5 Key Skills That a Data Analyst Must Have

Making a career in data world seems tough. But a little effort plus mandatory education can put anyone ahead of curve. Joining the league of undesirable profession brings nothing but the grudge. Glitches intercept the way to successful career if one has inadequate knowledge.

A data scientist in the USA earns $120,000 while the same profile person collects $126,000 in the bay area yearly. Boom in the data industry is a clear indication of its prosperous future. This portfolio honours the person with desirable perks along with progression in career.

Having hacking skills, appetite for machine learning, data science, traditional research and substantive expertise are necessities for a data analyst. Good command over mathematics and statistics can be an edge. Aspirants who want to take a giant leap in short interval in data science should be the master of the below skills:

1. Developing Understanding: Theoretical knowledge makes one brainy. But practical environment creates a successful professional. Learning generates an acknowledged person. But what if a qualified MBA who is a fresher fails to clinch a business deal? He is the master of bookish knowledge. Then what led him to fallout?

The real battlefield is the job. It’s a practical world that comes with the daunting challenges. The real professional overcomes them with skills and structured process. Conquering challenges strengthens basic skillsets. Eventually, a great analyst takes birth.

Sometimes, the project is very much structured. But its completion needs usage of tools and techniques. At that time, the working experience and innovative attitude push the completion button.

2. Domain/Business Understanding: Insight carries A to Z of operational as well as functional units of an organization. Without prior knowledge, it would be a herculean task to serve the best of the skills. Comprehending drivers, the data analyst can easily comply with the business metrics.

The data analyst should be familiar with the terms, like data mining, data extraction, data munging, data cleansing and so on. It ensures conversion of raw data sets into the desired format that can easily be understood.  The tools, like Data Wrangler and data.table dplyr, can conveniently solve the problem.  
Data visualization, effectively, presents data in interpretable format. The aspiring data analyst should learn structuring it into the chart, graphs, table, and so on. 

Data analysis is the final step that this profession demands. So, the candidate should learn how to put the analysis and result into comprehensive layout. Thereby, the conclusion can subtly be understood by the observers.

3. Numeric and technical skills: Mathematics and statistics are the primary subjects. The analyst should be champ in that. For example, vetting or examining the data sets of various locations and names requires formula application.

Having knowledge of how to use python, SPSS, Hive and Pig etc. tools adds an edge to the skills. Data cleansing, statistical/ predictive analytics technique and interpreting the statistical output, clustering, time series, decision trees, and so on add enhancing skills.

4. Verbal and written communications: Technical skills help in completion of the project. But sometimes, the clients have queries. It needs to be removed by interaction. At that time, communication is the only mean that settle down all queries. Analytical aspect needs to be communicative. If its analysis is in demonstrative format, like pie chart, graphs and images, least efforts are required.

But a power point presentation makes it comprehensive. The crystal clear analysis can be presented. So, the data analyst should be well off with the communicative skills.  

5. Net Savvy: The data analyst should have tendency to explore internet. As big data repository is at hand on internet, the analyst should be used to it.