Data Analyst and Data Scientist skill set in Digital Marketing

Is there a big demand for Data Analyst and Data Scientist in the job market these days? Yes, there is. With Big Data and the Internet of Things popping up, companies are recruiting those who have skills in customer and user insights, data analytics, statistical analytics, programming, machine learning, and data visualization. As a digital marketing graduate student who is looking for a job opportunity in big data and digital marketing field, I feel that getting a master degree in digital marketing is not enough because companies are looking for those who have experience in some tools that are related to digital marketing and data analytics. Indeed, having the basic knowledge in Data, such as excel, to analyzing data is not sufficient to compete aggressively in the job marketplace. In this blog will explain what is the difference between Data Analyst, Data Scientist, and states the important skills that each job role must acquire.

To begin, when I try to search for a job in the digital marketing field- from my experiences, I have noticed that companies are looking for those who skilled in content marketing, data analysis, SEO, PPC, Email marketing, HTML/CSS, and graphic design. However, these are not the only skills that they are looking for. Currently, there is a big demand for a data scientist and data analyst to be hired in the digital marketing area. So, as a digital marketer, what are the most important skills that you should have in your resume to function as a data analyst or data scientist in an organization?

Before start answering this question, first, let me define what does data analyst and data scientist mean? and what are their main roles in an organization?

According to SAS, “Data scientists are a new breed of analytical data expert who has the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.”

Data scientists job duties

• Collecting large amounts of unruly data and transforming it into a more usable format. 
• Solving business-related problems using data-driven techniques. 
• Working with a variety of programming languages, including SAS, R, and Python. 
• Having a solid grasp of statistics, including statistical tests and distributions. 
• Staying on top of analytical techniques such as machine learning, deep learning and text analytics. 
• Communicating and collaborating with both IT and business. 
• Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line. 

Data scientist’s toolbox 

• Data visualization: the presentation of data in a pictorial or graphical format so it can be easily analyzed. 
• Machine learning: a branch of artificial intelligence based on mathematical algorithms and automation. 
• Deep learning: an area of machine learning research that uses data to model complex abstractions. 
• Pattern recognition: technology that recognizes patterns in data (often used interchangeably with machine learning). 
• Data preparation: the process of converting raw data into another format so it can be more easily consumed. 
• Text analytics: the process of examining unstructured data to glean key business insights.

On the other hand, Data Analyst defined will be responsible for importing, transforming, validating or modeling data with the purpose of understanding and drawing conclusions from the data to drive operational decision-making within the organization. Mainly looking at historical data from new perspectives, and presenting this historical data in charts, graphs, and tables as well as designing and developing relational databases for collecting data.

Data Analyst job duties

• Interpreting data, analyzing results using statistical techniques and providing ongoing reports
• Developing and implementing databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality
• Acquiring data from primary or secondary data sources and maintaining databases/data analysis systems 

Data Analyst’s toolbox

• Ability to Build and Maintain Analytical Models 
• Analyze Consumer Demographics, Preferences, Needs, and Buying Habits 
• Basic SQL Experience 
• Experience with Statistical Software Platforms (SPSS, WinCross, SAS, Market Sight) 
• Hands-On Experience with Strategy and Competitive Analysis 
• Use Statistical Software to Analyze Data 
• Working Knowledge of Tableau

So, why does the digital marketing world continuously demand marketers with analyst and data scientist skill set?

Due to the rapid evolution of technology, it has become obvious that companies are attracting some of the best talents who are able to use data to understand customers and predict buying behavior and to create meaningful marketing campaigns and improve the customer experience. In addition, to use data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets. Finally, having a strong communication and storytelling skills to present data in order to reveal insights which will lead the stakeholder to an ultimate conclusion. It is time for marketers to start thinking like data scientists by focusing on obtaining skills that help you to analyze and visualize data. These skills are required to evaluate what is happening in a business and to model solutions that will take advantage of a new opportunity or mitigate a challenging situation.

Are Digital Marketing Certifications a Key to Marketing Professionals?

Yes, they are. Many experts encourage digital marketers to get Digital Marketing Certifications. If you are interested in getting certification in digital marketing, I encourage you to check this link:  10 Best Free Digital Marketing Certifications and Courses for 2017.

To sum up, in the past companies was looking for an employee who masters Excel very well to do data analytics and data visualization. However, with the advancement of information technology, the situation has changed dramatically. Today companies are hiring people who are enthusiastic and passionate about understanding and implementing big data.