A Beginners Guide To The Data Analysis Process

Summary

This video script offers a comprehensive guide to the data analysis process, outlining five key stages: defining the question, collecting data, cleaning data, analyzing, and sharing results. It emphasizes the importance of understanding business objectives, using various data types, and employing tools like Databox and Tableau for analysis and visualization. The script highlights the crucial role of data cleaning and the analyst's responsibility to communicate findings clearly to influence business decisions.

Takeaways

• 🔍 The first step in the data analysis process is defining the objective, which involves formulating a hypothesis and determining how to test it.
• 🤔 Understanding the business and its goals is crucial for a data analyst to frame the problem correctly and identify the right data to solve the business problem.
• 📝 Data can be categorized into first, second, and third-party data, each with different sources and levels of relevance and reliability.
• 🛠 Tools like Databox, DashaRoo, Grafana, Freeboard, and Dashbuilder are useful for creating dashboards to visualize data at the beginning and end of the analysis process.
• 📈 After defining the objective, a strategy for collecting and aggregating the appropriate data is necessary, which includes determining the types of data needed.
• 🧼 Data cleaning is a critical step that involves removing errors, duplicates, outliers, and irrelevant observations to ensure high-quality data for analysis.
• 🕵️‍♂️ Data analysts spend a significant amount of time—up to 70 to 90%—cleaning data, emphasizing the importance of this step for accurate analysis.
• 📊 Various data analysis techniques exist, including univariate, bivariate, time series, and regression analysis, each serving different analytical goals.
• 📚 Descriptive, diagnostic, predictive, and prescriptive analyses are the four categories of data analysis, each providing different types of insights into the data.
• 🗣️ Effective communication of findings is essential, using reports, dashboards, and interactive visualizations to present data insights clearly and unambiguously.
• 🛠 Tools like Google Charts, Tableau, Datawrapper, and Infogram facilitate the sharing of data insights without requiring coding skills, while Python libraries like Plotty, Seaborn, and Matplotlib cater to those with programming knowledge.

Q & A

What are the five key stages of the data analysis process mentioned in the script?

The five key stages of the data analysis process are: 1) Defining the question, 2) Collecting the data, 3) Cleaning the data, 4) Analyzing the data, and 5) Sharing the results.

What is the importance of defining the objective in the data analysis process?

Defining the objective is crucial as it sets the direction for the entire analysis. It involves formulating a hypothesis and determining how to test it, which helps in framing the problem correctly and identifying the right data to solve the business problem at hand.

Can you provide an example of how a data analyst might reframe a business problem?

An example given in the script is when senior management asks, 'Why are we losing customers?' A data analyst might reframe this to 'Which factors are negatively impacting the customer experience?' or 'How can we boost customer retention while minimizing costs?'

What are the three categories of data sources mentioned in the script?

The three categories of data sources are first party, second party, and third-party data.

What is first party data and how is it typically collected?

First party data is data directly collected by the company from its customers. It often comes in a clear and structured form, such as transactional tracking data or information from a customer relationship management (CRM) system.

How does second party data differ from first party data?

Second party data is the first party data of other organizations. It is usually structured and can be obtained directly from the company or from a private marketplace, and while it is less relevant than first party data, it tends to be reliable.

What is third-party data and where can it be sourced from?

Third-party data is collected and aggregated from numerous sources by a third party. It often contains a lot of unstructured data or big data and can be sourced from industry reports, market research, open data repositories, government portals, or firms like Gartner.

Why is data cleaning considered a crucial step in the data analysis process?

Data cleaning is crucial because it ensures that the data is of high quality and free from errors, duplicates, or outliers. This step is important for accurate analysis and can prevent incorrect conclusions, which might lead to poor business decisions.

What percentage of time does a good data analyst typically spend on data cleaning?

A good data analyst typically spends about 70 to 90 percent of their time on data cleaning.

Can you explain the four categories of data analysis mentioned in the script?

The four categories of data analysis are: 1) Descriptive analysis, which identifies what has already happened, 2) Diagnostic analysis, which focuses on understanding why something has happened, 3) Predictive analysis, which identifies future trends by analyzing historical data, and 4) Prescriptive analysis, which allows for making recommendations for the future.

Why is it important for data analysts to present their findings clearly and unambiguously?

Clear and unambiguous presentation of findings is important because it influences the direction of the business. Decision makers rely on these insights for making strategic decisions, and honest communication ensures that conclusions are scientifically sound and based on facts.