Introduction To Data Analysis

Welcome to the world of data! The world of data is super interesting; however, you may face the question of how and where to start. Breaking into the world of data is not a puzzle, so a simple and basic place to start is to develop a curiosity about data. Begin by asking yourself, “What questions or solutions can a set of data or information give me?”

As an aspiring data analyst or business analyst, my first advice to you is to feed your curiosity and ask a lot of how-to questions. There are a variety of tools used for data analysis, but your growth depends solely on your ability to provide answers that drive decision-making with the use of data. Your growth is not dependent on how many tools you can use for analysis.

What is Data Analysis?

Data analysis involves the practice of examining and interpreting data to gain valuable insights that support decision-making. A data analyst carries out the analysis of the data. A data analyst gathers, cleans, studies, and interprets a dataset to help solve problems. The expertise of a data analyst is required in many industries, including health, government sectors, business, finance, marketing, science, etc.

In different industries, data analysts are asked a variety of questions. In marketing, “What kind of customer should my business target in its next ad campaign?” In health/medicine, “What age groups are more vulnerable to this disease?” Your job as a data analyst is to ensure that these questions are properly answered with the help of data to ensure an effective decision-making process.

The process of analyzing data typically requires iterative phases, which include:

1. Identification of the industry and business question
2. Collection of data
3. Cleaning of data
4. Analysis of data
5. Interpretation of data

Data analysis can take different forms, and the form it takes depends on the question you are trying to answer as an analyst. However, all analytical processes revolve around the five major phases highlighted above.

• Identification of industry and business questions: Before the commencement of any analytical process, one must first identify the industry from which your data is derived. This will help you properly identify the terms and essential variables to include in your analysis.
• Collection of Data: This stage requires you to collect the raw dataset that will help you answer the questions identified by the client to aid their decision.
• Cleaning of Data: This is the most rigorous process in your data analysis process. It involves identifying and removing duplicates, inconsistencies, and syntax errors, as well as standardizing data structures and formats.
• Analysis of Data: This stage requires the use of different techniques and tools to identify trends, correlations, outliers, and variations that tell a story to aid decision-making. Data visualization tools are also used to convert the data into graphical formats for better understanding.
• Interpretation of Data: This is the final stage in the analytical process. This stage defines how well the result of your analysis answers the business question.

Tools Used by Data Analysts

- Microsoft Excel
- Google Sheets
- Python
- R
- Microsoft Power BI
- SQL

Types of Data Analysis

Data analysis is used to aid decision-making and provide answers to questions. To interpret data effectively, you should be familiar with the following analysis:

1. Descriptive Analysis: This type of analysis helps describe or summarize quantitative data by presenting statistics. An example of this is an analysis that shows the distribution of sales over a specific time frame.
2. Predictive Analysis: This type of analysis draws conclusions based on data gleaned from historic events. An example of this is the high sales of a specific type of product in a specific month and the poor sales in another month. This data is used to predict the month in which the product in question should be restocked or not.

Note: Whether you’re performing a descriptive analysis to summarize data or a predictive analysis to draw conclusions from past events, data analysis offers endless possibilities for exploration and growth in a wide range of industries.

Getting started with data might be uneasy. In fact, getting started with anything isn’t easy, but by embracing curiosity and asking the right questions, you can be an asset in the world of data. My final words to you are to equip yourself with diverse toolkits such as Microsoft Excel, Python, and SQL to enhance your analytical capabilities.

Welcome to the world of data, and do well to enjoy your learning process.