Unlocking Insights: A Journey in Learning Data Analytics
In this note, I will discuss my journey in learning about data, specifically Data Analytics and Data Science. Although the desire to learn about data has been with me since college, I only started my journey in October of last year due to campus busyness. As someone without an IT background, learning about data has been a challenge, but fortunately, I have a partner with whom I can discuss and ask for advice.
Let’s start with Data Analytics, which is where I began my data learning journey. Data is a collection of raw or unprocessed facts or events. In today’s fast-paced technological era, the need for data is increasing rapidly, and the amount of available data is also growing. This has led to the emergence of big data, which holds a lot of potential value for users. Therefore, the need for skills to extract this potential value from big data has also increased, leading to the emergence of data analytics.
Data analytics involves using exploration, analysis, and visualization to extract value from big data. The process of data analytics can be divided into several types of analysis, including descriptive, diagnostic, predictive, and prescriptive analysis. Problem-solving and analytical thinking are essential skills to master in data analytics. They enable us to observe and interpret data, develop ideas, and find solutions.
Mathematics and statistical skills are also critical when processing data. The most commonly learned programming languages in data analytics are R, Python, and SQL. These programming languages allow data analysts to manipulate data and apply statistical models to derive insights. Additionally, data analytics tools such as Python, R, Excel, Power BI, or Tableau are used to process data and derive insights.
However, it is not enough to merely manipulate and analyze data. The insights and solutions that can be obtained from the processed data need to be explained. This is why communication skills are essential in data analytics. Effective data visualizations should be used to ensure that the results of data processing can be easily understood. Data analysts must be skilled in visualizing data and presenting findings to non-technical stakeholders.
In conclusion, learning about data analytics and data science has been a rewarding journey for me, and there is still so much to learn. Here are some links that I read when starting to learn about data analytics, which may help others who want to learn about it as well:
