A data analyst is a person who employs the use of technical skills in finding meaning out of data and using that information to provide actionable insights.

Introduction

Whether you want to be a data scientist or interested in Big Data, data analysis gives you the foundation to proceed. The term data analysis has gained so much traction of late with the emergence of fourth industrial revolution (artificial intelligence, Internet of Things, machine learning, etc.), with insights from data becoming key in providing solutions to business and social problems.

 

Learning data analysis is not hard. However, because it is still not that old enough concept, there is no clearly set out guidelines on how one can study to be a data analyst. Most beginners (I included when I started out) tend to have confusion on where to start exactly. This article will not provide you with the skills or tools to start with, but will rather give you a guideline to help you achieve whatever your target is.

Data analysis comprises of a mixture of skills and tools. Whether it is SQL skills, programming skills or communication skills, you at least need to have basics of them to become a data analyst. There are different skillsets that cut across various domains that you will be required to have as a data analyst. They include the following six.

 

Key skills for a data analyst 

 

1. Data cleaning.

   Data cleaning accounts for up to 80% of most data analysis procedures. It involves preparing data for further analysis. You will need to retrieve data from its source, check out for any missing and duplicate data, and make it ready for categorical and numerical analysis. The skill is important as you will need it at the onset before you can make any good use of data.

2. Data exploration.

   Data exploration includes finding trends in data that could provide answers to questions you might have concerning the business or problem. You find relationships in different aspects of the data that will guide you in reaching to insightful conclusions.

3. Probability and statistics.

   A basic background in probability and statistics is essential in becoming a data analyst. This does not mean that you now have to enroll in university degree on statistics or related. All you need is to check out on simple options or beginner courses that can help you gain some knowledge on the same. Understanding concepts in probability and statistics will help you to understand the procedures and formulae involved in data analysis as well as get rid of logical errors that might arise.

4. Data visualization.

   Once you are through with analysis of data, you will want to present it to a team or concerned parties for actions to be taken. You will therefore have to understand the different methods of data visualization. Here, you will need to have a deeper understanding of your audience and the type of data you want to visualize. What you can present using a bar graph will not always make sense when done using a pie chart. The different options in visualizing data will always suit different situations.

5. Report writing and creation of dashboards.

   As you visualize data, you will need to explain the different aspects of it as well as provide recommendations and actionable insights. Report writing therefore becomes a key skill needed. How to create dashboards is also very essential. There are different tools that you can use in creating dashboards and you will definitely need to know how to use some of them.

6. Communication skills.

   Communication is one of the most ignored skills when one is learning to be a data analyst. You have to know how to communicate the results you get from your data analysis. Not everyone will understand the analytical terms and actions used unless you talk to them in a way they can understand. You will therefore need to master good speaking and listening skills to be valued as a good data analyst.

The above six skills form the basics of what you will need to have as good data analyst. In addition to the skills, there are various available tools that will help you to practically apply the skills. There are no specified tools that you should start with but at least there are those that will help you graduate from a beginner into intermediate and so on.

 

As a common practice, most people start with Python, R, Tableau, and advanced Excel, in addition to either of the SQL tools available. Based on that trend, it will also be wise if you can start with the same tools. They will give you insights and some sense of direction when proceeding with your studying of data analysis.

 

Now that you are aware of the basic skills and tools you need to get you started, how will you ensure that you learn them in the shortest and most convenient time possible? This article provides you with six steps that when used keenly will help you achieve your desired goals and become a good data analyst within a short time.

 

Tested and proven steps to becoming a data analyst fast

• Set goals.

  Before you get your feet in studying data analysis, you first need to set out goals you want to achieve in the process. The goals should be specific, measurable, achievable, realistic, and timely. Writing your goals down on a piece of paper or notebook will help you not to lose interest in the process as you keep on studying. You will also have a clear mind of what you want to achieve in the long term.

• List down the skills you want to gain.

  As discussed above, there are different skills that make up a data analyst. Whether you are a beginner or with intermediate skills, there are a number of skills you will want to have at the end. Listing them down will not only give you focus but also help you to know what you will have acquired upon completion of your learning.

• Identify tools you will need to use.

  Different skills will utilize different tools. One tool can also apply to a range of skills. What matters is the type of data you will want to handle and the kind of insights you will want to withdraw from it. Identifying the tools you need before starting out will help you have a clear sight of what you will learn and choose the method of learning about them early enough.

• Create your learning milestones.

  Learning milestones are like checks that will help you gauge your progress. Where do you want to be after studying for the next two weeks or so and how will you evaluate your progress? Setting milestones will help you know whether you are in the line of achieving your goals or not.

• Join a community

  Join a community of people with similar passion or data analysts. It is always fun when you learn together with other people. You can identify certain persons whom you will want to help you keep track of your learning progress, and even to encourage you when you seem to get tired along the way. Another option is to join an online community of persons with similar interests as you. You will be motivated by their progress as well as learn new trends in data analysis as you participate in discussions with them. You can find such online communities on various platforms. Join them and you will find learning data analysis enjoyable. I’ll recommend you to join the 97Club Data Science community where you will meet peers, get mentorship, and be helped to grow in the profession.

• Develop a data analysis projects portfolio.

  Once you have started gaining skills and now you can derive insights from data, it is good to start engaging in projects that will boost your practical grip on the skills. Start with simple ones as you advance. Showcasing the projects within an online portfolio (for example in GitHub) will help you grow professionally even as employers are looking for suitable data analysts to fill job vacancies. When it comes to data analysis, it is not about how many certificates you have but how you can apply the skills needed. Projects are therefore important in proving that you can apply what you have learned.

The above steps are proven and can help you become a data analyst within the time you intend and as well gain all the skills you want. Remember, knowing what to do is not enough until you apply it practically. Take action.