Data Analysis: How to get started

On June 20, Business Analysis School hosted a LinkedIn live event to guide beginners on how to get started in Data Analysis. Over a 100 attendees learnt a lot that night, questions were answered and opinions were suggested. 

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But if you missed the event, consider this blog post series a recap. We would explore -

• What is Data Analysis
• Types of data analysis
• How to get started in Data Analysis

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What is Data Analysis

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Data analysis involves the examination, cleaning, transformation, and modeling of data to discover useful information, draw conclusions, and support decision-making. 

For example, say you have a bunch of sales data for a store. 

Data analysis would involve examining that information to find out which products are selling the most, which ones are popular among certain groups of people, or if there are any specific factors that affect sales. 

By analyzing the data, you can make informed decisions about what products to stock, how to price them, and who to target as customers.

During the event , we discussed how there are basic and complex data analysis.And this leads us to the types.

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Types of Data Analysis

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Data analysis encompasses various techniques and methodologies, depending on the objectives and nature of the data being analyzed. Here are some common types of data analysis:

1. Descriptive analysis

Descriptive analysis is a fundamental type of data analysis that aims to summarize and present data in a meaningful and informative manner. By calculating various statistics, descriptive analysis provides insights into the characteristics and patterns within the data. 

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Here are a few examples of descriptive analysis techniques:

• Survey Results:

Imagine you conducted a survey among your classmates to gather their opinions on favorite leisure activities. 

After collecting the responses, you can use descriptive analysis to summarize the data. You could calculate the percentage of classmates who enjoy different activities like reading, playing sports, watching movies, or engaging in outdoor activities. This would give you an overall picture of the preferences within your class.

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• Sales Data:

Suppose you are managing a small online business and want to analyze your sales data. By applying descriptive analysis, you can calculate the average daily sales, the most frequently sold products, or the distribution of sales across different customer segments. This information can help you understand which products are popular, when sales are highest, and who your target audience is.

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• Exam Scores:

If you are a student, you can use descriptive analysis to understand your performance in different subjects. For example, you could calculate the average score, the range of scores, and the percentage of students who scored above a certain threshold. This analysis would provide insights into your strengths and weaknesses, helping you focus on areas that require improvement.

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2.  Diagnostic analysis 

Diagnostic analysis is a powerful technique you can use to understand the cause-and-effect relationships within a dataset. It goes beyond descriptive analysis and focuses on investigating patterns and trends to identify the factors that contribute to specific outcomes or behaviors. 

Here are some examples to make you understand better:

• Customer Churn:

Imagine you work for a telecommunications company and want to understand why some customers are leaving the company (churn). 

By conducting a diagnostic analysis, you can examine various factors such as customer demographics, service usage patterns, pricing, and customer satisfaction levels. By analyzing these factors and their relationship with churn, you may discover that customers with longer contract duration's and lower customer satisfaction are more likely to churn. This insight can help the company take proactive measures to improve customer satisfaction and reduce churn.

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• Product Performance:

Diagnostic analysis can also be applied to assess the performance of a product or service. Suppose you work for an e-commerce company and want to investigate why certain products have higher customer ratings than others. 

By analyzing customer reviews, product features, pricing, and delivery times, you can uncover patterns and identify the key drivers of customer satisfaction. This analysis may reveal that products with faster delivery times, competitive pricing, and positive customer feedback tend to receive higher ratings. Armed with this knowledge, you can optimize product offerings and marketing strategies to enhance overall customer satisfaction and sales.

Read also : Business Analysis vs. Data Analysis

3. Predictive analysis 

As it implies, predictive analysis uses historical data to build models and make predictions about future outcomes. 

This means applying machine learning to extract patterns and relationships from past data to forecast future trends. 

For instance, in sales forecasting, you can predict future sales based on factors like advertising expenditure, seasonality, and economic indicators.

Predictive analysis considers factors such as trends, seasonality, and cyclical patterns to make accurate predictions. It is widely used in finance, stock market analysis, demand forecasting, and weather prediction, among other fields.

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How to get started in Data Analysis

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1. Learn the Fundamentals: 

Begin by building a strong foundation in data analysis, mathematics, and programming. Understanding concepts such as probability, hypothesis testing, and data manipulation will be invaluable in your data analysis journey. 

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2. Acquire Data Analysis Tools: 

Familiarize yourself with popular data analysis tools and languages such as Excel, SQL, Google sheets, Python, R. These tools provide efficient ways to manipulate, visualize, and analyze data.

Read also : DATA ANALYSIS: 3 TOOLS EVERY BEGINNER SHOULD HAVE

Data visualization plays a crucial role in data analysis. So learn how to create meaningful visual representations of data using tools like Tableau. Effective visualization can help communicate insights and findings more effectively.

Stay Curious and Keep Learning: Data analysis is a rapidly evolving field, so it's essential to stay updated with the latest techniques, tools, and trends. 

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3. Gain Hands-on Experience: 

Practice is key when it comes to data analysis. Start by working on small projects or solving data-related problems like the examples listed above. 

Participate in online competitions, help friends organize and clean their data, contribute to open-source projects, or create your own datasets to analyze.Follow blogs, join data analysis communities, and participate in online courses or workshops

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Bottom Line 

Now that you’ve learnt the basics, enrol for a data analysis course and kick-start your career today.

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