Key skills to become a data analyst and why it is rewarding
June 8, 2020
We live in the age of data explosions. There is an abundance of raw, unprocessed data that we usually don’t know what to do or how to understand or how to save. However, a data analyst can fill those shoes and help us gain a competitive advantage in the market or industry today. With the growth of data, the demand for the data analyst also increases. And when empowered with disruptive technologies like artificial intelligence, machine learning, the Internet of Things (IoT), data analysts can help businesses thrive and prosper in the days to come.
Data analysis refers to the qualitative and quantitative techniques, algorithms and processes used to improve productivity and business gains. Thus, data is extracted, recognized and bifurcated to identify and analyze behavioral data, techniques and patterns can be dynamic depending on the needs or requirements of a particular business. This is the reason why data analytics has a wide range of areas across multiple industries. Brands use data for a variety of purposes that align with their goals and their customers’ demands, making skilled data analysts one of the most influential people in the modern industrial world.
Regardless of the positions or jobs of industry data analysts, their duties generally include generating and extracting digital data, interpreting the data (mostly structured), observing trends, patterns in the data, product research, positioning, sentiment analysis, market analysis. After these, the final task is to generate and present detailed reports of the findings to the relevant authorities in order to make informed future decisions. So it can be assumed that the job of a data analyst is also quite exciting and challenging. Hence, you can also consider this as an option for a career.
Why be a data analyst?
According to an independent research report from Analytics Insight, Big Data is expected to grow at a CAGR of 10.9% in 2019-2023 while the global market value will be $ 301.5 billion by 2023. Hence , from the figures given, we can understand the vast opportunities available to us in this sector. And if you assume that the importance of the data analyst is limited only to the role of a data analyst, you might be wrong here. Having a competent training in data analysis is your visa to become a business analyst or enter the world of data science. And being a data scientist is considered the sexiest job of the 21st century, even by the Harvard Business Review.
Other options include Analytics Architect, Metrics and Analytics Specialist, Marketing Analyst, Sales Analyst, Financial Analyst, Operations Analyst, and more. One can also choose from the three types of data analysis depending on the big data environment, namely prescriptive analysis, predictive analysis and descriptive analysis. Additionally, data analysts are well paid for their work, and with the labor market demand exceeding entry-level applicants, the salary will increase in the future.
In addition, there is a massive deficit on the supply side even when the demand for analytical skills is steadily increasing. This deficit occurs mainly in the positions of analytical consultant and data scientist. Thus, there are a lot of vacancies in the market that can be filled. Even the tools they use are not limited. Data analysts can use Google Analytics, Tableau (for data aggregation and analysis), Jupyter Notebooks (test codes), Github (sharing projects), and many more. The exciting work environment never makes their work monotonous or tedious.
The first step in becoming a data analyst is a high level of statistical knowledge and a natural flair for mathematics. Subsequently, we can train by learning the following skills:
Computer skills: These include Java, C ++, MATLAB, Python, PHP programming and scripting and many more. Data management and manipulation skills are also essential. This takes place through languages such as HIVE, R, Scala and SQL, or Structured Query Language, SQL takes different forms on a wide range of data platforms, such as Microsoft’s T-SQL or MySQL, commonly used online. To be able to spot forecasting patterns and trends, it is possible to master Microsoft Power BI, SAS, Oracle Visual Analyzer and Cognos.
Microsoft Excel advanced: This is a critical skill that differentiates a data analyst from a data scientist. Data analysts should have a good command of Excel and understand advanced modeling and analysis techniques.
Data visualization: Effective data visualization requires trial and error. An effective data analyst understands the types of charts to use, how to scale visualizations, and knows which charts to use based on their audience.
Practical business and communication skills: Analysts need to be good communicators to ensure that the data provided matches the goals and criteria of the business. There may be times when an analyst needs to discuss and collaborate with executives, customers, other IT specialists, and various employees. They should be a good team player.
Creative and analytical thinking: The data is huge and booming. Analysts may need to understand techniques for cleaning, organizing, and structuring data in order to provide effective and reliable results. For this, it is crucial to have a creative and analytical mindset to be able to come up with interesting research questions and conclusions. In addition, a keen eye for detail is required to minimize incorrect misses and data redundancy.
To start a career as a data analyst, one can start by having a bachelor’s degree in subjects focused primarily on math, statistics, computer science, information management, finance, and economics. Then, after gaining relevant work experience, one can take a few courses that will help them stay one step ahead.
Because analytics helps improve the company’s value chain, leveraging industry knowledge and having a knack for exploiting opportunities before competitors, many companies are looking to hire data analysts. A wide range of organizations like Ayata, IBM, Alteryx, Teradata, TIBCO, Microsoft, Platfora, ITrend, Karmasphere, Oracle, Opera, Datameer, Pentaho, Centrofuge, FICO, Domo, Quid, Saffron, Jaspersoft, GoodData, Bluefin Labs, Tracx, Panaroma Software and countless others use Big Data Analytics for their business needs and huge employment opportunities are possible with them. So what are you waiting for?