Data analysts utilise specialised techniques and tools to determine how a business can use data to make better decisions. They’re able to impartially analyse vast amounts of data and communicate the impact of these results and are one of the most in-demand resources in 2019 and beyond.
Working across a wide array of industries, successful data analysts possess a specific set of skills that set them apart and allow them to pursue high salary opportunities using their expertise, especially in data science. With an average salary of $116,000, highly skilled data scientists have great opportunities available to them.
What does a data analyst do?
Data analysts process data, and turn this into business insights that can drive a company forward (by forecasting future KPI’s and revenue growth). With an understanding of costs, interpretability, latency and bandwidth, they’re able to not only run the data simulations to collect this information but analyse and understand it. An analyst can look at this data from an impartial view and deliver it to a variety of teams across a business.
Not limited to analysing one data flow, a data analyst can bring together information from different sources, extracting, transforming and loading (ETL) all information to one unified data warehouse, housing multiple sources rather than a solitary one.
Five skills of a successful data science analyst
While data science analysts have a wide breadth of skills across the technological and business spheres, these are five essential skills that a successful data science analyst will possess.
1. Machine learning and AI
Machine learning and expertise in artificial intelligence (AI) is one of the more recent necessary skills for a data analyst to be proficient in, with the two technologies increasingly prevalent in today’s businesses.
AI is a broader, more adaptive programming capability, whereas machine learning is a specific building of a machine to process data and continue learning a specific process. Data analysts are able to use machine learning to perform precise tasks, such as analysing customer data, predict lead scoring and then undertake sales forecasting.
2. Critical and strategic thinking
With the constant evolution of data and data analysis, those working in the field need to continually learn and keep up with the growing industry. Utilising solid business acumen helps data analysts identify and find solutions for the problems results pose, by not merely presenting the data to employees but explaining what it means. Essentially, data analysts are able to craft a story with the data and take it from merely numbers or percentages to issues or solutions within a business. Practically, data scientists can predict how businesses will be impacted, advising on how to guide the business in the right direction.
Eliminating bias and delivering information with impartiality is an integral part of utilising critical thinking as a data analyst. Quite often results will need to be delivered to team members with little to no technical expertise. By using impartial delivery, you can communicate the data’s story, not just the results, and explain how it affects the business in a multitude of ways.
3. Applied statistics
A more traditional method of learning, data analysis is an extension of applied statistics, with mathematics-based learning integral for your growth as a data analyst. By perfecting this, applied statistics helps you understand summaries, describe graphic forms and analyse all basic data.
Measuring tendencies, variability and data sampling distribution are key steps on the way to presenting data at the highest level.
4. Online Analytical Processing (OLAP)
OLAP allows data analysts to analyse information from multiple database systems at the same time. An analyst can use OLAP to extract and view business data from different points of view. Divided into cubes, OLAP creates multidimensional data with different and unrelated sources, allowing a multidimensional analysis and leading to better decision making. This gives analysts faster access to shared data, which in turns gives an analyst’s company shared knowledge to use at their will.
5. Programming Skills with SQL and Python
Structured Query Language (SQL) is one of the more traditional components of data science, and although not as prominent as it used to be, it is still an essential skill to learn. A successful data analyst needs to be able to write and execute complex queries, allowing you to add, delete and extract information from a database.
Carrying out analytical functions and transforming database structures helps you understand when to utilise database querying, undertaking concise commands that help you with time management.
Python coding is one of the most common coding languages, alongside Java, Perl or C/C++. Incredibly versatile, Python can help across all data science processes. Allowing you to import SQL tables into your code with ease, this creates datasets and allows you to find anyone on Google.
A field with growing opportunities
Data science analysts are in demand across a wide range of industries, including:
- Transport management
- Mass market retailing
- Workforce analytics
- Banking and insurance.
If you want to ride this growing wave of data analyst opportunities, you may choose to get qualified with a Master of Data Science at the University of New South Wales. Offered completely online, this two-year program allows you to maintain your business or current employment while coming away with a cutting-edge degree. You’ll be equipped to apply for a multitude of high paying jobs in a number of different environments. In fact, UNSW Master of Data Science graduates are placed in the top five per cent for median starting salary, which is the highest of any university in New South Wales. Find out more about this exciting degree today!