As a data analytics and data science enthusiast you will be exposed to a lot of choices and advice in terms of acquiring the right skills. New tools and techniques are developed regularly and as a fast paced discipline data analytics demands constant adaptation from its practitioners. Nevertheless, there are certain skills that have stood the test of time. These will serve you well in your analytics venture. Let us see what they are.
Microsoft Excel
It may be hard to believe for someone from the outside but a large portion of a data analyst’s job can effectively be performed on Excel. You must remember that despite all the buzz and the popularity, only a small number of operations actually involve big data as we know it. Excel can handle up to a few hundred thousand rows of data. And you will find that a firm grasp over the advanced Excel functions serve you better than almost anything else.
The charts, pivot tables, and advanced formulas let you perform the entry level data analysis and helps you present a comprehensive picture of the data at hand. It is one of the most popular tools for small data analysis and presentation.
SQL
The structured query language or SQL is going to be your best friend on the job as a data analyst. It is what you use to fetch data from databases. It is useful for data cleansing and data preparation, which, of course, are a large part of your job as a data analytics professional. SQL is one of the must have skills for a data analyst.
Data visualization skills
As a data analytics professional your usual goal is to scoop insights out of large pools of data and to ensure that those insights can solve a certain business problem or aid in decision making. Your work finds success and completion through data visualization. The better you represent your findings through graphics, and charts, and other forms of visuals, the higher the possibility of it making a mark and pushing the authorities to some action.
Visualization skills are a great addition to the skill set of a data analyst and a business analyst. Tableau and Power BI are some commonly used tools for this.
Domain knowledge
This is something that is often undermined or ignored but holds immense value for a data analytics professional. The models you choose, the tools you use, and the data you work with, all depend on the business that you are serving. The position of your company in the market, its customer base, the products it is trying to sell, the competition, everything plays an important part in your job. You can come up with actionable insights only if you understand the domain.
It is important to study the business domain before working as a data analyst.
Programming skills
With ready to use analytical platforms and self service analytics capabilities doing rounds of the industry, the necessity of programming skills for data analytics professionals has dipped a little. However, if you want to be really good at your job, and nurture the ambition of becoming a data scientist some day, it is highly recommended that you learn a language like Python or R.
In fact, a lot of data analyst positions require coding skills but they do not require you to have in depth knowledge.
Machine learning
Machine learning had never been a part of a data analyst’s repertoire, well not until recently. The roles of data analysts, machine learning engineers, and data scientists have merged at many places. A startup that is looking for a data analyst would definitely appreciate someone with basic machine learning skills. Machine learning has become so vital for data analysis, that companies want to hire analysts with machine learning skills. You can gain a basic understanding of the concepts from a machine learning online course.
Storytelling and effective communication
A data analyst without great storytelling skills is set up for failure. Human beings care for numbers but those numbers make the best impact when included in a story. If you can explain what your numbers represent in a telltale manner, it has a better chance of ensuring action.
Communication skills play a huge part in an analyst’s success, it helps you bridge the gaps between the stakeholders, the managers, and the developers. ‘
Well, that is all for this article. It is not an exhaustive list, but we have covered some ground. We certainly hope it helps you.
