What to Learn for Data Analysis?
With the development of big data, there seems to be an increasing need for "data awareness" and "data analysis" skills, especially among large enterprises. In simple terms, data analysis allows businesses to gather insights from the vast amounts of data generated by their many customers.
This is in addition to traditional methods like surveys and interviews. Analyzing data can provide an understanding of customer feedback and product performance. Often, data analysis is more accurate and efficient, making it highly valued by both businesses and job seekers.
Many companies now prefer candidates with data analysis skills when hiring, and many employees have found that basic Excel skills are insufficient for their job requirements. Self-learning data analysis can significantly improve office efficiency, leading to more individuals taking up this skill.
Today, I will share what data analysis tools are worth using and learning in addition to the conventional Excel. The skills are categorized into three levels:
Beginner Level: MySQL/Oracle/PostgreSQL
SQL is a fundamental skill for data professionals, with a significance comparable to Excel. Data analysis professionals often refer to themselves as "SQL laborers" due to its essential role in their work. For non-data professionals, mastering SQL is often sufficient for their data analysis needs. SQL is relatively easy to learn and revolves around searching, retrieving, and manipulating data within databases. In essence, if you want to enter the field of data analysis, SQL is a necessary skill.
Intermediate Level: Tableau/Power BI
Tableau and PowerBI are indispensable for data analysis as visualization tools. Tableau is particularly easy to learn; by dragging and dropping processed data, it can generate beautiful visualizations. Data visualization is a clear and effective way to identify issues. It greatly enhances your performance in data analysis and presentations. This is suitable for individuals who have data analysis requirements and need to create reports.
Professional Level: Python
Python is another essential skill besides SQL, but it's more suitable for those who require advanced data analysis skills. Python is relatively easy to learn, and its syntax is similar to English, making it more accessible than Java or C++. Python is efficient for cleaning and processing data, and many data scientists use Python for coding models. Python also supports web scraping, game development, and more. It's a must-have tool for data analysts.
Professional Level: R Programming
While the use of R Programming has decreased in recent years with Python's widespread adoption, it is still an important tool, particularly for statistics and graphics programming. R is widely used by statisticians and data miners to develop statistical software and data analysis. It is also a free software. Its libraries are well-maintained, and you can often find answers to any issues you encounter within the libraries.
Professional Level: Apache Spark
Apache Spark is a big data analysis tool for processing large-scale data. It provides high-level APIs in Java, Scala, Python, and R, as well as an optimization engine supporting regular execution graphs. It is used to process and generate large datasets using parallel distributed algorithms on a cluster, similar to Hadoop MapReduce. However, Spark is 100 times faster than MapReduce. If you aim to become a data analyst in the field of big data, Spark is a necessary skill.
Professional Level: BigML
BigML is another widely used tool that provides an interactive platform for using machine learning algorithms. It offers standardized software for meeting industry needs using cloud computing. Companies can use BigML across various departments, such as sales forecasting, risk analysis, and product innovation. BigML focuses on predictive modeling and employs various machine learning algorithms like clustering, classification, time series forecasting, and more.
In Summary
In the ever-evolving landscape of data analysis, acquiring the right skills is not just an option; it's a necessity. As big data continues to reshape the business world, proficiency in data analysis has become a sought-after asset for both organizations and job seekers. With a spectrum of tools and skills available, the journey of mastering data analysis is divided into beginner, intermediate, and professional levels.
At the beginner level, SQL stands as the fundamental gateway to data analysis, allowing for efficient data retrieval and manipulation. Aspiring data professionals often start their journey as "SQL laborers," acquiring the essential skills for data extraction and management.
Moving to the intermediate level, tools like Tableau and Power BI emerge as invaluable assets. Data visualization takes center stage, enhancing the ability to uncover insights and communicate findings effectively. These tools empower analysts to create compelling reports and visualizations that drive informed decision-making.
For those seeking advanced data analysis skills, Python and R programming occupy a prominent place. Python's accessibility and versatility make it an ideal choice for cleaning and processing data, coding models, and supporting various applications beyond data analysis. R programming, though less commonly used than Python, remains essential for statistics and graphics programming.
To tackle the world of big data, professionals often turn to Apache Spark, a powerful tool for processing and analyzing vast datasets at unmatched speeds. Spark's capabilities outshine traditional methods like Hadoop MapReduce, making it indispensable for data analysts in the realm of big data.
Additionally, BigML offers an interactive platform for harnessing machine learning algorithms, catering to diverse industry needs, from sales forecasting to risk analysis. The predictive modeling capabilities provided by BigML empower organizations to leverage data-driven insights for business growth.
In the era of data, continuous learning and adaptation are the keys to staying ahead. As businesses and technologies evolve, so too must the skills of data analysts. Embracing these tools and skills at different proficiency levels ensures that data professionals remain at the forefront of this dynamic field, ready to unlock the potential hidden within the vast sea of data.
