Top skills required for advanced data analytics

With the digital transformation of businesses and evolving technologies, enterprises have seen a significant increase in data silos due to the rapid expansion in the volume and variety of data. The probable challenge is to evaluate, comprehend, and act on enormous amounts of data.

Professionals gather data from diverse sources across the company, model it, and analyse it to help and support sound business decisions. This cohort should also look into Enterprise Data Analyst Associate certification. Database administrators, solution architects, data engineers, data scientists, AI engineers, and Power BI data analysts work in tandem. On an annual basis, a significant job demand is generated in this domain. But which expertise is most in demand in the data world?

Here are the top skills in demand to have a successful career in this domain.
1) SQL: The knowledge of SQL or Structured Query Language is a must to land a job as an Enterprise Data Analyst because practically all data analysts will need it in retrieving data from a company’s database. Often, a technical SQL screening is a part of data analyst interviews.
2) Statistical programming: Thanks to R and Python programming languages that make cleaning, analysing, and visualising large data sets simpler. While Python is more widely used, and easier to learn R is a statistical language used for analysing and visual representation of data.
3) Machine learning: Though Machine learning expertise is not a requirement for data analysts mastering machine learning techniques provides a competitive edge and put you on the path to becoming a competent data scientist.
4) Probability and statistics: This subset core of math and science deals with gathering, analysing, interpreting, and presenting data is known as statistics. This roughly resembles what a data analyst does.

Understanding probability and statistics help them achieve the following:

Determine the data’s patterns and trends.

Avoid including biases, logical fallacies, and other inaccuracies in the analysis.

Produce reliable and accurate findings.

5) Statistical visualisation: Finding insights into the data is just one step in the analytical process. Insights help develop a story that will guide smarter business decisions is another essential component. Data visualization help you narrate the statistical story in an interesting manner.
6) Econometrics: Econometrics is a method of estimating future trends by analyzing previous data. It employs mathematical and statistical models. Data analysts seeking employment in the financial sector, notably at investment banks and hedge funds, must know about it.

Data analytics is the science of finding significant patterns in data while “enterprise data analytics” (EDA) is a catch-all for the approaches, procedures, and tools used in the field. In fact, Microsoft has an exclusive certification course for enterprise data analyst aspirants.

Specific skills expected:

Implementing and managing a data analytics environment

Implementing and managing data models

Effective Data Visualization.

Advanced Power BI skills, including using Power Query and Data Analysis Expression

Maintaining data repositories, processing data both on- and off-premises (DAX).

Get skilled from Azure Synapse Analytics, knowledge of querying relational databases, Transact-SQL (T-SQL) to analyse and visualise data.

While earning a certification is a proven step to building a career as an Enterprise Data Analyst, enrolling for training from an authorised training partner for this course helps build confidence to crack this exam.

(The author is a founder of a cloud
consulting firm)