Top 10 skills for data scientist/data analyst in 2019

If you have a knack for numbers and statistics, a career in data analysis or data science is a good fit for you ( and the pay is pretty good too). Harvard Business Review even dubbed “data scientist” as the “sexiest job of the 21st century.”

Data science and analytics (DSA) jobs are in high demand. According to the Royal Society report, there has been an explosive growth in data scientists job postings with 1287% growth from 2013 to 2018 whilst data specialist job postings increased by 231% in the same period. With an average salary of £64,376, being a data scientist can really pay off.

So What Does Data Analyst and Data Scientist do?

Data Analyst Responsibilities:

Data analysts sift through data and provide reports and visualizations explaining what insights can be revealed through data. When somebody helps people understand specific queries with charts or data, they are filling the data analyst role. In some ways, you can think of data analysts as junior data scientists.

Data Scientist responsibilities:

According to “Doing Data Science,” a book based on Columbia University’s Introduction to Data Science class, a data scientist is someone who “spends a lot of time in the process of collecting, cleaning, and munging data because data is never clean.”. It further explained that “a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms—some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership.”

Data Scientist vs Data Analyst:

There can be a lot of confusion between data science and data analysis as requirements for them can be somewhat similar.

-From our experience, a data analyst can be seen as an entry-level role and a junior data scientist job in many firms.

-Data Analysts tend to be more client-facing, and they tend to use data to provide business insights and solve challenges the business is facing.

-Data Analyst does more descriptive (i.e.what happened) and diagnostic (i.e. why it happened?) analysis, which involves analysing and explaining past events of the data analysis.

-Meanwhile, the data scientist skillset can be built upon a data analyst skillset, with additional Machine Learning and Software Engineer skills. They tend to apply varied statistical, algorithmic and predictive techniques to existing datasets to find patterns in data and transform them into useful information for organisations.

-Data Scientists also tends to do more predictive (i.e. what will happen) and prescriptive(i.e. what should I do?) analysis, which involves more about predicting the future and actions to implement based on those insights.

In-demand skills for data analyst/data scientist:

According to Royal Society report, the top 10 data scientist clusters are:

As can be seen from these clusters, data scientists usually expected to know about scripting programming languages like Python, R or SQL. Solid knowledge of statistics, data analysis and data science techniques such as data mining, data cleaning and data analysis are required. Depending on the role, the ability to use Big Data open-source tools such as Hadoop or Apache Spark can be vital for the position.

Apart from those very popular skills, two of the most critical aspects that often get overlooked for data science jobs are industry expertise and communication skills. Industry expertise allow you to know what to look for,  and combined with technical skills, it will be more likely for your solution to be implemented. Another important aspect of the role is communication skills. Data Scientists tends to work in different teams across different departments of the company, and they will also have to communicate their findings to various stakeholders. Consequently, communication skills are becoming more and more necessary for both data scientists and data analysts.

One very popular characteristic of a data scientist is that 74% have an advanced degree (Master/PhD), according to KDNuggets. Even though it is not a requirement in most of the job postings, without an advanced degree and at least 2 years of experience, you will be better off applying to data analyst positions.

Want to constantly keep your knowledge updated in the industry, one of the good way to listen to these data science podcasts by leading data scientists in the industry.