Beginner’s Guide: How to Become a Data Analyst

5 min read

Indeed.com reported that the growth rate for this profession had reached more than 4,000 percent. Data analysts are now wanted by most businesses. But how do I become a data analyst? This article shows you what skills are important for a data analyst and how to start from data itself.

Career Path for a Data Analyst

Data analytics is a fundamental part of every industry. As such, data analysts have a broad career path in different industries.

 source: PayScale

Industries that have high demand for data analysts

Market Research analyst

They conduct research to analyze the present market landscape. They collect consuming behaviors, buying habits, etc. Then they estimate the product demand to help companies optimize sales. The entry-level salary is $51,000 to $65,000.

Financial analyst

They work with financial data to provide models and forecasts. Investment industries, like banking investment, rely highly on data to explore investment opportunities. The entry-level salary is $54,700 to $69,000

Business analyst

They turn data into actionable business insights. It requires extensive skills in Excel, Power BI, and SQL. The entry-level salary is $52,700 to $66,000

What Skills Do a Data Analyst Need to Succeed?

Skills You Should Have for Data Analysis

  1. SQL: Structured Query Language is designed to access, manage, and manipulate the database. This is a basic requirement for a data analyst. 
  2. Excel: For lighter and quicker data analysis, advanced Excel skills like writing Macro and VBA lookup are required. 
  3. Statistical programming: R, MATLAB, and SAA are statistical languages to explore large datasets and display in fancy graphs for better understanding.
  4. Data visualization: the ability to present and describe the outcome is essential as well. Tools like Power BI and Tableau are considered standard analytics tools.

On top of all this, you need to create a data pool that enables you to conduct analysis.  

Web scraping can’t replace all analytical skills but complement them. Most of the time, data analysts need to cope with messy data unless you know a better way to locate and extract structured data. Luckily, there is a quick way to get started by using a web scraping tool like Octoparse. There are many other options as well.

Quick Example of Implementing Data Analysis

Let’s take an example to make sense of data analysis using web scraping, Excel, and Tableau together. The end goal here is to examine the relationship between GDP per capita of a country and its internet user growth rate.  

#Step 1: Data Extraction

To do this we need data from two data:

  1. GDP per capita (https://www.cia.gov/library/publications/the-world-factbook/) Note: The link is currently not available. You may choose another page to practice.
  2. Online user growth rate ( https://www.internetworldstats.com/top20.htm)

Then we need to set up a crawler using Octoparse for each website. I encourage you to watch this video and learn how to build a crawler if you haven’t tried it yet. 

Here is a preview of the complete workflow. Octoparse allows you to interact with the webpage and extract desired information via point-and-clicks. The workflow is visualized and you can edit it through drag-and-drop.

source: Octoparse

After you finish setting up the crawler, click the “start extraction” button. Octoparse will work on its magic and get the data for you. The best part is that the extracted data is structured. It means you just save yourself tons of time from cleaning the data as you would before knowing Octoparse. 

For this extraction, I did a little editing with built-in Regular Expression tool. Again, you don’t need to spend time writing Regular Expression Model with Javascript.

I scraped the data and put them into spreadsheets, welcome to practice with them. 

#Step 2: Excel Data Processing

Next, we need to use Index and Match formats to joint countries and corresponding values (internet user growth-rate and GDP per capita)  from two separate spreadsheets. 

INDEX (column to return a value from, MATCH (lookup value, column to lookup against, 0))

First, we need to use Match format to look up the “country” from Sheet 2 and return the position from sheet 2.

Then, we use the Index format to look up the “position” and return the corresponding value from Sheet 1

Data1 and Data 2 are the lookup ranges I named from Sheet1. This is because we are cross-referencing from two sheets. It’s easier to call up rather than typing the cell range.

With this formula, it will look up the Country position (DATA2) returned from the Match function, and return the corresponding value from GDP_per_capita (DATA1).  After you type in the format, drag the plus sign to the right corner of the cell.

#Step 3: Data Visualization

Once we finish matching the values, we will be able to visualize the data. Tableau is easy to pick up. You can just drag the desired values to the dashboard. It looks like the following chart.

#Step 4: Start Your Data Analysis 

There is a strong negative correlation between the internet growth rate % and GDP per capita for a country. That could mean the faster internet users increase,  the lower the GDP per capita. It makes sense as high-GDP countries are usually more developed with limited room to grow. Whereas, lower-GDP countries have the full potential to increase internet infrastructure. Thus the overall internet growth rate increases faster than advanced nations. 

In conclusion: If you plan to pursue a career in data analysis. you’d better plan out your career path as each industry has a unique definition of a job title. Next, honing the basic skills mentioned above. There are an abundance of free resources available online. In addition, web scraping can be a bright spot on your resume as it significantly increases the efficiency of data analysis as it saves you time from data collection and data cleaning. 

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