Data Analysis for Beginners: How To Get Started

Data analysis is often perceived as a complex process, but it doesn’t have to be.

As a beginner, it can be daunting trying to figure out what to do with your data or where to start your analysis. Yet, the ability to unearth valuable insights from a dataset is a powerful skill that can greatly impact your decision-making process in both professional and personal contexts.

In this blog, I’ve outlined simple steps to get insights from your data that in turn will help you make better data-driven decisions in your life.

Step 1: Define Your Goals

Before diving into data, pause and articulate what you want to achieve. Goals can range from understanding customer demographics for a marketing campaign to optimizing your daily routine for productivity.

Ask yourself: What do you want to learn from the data? Are you trying to improve a business process, understand customer behavior, or track personal finance? Defining your goals will guide your analysis and help you stay focused.

Be specific and realistic with your goals to ensure they guide your analysis effectively. For instance, instead of a broad goal like “save more money,” aim for a more precise goal like “save 20k by the end of the year”.

If this is your first data analysis project, I would recommend picking a question you are actually interested in knowing the answer for so you are more motivated to understand the insights from the data.

Step 2: Determine How You Want to Measure Your Goals

Every goal needs a metric for measurement. These are your Key Performance Indicators (KPIs) which tell you if you are making progress towards your goals.

For example, If your goal is to save money, one of your KPIs would be how much money you spend on non-essential items. If you’re focusing on fitness, one of your KPIs might be to track the number of steps you take each day or your workout duration.

Whichever KPI you choose, it’s important you make sure your metrics are aligned with your goals and can be accurately measured with the data you have.

Step 3: Collect Your Data

Gathering data is the next step. Think of data as the building blocks of your analysis. that will help you understand your progress.

For budgeting, this could be bank statements or receipts. For a fitness goal, it might be data from a fitness app or a spreadsheet where you log your daily activities.

There is no set rule for how you should collect data but it’s important to focus on quality over quantity. More data isn’t always better; it’s about having the right data that is relevant to your goals.

Step 4: Clean the Data

Spend time tidying your dataset: remove irrelevant entries, standardize formats (e.g. date and time), and handle missing or outlier values. This step can be tedious but is essential for reliable results.

An example of this might be categorizing expenses into groups like ‘groceries’, ‘dining out’, and ‘entertainment’ for easier analysis, rather than looking at individual transactions. If your using fitness data generated from your phone or apps, this might involed ensuring all your workouts are logged in the same format.

Step 5: Decide What Type of Analysis to Perform

Once you have accumalated enough good quality data, choose what type of analysis you want to do.

There are 4 main types of analysis you could do:

A. Descriptive Analytics: What happened?

If you want to know how much you spent last month, you would simply add up all your expenses.

B. Diagnostic analytics: Why did it happen?

If you notice you spent more than usual, you might look at each category to see where you spent the most. This could help identify why your expenses increased.

C. Predictive analytics: What will happen?

Based on your spending trends, you might predict how much you’ll spend next month and adjust your budget accordingly.

D. Prescriptive analytics: How can we make it happen?

If you’re looking to save more, prescriptive analytics might involve creating a new budget based on your spending patterns to help you reach your savings goal.

If you are a complete beginner, the descriptive and diagnostic analysis of you data should be more than enough to understand your progress. There are plenty of beginner-friendly ways of doing this which I have outlined here.

It’s important to interpret the results in the context of your objectives and consider external factors that might have influenced the outcomes.

This step may lead you to refine your goals or data collection methods for future analyses.

Step 6: Visualize The Data

A picture is worth a thousand words, and the same goes for data visualization.

Create simple graphs or charts that make it easy to see your progress. You could use a line graph to track your daily steps over the month or a pie chart to visualize how you’re distributing your spending across different categories. Tools like Excel and Tableau Public offer great flexibility in creating interactive and engaging visualizations.

Keep it simple and focused, and as your confidence grows, you can explore more complex analysis methods.

By following these steps, you can approach data analysis with confidence. This process is iterative, so don’t be discouraged by initial setbacks. Each analysis is an opportunity to learn and refine your approach.

Remember, the goal is to gain insights that inform decisions – not to get bogged down by the process. Start simple, learn from each step, and gradually build your analytical skills.