The sheer amount of data generated during interactions between people and organizations is too much for a company without technical data knowledge to effectively collect, organize and analyze. Data scientists exist to solve this very problem. Unlike data analysts, data scientists have these core responsibilities:
- They are data visualization experts, who find patterns in data that would never be found if the data is in its raw format.
- They gather and analyze data and use a wide array of techniques to process it and reach a conclusion.
- They work with advanced algorithms, which they mostly create by themselves (especially those with a background in computer science and statistics and mathematics).
- They are the experts who tell you what all the data you have collected means.
To find out more about data science skills and where to get a data science course, read below.
Opportunities for Data Scientists
Responding to the question, “how will data science change in the next 5 years,” Anthony Goldbloom, co-founder and CEO of Kaggle, noted that the value of data science as a key decision-making tool is becoming increasingly apparent in most organizations.
A good example is Air BnB which in 2013 had 5 data scientists. By 2016, the number had grown to 70, providing analytical support to all business units, with excellent results for the company. This reflects the trend among leading companies that are data-centric.
As the demand increases, so do opportunities for anyone with the right mix of skills. According to PWC, the most valuable professionals in the data science field are T-shaped individuals. This means that in addition to possessing the necessary technical skills, you should demonstrate a wide variety of soft skills such as:
- Effective communication. You will, after all, be advising top-tier management about the direction of the business.
- Teamwork and willingness to work cross-functionally
- Critical thinking and creativity
Other evidence shows that professionals who pursue a data science career find the salary to be well-worth their while. According to Glassdoor, in 2017, the data science category, with a median salary of $123,000, was the leading profession in America.
Skills required to become a data scientist and how to develop those skills
Education and training
Anyone can become a data scientist as long as they have the principal skills for the job. However, 365 Data Science conducted a study that showed that at the moment, the industry is more male-dominated. Working with 1,001 resumes of LinkedIn professionals with the words ‘data scientist’ in their profile titles, the survey concluded the following:
- 70% of the resumes were male
- They speak at least 2 languages
- Hold masters or PhD
- Are conversant with R or python
- Have between 2 and 4.5 years of work experience
But while masters and PhDs hold a lot of weight, the institution that issued the degree is not important. People from unranked universities have joined the field and succeeded in building a career in data science. From the sample, 25% of the samples were from unranked universities. What’s more, 40% of the resumes reported having taken an online course.
This is good news. Becoming a data scientist doesn’t mean you have to go back to university for another few years of data science and analysis specialization. Whatever your current degree qualification, you can enroll for a relevant online course.
The study also showed that people without a data background could go into data science and excel. 64% of the data scientists worked in different fields in their previous jobs or were scholars. This means that even if you have little experience or you weren’t a data scientist in the past, you can be competitive as long as you have the necessary certifications. Note that 8% of people currently working as data scientists were interns in their previous job.
Although there are many programming languages used in data science, R and Python are the most dominant languages. Projections show that python will be the most popular language by 2019.
Figure 1: Python the fastest growing programming language
Domain knowledge, management skills, machine learning, and others
The more knowledge and skills you have, the more marketable you will become. Knowledge in areas such as Tableau, LaTex or Hadoop will be an added advantage. An analysis by PWC shows the following as the important skills to have for a data scientist
Figure 2: Data scientists should have a wider range of skills
Recommended courses for Data Scientists
This 10-data science courses program from Simplilearn co-developed with IBM will put you on a learning path that will equip you with the skills and confidence you need to become a data science master. You will learn data science with R, Hadoop, Python, SAS, Capstone and use a wide variety of tools to test out concepts such as regression models, data exploration and hypothesis testing.
For hands-on projects, you will have access to real data from 15+ domains such as retail (Walmart, Amazon, Target, etc.), insurance (AIG, Berkshire, etc.), banking, stock exchange (e.g., NASDAQ), among other industries. You will clean the data, build visualizations, predict sales and discover how factors such as unemployment and other industry-specific trends affect business.
You can pace your learning; classes are recorded, so that you can revisit challenging areas later on.
Certification: yes (per course)
This course is offered as part of a collaboration between Microsoft, universities, and experts from industry. The course teaches the fundamentals of data science and imparts a wide range of skills and methods for analysis.
Students can choose to study different data analysis and visualization methods using different languages and tools. Depending on the learning path you choose, you will gain mastery of the following areas:
- How to use Microsoft Excel, R or python as data analysis tools
- Creating data models and data visualization with Power BI or Excel
- Implementing machine learning models with python or R, including validating models with Azure
- Application of data science to solve different scenarios
- Database query with Transact-SQL
- Data analysis using statistical methods
Offered by the University of Michigan, this Python-based course uses theory and hands-on projects to improve techniques and methods used in the analysis. You will work with toolkits such as matplotlib, pandas, scikit-learn among others. Before you begin, you need to have a background in programming. By taking this course, you will:
- Get introduced to data science using python
- Learn data plotting and representation
- Explore machine learning in data science
- Learn to interpret data visualizations
- Apply statistical methods to the analysis
You can set your own study schedule and have the ability to pause and restart at your pace. If you consistently study for 7 hours a week, you will finish the course within 5 months.
This course is offered by Coursera in collaboration with academic professionals from Johns Hopkins University. The 10-course program will teach you how to use R to query data, manipulate it, create visualizations, interpret it and communicate results.
In addition to learning the theoretical aspects of data science such as regression analysis, you will use GitHub to work on projects based on real data.
To succeed, you need a beginner’s understanding of python and regression. While deadlines for learning are flexible, dedicating 5 hours weekly will guarantee that you finish the course within 8 months.
Learning a new skill can be time-consuming and challenging. But the reward in being a data scientist is evident in the compensation, not to mention the value you will hold within a company. The key to success is to:
- Remain disciplined,
- Set a specific amount of time to learn,
- Practice daily, and
- Stay in touch with data science communities and forums.
Once qualified, expect to work in an office where you will collaborate with teams on projects and communicate with people lower or above you.
Companies differ. As such, the tempo of your work environment will depend on the industry you work in and the specific company you work for. Some environments, especially online businesses, retail, and stock markets are fast-paced and expect quick results while other industries require slow and methodical analysis. Whichever environment you find yourself in, you will be a key contributor to the growth of your company.