What is It Like to Work As a Data Analyst?

Last updated on January 30th, 2021 at 07:08 am

If the job of a data analyst is your career choice, then research well because this is the job that you will be doing day in and day out for many years. There will be many aspects that you need to be well-equipped with to be successful in this career and it is hoped we can present a sort of cheat sheet to help you zero in your choice of career.

Data is What Your Job Is About

Modern times use data effectively and generate tonnes of data every moment. Since the job deals with data as a data analyst you will need to use the data both from internal sources of the organization like sales, marketing inventories and more, and outside sources like from the web, social posts, etc. This is an ongoing process of compiling, curating and keeping your data readily available. From here, the data is used to make inferences, find insights, track trends etc to help the business objectives with predictions, findings anticipated forecasts and such to base decisions on. Sometimes you may even have to train others on data analytics and your systems, tools, and techniques. So expect to do the following tasks regularly in your daily work-day.
1. Presentations and reports:
As an analyst, it is your job to make presentations of the data in understandable and succinct ways such that the next level of users of the report, are able to justify their decisions based on your reports. It definitely is not about just presenting data but making the inferences from the data clear and presenting it with supporting data.
2. Discovering the connections and patterns:
Reports should be regularly generated and presented in order to be able to spot any deviations, patterns, and trends. To produce an effective report is to find those connections, trends, patterns, etc that can be justified on the basis of your regular reports. Further, this task also means that others can access the databases easily and that they are in usable formats, stored safely and forming a framework that can be used by people who need data and may not necessarily know how to deal with it.
3. Data Collaboration:
Well, decisions are never taken in isolation and modern times are agile and involve experts working together as a team collaboratively. Your communication skills, presentation skills, and reporting abilities can be showcased to the hilt. It is also very useful in data collection and its maintenance to have harmonious relationships with all departments.
4. Infrastructure and tools:
Besides collecting the data, drawing inferences, maintaining data sets the data infrastructure itself needs to have a streamlined procedure and framework for garnering data regularly and across the organization. Such data needs to be processed and the framework maintained to ensure the right data is available to the decision makers at the right time.

Job Roles of Data Scientist vs Analyst

Are they the same or different job roles? If its data it cannot be so different. Right? No, wrong! There are many differences in the roles that you need to probe and research before making a choice of career.
Here is a brief list of qualities that should help understand the two roles and capabilities.
Data analysts:
• Require understanding and not in-depth knowledge of statistics, math, calculus, algebra and probability theory.
• Need keen business acumen and understanding of various verticals to provide insights across the organization.
• Their coding and computer science skills need not be of a very high standard.
• Their job is to visualize the data, cull, and drill through to the indicators of performance, and utilize the insights for business productivity.
• They use effective analytics and business tools.
Data scientists:
• Generally, have technical degrees and great in-depth statistical and math skills.
• Their business acumen is used in finding general patterns, trends etc from the data sets on which they work.
• Their coding and computer science knowledge are excellent.
• ML is used to identify trends, patterns and forecasts of a general nature.
• Their trend reports and analysis form the basis of their predictions.
• They also write code and ML algorithms to train the data set and enable analysis.
Depending on the size and type of organization you work for there may be overlapping or merging of job responsibilities and roles.
Categories of analytics:
The data analyst’s role demands the use of data by asking questions and finding answers to gain insights and make forecasts. Again, based on the kind of answers asked of the data the analytics can be subdivided into four different types. Namely
• When the answer describes what happened- Descriptive.
• When the data answers to the why question – Diagnostic.
• When the answer forecasts what could happen- Predictive.
• When the answer lets you know what action to take- Prescriptive.
The Data analyst is the expert with the solution to match the scenario with data and the right kind of analytics. For ex: if the logistics department suffers from schedule delays then a diagnostic algorithm can find the reasons for the delays. This can be further analyzed with other types of analytics like predictive questions to detect when the next event could happen or prescriptive questions to find out the right solution to the issues from the relevant data set.
Tools used:
From collecting data, preparing the data to visualizing and finding the right inferences the analyst uses a variety of tools like the Google suite of Google Analytics, Tag Manager, AdWords, programs from the Python libraries, K-clusters, SQL, Excel from the Microsoft stable, SAS, Spark, NoSQL, Tableau to interact with R programs and many more.

How to Become a Data Analyst

As long as there is a need for data there is always scope for data analysts and scientists. This plum role has very high payouts and makes a very good career choice. To be well-equipped, do a course from Imarticus Learning.
In parting, doing formal training and obtaining a certification from reputed institutes like Imarticus is the right path if the data roles are your career choice. Their courses stress on equipping you with the basic competencies of data analyst work namely your programming skills, ML, Statistics, Data visualization and munging. Your business skills are also honed through real-time industry relevant projects. Hurry, act on your choice and earn the benefits of a dream career.