Last few months we had been hand-holding a few students from a reputed college, who were in the final semester of their 5 Year post-graduation course in Computer application and Data science. The experience I got interacting with them, brought me an idea to pen down a blog of interest to the young data aspirants. As the systems and technology has progressed too much ahead of their syllabus, and due to the CoVid-19 study from home model , which has disrupted a lot of updations to almost every student’s curriculum during this period. It was of utmost necessary to bridge the gap by undergoing an internship program. It was to our surprise that the power of data science was not known to most of them. And even they had not set up their career goal in selecting which role to aim for, whether it is a data analyst role or data architect or a data scientist. In my future blog, I would be describing these roles in detail.

The traditional approach to education typically focuses on what is prescribed in the syllabus. Teachers give a lecture, students take notes, and are tested by means of examinations. However, this approach doesn’t work well for all subjects. Data Science has come a long way. Fast-forward to the present date; Data Scientists are the most important resources for any business looking to thrive in this mad rush. They are now the ‘wizards of all problem solvers’.

This is the primary reason the syllabus of Data Science courses includes concepts that touch base on cloud computing, big data, natural language processing, and data sentiment analysis. A Data Scientist is responsible for deriving sensible outcomes from large data sets and, enable a business to make the right decision.

A solid foundation of skills and experience is required for a successful  data analytics career. Students should learn basic data analysis techniques, data-oriented programming languages, and have a strong mathematical background. To succeed in this industry, aspiring data analysts also need excellent communication skills , teamwork, and leadership skills.

If you’re interested in data science and want to get started in this field, you first need to understand exactly what it is. There is a combination of hard & soft skills that you might need in the right proportion.

Hard Skills

Hard skills include specific technical knowledge and learnable skills. Hard skills are easy to quantify and are likely to be tested in interviews. Here are some of the hard skills every company looks for in a data analyst.

• Structured Query Language (SQL): This programming language enables data analysts to read, write, organize, and analyze data in relational databases. This is a basic skill required of an experienced data analyst. Most data companies have at least one SQL specialist. Many job listings list SQL as one of their basic requirements. Common database systems that use SQL are MySQL, MS Access, and Oracle.  
• Microsoft Excel: Knowledge of MS Excel means more than just basic spreadsheet skills. Advanced knowledge of MS Excel methods such as macros and his VBA lookup is required. They are useful for handling small data sets and quick analysis. MS Excel is especially popular with small businesses and start-ups.
• Programming Languages: R and Python are the most commonly used statistical languages. They allow you to analyze large data sets quickly and easily. They’re also used in predictive and advanced analytics. You need to master at least one of these programming languages to be considered a strong contender for a data analyst position. Several certification courses can help you master these languages. Certifications also improve your resume and demonstrate your commitment to prospective employers.
• Data visualization: Not everyone in your organization can understand complex data patterns and information. Data analysts need to transform complex data into understandable formats. A data analyst’s job is to draw conclusions from data and present them using visually appealing charts, tables, and graphs.
• Data cleansing: Data cleansing is an important part of a data analyst’s job. Data is obtained from various sources and prepared for analysis. Data may be formatted differently or contain errors, missing fields, or inaccuracies. Before we can do any useful analysis, we need to fix the data.
• Statistical Knowledge: Statistics are very important in analyzing and interpreting data. A background in statistics and knowledge of important mathematical principles can set you apart. You should be familiar with clustering, MapReduce technology, unstructured data concepts, and association rules.

Soft Skills

Unlike hard skills, soft skills are subjective and more difficult to quantify. Soft skills or interpersonal skills (also called “people skills”) deal with the way you relate to and interact with other people. Along with the technical skills listed above, you must possess certain soft skills if you want to succeed as a data analyst:

• Communication: Digging into data and making insightful findings is useless if you can’t communicate your findings in a way that the rest of your team can understand. Data analyst’s goal is to help business leaders use the power of data to make informed business decisions. To achieve this, data analysts must possess excellent oral and written skills. You should also be familiar with data visualization tools to effectively tell a cohesive story.
• Presentation skills: Like communication skills, presenting findings is an important part of a data analyst’s job. Good presentation skills will help you sell your vision to your company’s stakeholders and help them understand your point of view. 
• Critical thinking: To become a data analyst, you have to start thinking like that. To get the right information, you have to ask the right questions. The results may not be obvious. It requires critical thinking. 
• Problem Solving: Throughout your role as a data analyst, you would constantly encounter errors, bugs, and roadblocks. For this reason, proper problem-solving is important. You need to think quickly and be innovative in your approach. Since this is an essential skill, recruiters usually ask problem-solving questions during interviews. You may need an example of when you had to solve the problem in the past. Or you can give it a simple task to solve in real-time. 
• Collaboration: To be an effective data analyst, you need to collaborate with other professionals. Work with engineers, web developers, and data scientists to reach your goals. You should be comfortable working with internal and external stakeholders. To work in harmony with your colleagues, you must respect and appreciate them.

We at Finlytyx are committed to data science and emerging technologies. Building a centre of excellence around data science is why we came up with the idea of incorporating Finlytyx AI Labs Pvt Ltd. Lot of talented people just need a small support and right guidance to achieve their dream. Wait to hear more on this from us soon !!!!

Author

Madhu M.Nair
Head of Service Delivery – Finlytyx AI Labs Pvt Ltd