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6 Steps to Becoming a Data Analyst

6 Steps to Becoming a Data Analyst

Who Is a Data Analyst?

Data analysts collect, analyze, and report valuable insights found in data. The daily duties of data analysts include:

  • Collecting data from a variety of sources.

  • Preparing the data for analysis (i.e. cleaning the data).

  • Performing an exploratory data survey.

  • Modeling and analyzing data.

  • Creating data visualizations and reports.

There is a lot of hype around this profession, but it’s hard to overestimate the role of data analysts in today’s data-driven organizations.  

For an even better understanding of a data analyst’s role, let’s list some activities that data analysts are likely to be tasked with when working on, say, a big project aimed at reducing customer churn. The data analyst might:

  • Use different data sources to collect information on customer dynamics over the last few years.

  • Clean the data, which among many other things includes fixing missing data, applying consistent formatting, and ensuring the compatibility of data collected from different sources (e.g. all measurement data is in the same units).

  • Detect anomalies in the data and explore possible explanations. For example, let’s imagine there was a huge customer outflow in March-April 2020. Was it due to COVID-19? Are there other possible explanations?

  • Analyze the data from different perspectives and with different tools. For example, the analyst may see if it makes sense to group churned customers in several groups to better understand why they left. Or they may look for factors that are leading indicators for customer churn. 

1. Get Really Good at Excel

So, as a data analyst, you should be very familiar with Excel. You should be able to collect data from spreadsheets and know when it can be more effective to do data analysis in Excel.


2. Learn SQL

If you want to learn how to work with data, consider learning Python, R, or Java after learning SQL. With Python and R, you can analyze your data for insights and visualize your findings to share them with others, and Java makes it easier to process big data.

3. Learn Statistics

Statistics is used in all kinds of science and business applications. Statistics gives us more accurate knowledge which helps us make better decisions.

Statistics can be used to explain things in a precise way. You can use it to understand and make conclusions about the group that you want to know more about. 

4. Dig Into Python

Python is another essential  tool for modern data analysts. Of course, data analysts don’t need the same programming skills as software engineers or developers. But they are expected to know how to clean data, explore and visualize it, and build simple machine learning models with Python. If you want to do data analysis, you should be familiar with Python’s popular data analysis and visualization packages. 

5. Discover Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

6. Power BI Tool 

Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data might be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses.


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