • Thankful

8. Communication

You must be a good communicator to be a successful data scientist. You must be wondering why and the answer is very simple. Data scientists don’t only interact with computers but they also interact with stakeholders and professional. Good listening skills are also valued while working in this field. Strong communication skills come into play during data visualization, if you understand the data but you are unable to communicate it then it is a negative point.

9. Business Knowledge

Despite having technical skills, business knowledge stands strong in the list of non-technical must-haves and cannot be ignored at any cost. A business-savvy mind helps in problem-solving and leads to a better understanding of business and work domain.Business knowledge is divided into three basic levels:

  1. General Business Knowledge ( which remains the same for every business)
  2. Industry-Specific Knowledge( which depends on the business industry)
  3. Company-Specific Knowledge( which depends on the company)

Data science is constantly evolving and to pursue a career in this field, staying current is as important as the aforementioned skills.