The subject of Data analytics is a wide and varied topic. It has a large variety of micro subjects covering almost every field on the planet. This brings a huge wealth of subjects to work in and is a great career opportunity. Also anyone with acute attention to detail and a knack for problem-solving. What do you need to become a remote data analyst, data scientist or data engineer? What are the top tips for landing a gig in this field?
Start with a remote data analyst role
As every business requires some kind of data analysis,. The role of a skilled data analyst forms a core part of the business. Whether it’s in sales and marketing looking for advertising results using A vs B comparisons against web traffic or indeed as part of the core business being sold to the customers. Data analysts can work from home as effectively as they can in an office. The self-motivated data analyst can crunch those numbers and produce the reports with very little technology.
With fast reliable internet now available, across much of the developed world, working as a remote data analyst is now possible from a laptop, accessing cloud platforms for hosting as well as shared storage and collaboration tools from Microsoft (SharePoint/Office 365), or Google Workspace to name just two. These tools enable collaboration with co-workers across the globe.
Build your experience
As a data analyst, the skills required can be varied but often start with the spreadsheet. Calculation of totals is possible with spreadsheet data. And then represented and collated to produce reports in text or chart format. Complexity occurs when the task at hand extends to retrieving data from multiple sources.
Tokens allow calls to API systems to replace manual input of data into spreadsheets. And using code to manipulating the resulting data often in JSON or XML formats. There are many tutorials and step-by-step guides available online for developing these skills.
There are even tools to help you try out these methods. One such tool is Postman, a free API tool that allows you to call the target API. You supply the relevant credentials and retrieve the data in either JSON or XML format.
This data can be from a source that provides you with the data based on a query that you have to retrieve the relevant data. Perhaps it is average house prices or stock market values. You provide an input (which year, country, county) or perhaps which stock index. The resulting API call will return the data to you automatically.
Other skills may need to be developed to build up this kind of data collection activity. Typical skills include:
- Data science programming (e.g. Python)
- SQL query writing
- Data cleansing
- Data visualization
- Probability analysis
- Statistical analysis
- Presenting complexity to the layman
Building a picture with complex data and navigating through it to tell a story is the key role of a data analyst. This form of analysis can form the heart of a business. The answers to the questions asked can form the direct decisions made by the board of the company. Did our investment work? Show us the evidence.
Data analysis can lead to the role of a Data Scientist or Data Engineer. Based on past data patterns, these roles can involve developing machine learning models to predict future data. A Data Scientist would often build their own systems and experiments to find key patterns and trends. These advanced data analysis roles would typically involve
- An understanding of advanced machine learning methods
- An understanding of statistical methods
- An understanding of Big Data and large data set analysis
Automating the data collection
Once the data is collected once and the report and analysis are presented, it may be necessary to repeat this activity regularly to prove the outcome and spot further trends or developments. The results of this could form part of a business decision that may need to change if the result dips or increases.
Collecting that data could encompass machine learning or a scheduled automated task that pulls in the data from multiple sources, passes it to a database and automatically then produces the report that is sent to the relevant staff. This activity needs to be monitored and errors reported and corrected, often by the data analyst.
As part of the data collection process, it is also necessary to store, archive, and back up the data. This crosses over into data infrastructure and computer hosting areas as well as performance, vendor choice, licensing and environmental considerations. Data storage is not free and the data analyst has to decide how and where to store it now and in the future.
Things you can do to help get that role
The job market is wide and opportunities are available for freelance work as well as contract and permanent roles. To help you stand out you can focus on the following:
- An up to date and relevant CV
- A fully populated LinkedIn account profile
- Github portfolio showing skills and capabilities
- A blog/personal website
All of these areas allow you to build a public profile and allow potential employers to view your skills from a distance. Be sure to ask for feedback too when you find out that you were not shortlisted for a role. This will help you to fine-tune your profile so that you stand a better chance in the future for the next opportunity that comes along. The world of data analysis is huge and there will always be opportunities that come along.
The gig economy is a good place to get your feet wet with freelancing websites like Upwork, Freelancer and Fiverr where you can offer your abilities for small requirements and develop your skills as you go.