Massive volumes of data are flowing into all organizations today. From big to small and across all industry verticals, there is a variety of data coming in – from sales to customer preferences to employee records. All this data can be a valuable source of useful insights that could guide an organization to make sensible strategic decisions on its future course of action. And that precisely is where data science comes in.
A data analytics career is a highly sought after option these days, for professionals from many different backgrounds. The work of a big data analyst is critical to organizations, and there is a shortage of suitably skilled professionals to meet the demand. The salaries and benefits packages too are very impressive.
What exactly does a data analyst do?
Data collection, processing, and statistical analysis of data are some of the things a data analyst does. The idea is to make the mass of data useful by prioritizing the raw data using suitable formulae and algorithms. This then helps the organization to take the right decisions.
Opting for a data analytics career is a great choice for someone who is passionate about numbers and algebra, and who enjoys presenting the work and insights to other people.
What tasks are performed by a data analyst?
The important tasks performed by a big data analyst are:
- Designing and maintaining data systems and databases
- Mining data from primary and secondary sources, and reorganizing it in a format readable by human or machine
- Working with statistical tools to interpret data sets, looking for trends and patterns valuable for diagnostic and predictive analytics efforts
- Presenting reports to executive leadership, using the data to communicate trends, patterns, and predictions
- Collaborating with programmers, engineers, and organizational leaders to identify opportunities to improve processes, modify systems, and develop data governance policies
- Preparing suitable documentation to help stakeholders understand the data analysis process steps and to facilitate duplication or replication as needed
Throughout a data analytics career, the work of an analyst is significant in the context of local, national, and global trends affecting the organization as well as the industry at large.
An analyst requires a big set of skills.
For a big data analyst, it is essential to be strong in programming. The following skills come in handy:
- Programming with R: R supports statistical computing and graphics, and is very popular. Some of the useful focus areas include:
- Dplyr: a bridge between R and SQL, it translates the codes in SQL and also works with both types of data
- ggplot2:a system helping users build plots iteratively, to be edited later as needed based on the graphics. ggally (to prepare network plots) and ggpairs (matrix) are the useful sub-systems.
- reshape2: this is based on two formats – meta (converts broad format data to long format data) and cast (converts long format data to broad format data)
- Programming with Python: Python is popular due to its simplicity and hence its suitability for large projects. It is free, open-source, works across platforms, and offers extensive libraries.
- Statistics: This is essential to interpret data. It helps to know how to form data sets; basic knowledge of mean, median, mode, probability, anova, chaining and distribution, and more.
- Mathematics:Advanced knowledge of matrices, linear algebra, relational algebra, CAP theorem, framing data, and series is useful.
- Machine learning: Essentially a combination of multivariable calculus, linear algebra, and statistics, it is among the most powerful skills. This includes supervised, unsupervised, and reinforcement learning.
- Data wrangling: Raw data is transformed into properly structured, logical and workable sets. This may need work with SQL and non-SQL-based databases.
- Communication and visualization: It is essential to communicate the derived insights to all stakeholders, for which knowledge of visual encoding tools is essential.
What other skills are needed?
Aside from programming and other core skills, the following are also useful for a big data analyst:
- Microsoft Excel
- Web development
- Data mapping skills
- Ability to find patterns in large data sets and to derive actionable insights
Is certification helpful?
For a data analyst, a big data analytics certification from the Data Science Council of America (DASCA) is a great choice. A preeminent developer of the most definitive credentials for marking excellence in the big data profession, DASCA offers the following certifications:
- Associate Big Data Analyst (ABDA™): for graduating students with majors in business, management, economics, statistics, or allied disciplines
- Senior Big Data Analyst (SBDA™): for experienced analytics, marketing, and research professionals who want to either move into or are already working in the big data space