The Data Analysis Process
Tags: dataanalysis, beginners, datascience
Published: 2022-08-11 15:35:07

Let's talk about the data analysis process. Oh wait, before diving into the process, we must understand what data analysis is. According to Wikipedia, "Data Analysis is the process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making." In simpler terms, Data Analysis is the process of extracting relevant information and insights from raw data to make helpful and informed decisions. From that definition, we see that data analysis is a process. Now, what is this process? How can we make good decisions and draw valuable insights from raw data? Hold on, we are getting there. But before that, let us make a quick detour to why data analysis is very important.

1. Data Analysis can be employed in different sectors to help them make more intelligent and informed decisions. For example, in the Finance sector, it helps in the areas of fraud detection, risk modelling, loan management, etc. In the Healthcare sector, It can be utilized to gather insightful information about the patients to enhance patient care. Additionally, it facilitates quicker diagnosis and more accurate decisions. In the entertainment industry, in addition to helping businesses in the sector make decisions that will help them increase their profits and service quality, it may be used to provide personalized recommendations to its users. In the retail industry, it can be used to predict consumer demands and increase satisfaction by gaining an understanding of their purchasing habits, trends, and patterns. This is just to name a few of the industries and identify how beneficial it is to just about any industry.

2. Data analysis can be used to identify the sectors in an organization or business that are most crucial to their ability to generate profits. By assisting with this, it helps the organization reduce operating expenses by allocating its financial resources to the areas that have a more significant beneficial influence on the business and investing less in technologies and activities that provide no value and have little to no impact on the company.

3. Data analysis assists businesses in knowing their target customers and understanding the best approach to reach out or promote to them by analyzing the market performance of their products and understanding the impact of each advertisement method and how effective it is in achieving its aim.

4. Data analysis can also assist in the resolution of problems that may occur in an organization by discovering abnormalities or malfunctions in the system.

Now that we have gone through the definition of Data Analysis and seen just how important it is to the success of different organizations and life, let us talk about the process used to come up with these valuable insights and information with raw data.

The data analysis process consists of five significant stages.
· Asking questions
· Data wrangling
· Exploratory data analysis
· Drawing conclusions
· Communicating your results

Don't be confused. These stages are actually not that hard. Let's go step by step.

**Asking Questions:** this is the step where you get familiar with the data you are working with and brainstorm possible questions you can find answers to. Let's take a movie data set containing features like the movie rating, the number of people who watched the movie, the success rate, and the budget used to make the movie, for example. We can ask questions like what features are most important to the movie's success rate, whether more people watch the higher-rated movies, and whether movies with a bigger budget have higher ratings and have a higher number of people watching them. In this stage, you might decide to ask your questions first, then look for an appropriate data set that can be used to answer your questions, or you can have the data set first and then use the features to generate questions.

**Data Wrangling:** okay, calm down. Wrangling might be a new word to you if you are a beginner, but that word might be the most complex thing in your data. Data wrangling entails three steps: gathering, assessing and cleaning your data.

In gathering your data, you get the necessary files for your analysis. This could involve downloading readily available files from the internet, extracting data from existing databases, or scrapping data from the web.

Assessing your data then helps you understand the structure of the data you have gathered and identify the possible problems associated with the structure or quality. In assessing your data, you can check the shape of the data, that is, the number of rows and columns, check if there are any duplicated rows, check for improper data type formats, missing values and even the descriptive statistics of your data.

In the cleaning stage, you solve whatever problems were found in your data during its assessment. This could involve changing the data types to their appropriate formats, renaming the columns, filling or dropping missing values, dropping unnecessary columns, and just putting your data in the proper structure.

**Exploratory Data Analysis:** the exploration stage. This is where you have fun with your data. Play with it. Try to understand it. Find the patterns present in your data, visualize the relationships between the features in the data using histograms, bar charts, boxplots, and the likes, and build intuition about the data. In this step, you could also create new features and remove outliers in your data.

**Drawing Conclusions:** this is where you bring out valuable insights from your exploration. You can also draw conclusions by using inferential statistics and machine learning.

**Communicate your Results:** After spending long hours or even days on your data, nobody can exactly understand what you did if your results aren't effectively communicated. This last step is crucial in the data analysis process. It entails telling people about your data, its importance, and the conclusions you were able to draw from it. A way to effectively communicate your findings is through data visualizations, and different ways to share your results include reports, blog posts, presentations and conversations.