Must-have Skills to Kickstart Your Data Analytics Career


Data-driven business strategies are extremely valuable today and most companies are realizing that. As such, they require skilled professionals who can provide insight from the constant information being collected. Research shows that around 70% of executives will prefer candidates with data skills for jobs by 2021. As the digitization of the world continues, the demand for data analysts only continues to grow. 

If you are thinking about making a career transition to get into data analytics, you might not know where to start. You may want to look at data-driven job postings to get an idea of things. However, a data analyst plays a nuanced role. This makes it hard to find out the skills that are worth investing your time and money in. Given below are the top data analytics skills you must have to stay competitive in the field. 

1. SQL

Structured Query Language, or SQL, is a database language that is considered to be the industry standard. It is perhaps the most important skill that a data analyst should know. SQL has an advantage over Excel because it allows the handling of large datasets that are just not possible with Excel. 

Almost all organizations require professionals who are proficient in SQL, whether for storing and managing data, relating multiple databases, or changing or building the database structure altogether. You can consistently find job postings that require SQL skills and someone with advanced SQL skills can even earn a lucrative salary. Even someone from a non-technical background can learn this tool. If you want to work with Big Data, the first step involves learning SQL. 

2. Excel

Microsoft Excel is typically associated with spreadsheets more than anything else. However, this tool is quite powerful when it comes to analysis. Of course, a programming language like Python or R is preferred for handling large data sets. However, for smaller, lighter, and quick analytics, advanced Excel methods like using VBA lookups and writing Macros are still used widely. 

If you are working at a startup or lean company, you may have to use Excel for handling the initial database. The tool continues to be the mainstay through the years for businesses in all industries, so you should learn it. You can find several free resources to learn this tool and if you want a deeper understanding, you can opt for structured data analytics classes as well. 

3. Statistical Programming with Python or R

Python and R can be used to do what Excel does at a larger scale and a much faster speed. These are two of the most powerful languages for statistical programming and they can be used for performing predictive analytics and advanced analyses on big data sets. Both these languages are considered the industry standard. So, when you are looking for data analytics courses in Bangalore, make sure it covers R or Python comprehensively. You need to learn more than SQL if you want to become a data analyst and learn either R or Python. 

You may wonder which of the two programming languages you should learn. Both Python and R are free and open source. Employers usually do not care which language their employees use as long as they provide accurate analysis. Since R was made particularly for analytics, some analysts do prefer it over Python to perform ad-hoc analysis and explore data sets. 

4. Critical Thinking

You need to ask the right questions if you want to get the right answers from data, which can be particularly tricky. Thinking like an analyst is the key to achieving success as a data analyst. Sometimes, the connections are not clear and the data analyst plays the role of uncovering them and synthesizing them. To an extent, this is an innate ability but can still improve your critical thinking skills. You may want to keep asking the basic questions regarding the problem while finding the solution as it will prevent you from getting carried away and coming up with a more complex solution than what’s required. You also need to avoid relying on what exists already and try thinking for yourself. 

5. Machine Learning

Two of the most popular topics in data science are predictive analytics and artificial intelligence. So, an analyst’s skillset must naturally include an understanding of machine learning. Not all analysts are required to work in machine learning but it is important to know the concepts and tools if you want to stay ahead of others. However, if you want to advance in this field, your statistical programming skills need to be good first. 

6. Data Visualization

If you want to keep the audience engaged and get your points across clearly, you need to be able to say something compelling about data. If it is not easy to identify your findings quickly, you will find it hard to get through to others as well. How much impact your data has is critically dependent on data visualization. A data analyst has to use eye-catching graphs and charts from presenting their findings concisely and clearly. An industry-standard tool for this purpose is the visualization software from Tableau. 

7. Presentation skills

Presentation skills go hand-in-hand with data visualization. However, these skills may not come naturally to you. Nerves can get the best of even the most seasoned presenters. Just like most things, you should begin by practicing and keep practicing until you get it right. You can use some time to get comfortable with presentations such as focusing on the audience instead of yourself and setting particular goals for your improvement. 

Developing your skills

A good way to kickstart your career is knowing the skills you will require to break into the field. Organizations are looking to hire professionals with these in-demand skills. If you improve your data analytics skills today, you will get more opportunities in the future. There are different ways of developing these skills. However, how you decide to hone your skills depends on your background, resources, time, willingness to commit, and goals.

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