Bandung, December 27, 2023. The preparation period for college life is a challenging moment. From determining the dream university to choosing the right study program, these decisions will shape your future academic and professional journey. However, financial constraints are often an obstacle to getting a quality education. The very limited funding institutions for education make quality education only accessible to certain groups. Edulab, as one of the largest tutoring institutions in Indonesia, took the initiative to establish a strategic partnership with Danacita (PT Inclusive Finance Group) to expand access to educational funding to the community to increase accessibility and financial inclusiveness for various groups of students. In this case, Edulab, which already has more than 10 thousand students throughout Indonesia, wants to increase its capacity to 20 thousand students by the end of 2024.
Danacita as a strategic partner
will help in facilitating education costs to study at Edulab. Founder and CEO of Edulab Indonesia, Oki Earlivan, said that in order to catch up on the gap in educational equality, financial inclusiveness in education must be enjoyed by people in regions and small cities. The same thing was also conveyed by the main director of Danacita, Alfonsus Wibowo. In addition, Oki added that the transformation of digital technology is inevitable and must be strengthened so that it can not only be accessed but also facilitate the process of submitting to disbursing education funds. Indonesia must catch up on the gap in higher education which is currently only enjoyed by 6.7% of the Indonesian people and to become a developed country at least 30% of the people must be able to enjoy higher education.
By signing the cooperation agreement between Edulab Indonesia and Danacita, Oki and Alfonsus hope that the equality of education will increase and this collaboration will become a momentum for accelerating quality education in Indonesia.
code. However, EDA is an important first step in understanding data. So, what exactly is EDA? Read on to learn more about EDA and recommendations for bootcamps that are suitable for beginners here!
What is EDA (Exploratory Data Analysis)?
EDA or Exploratory Data Analysis, is an important process in the world of data analysis. This is the first step that allows us to explore and understand Kenya Business Email List the data we have before starting a deeper analysis. In EDA, we perform a number of activities, such as creating visualizations, identifying patterns or trends in the data, and checking for missing or inconsistent data.
The goal is to get an overview of the data before we run statistical models or machine learning algorithms. In other words, EDA helps us find the hidden story in the data, helping us make better decisions, and extract valuable insights.
How to Implement EDA?
There are 3 techniques you can do to implement EDA. Here is the complete explanation:
1. Descriptive Statistics Techniques
This technique is an important first step in EDA (Exploratory Data Analysis). Descriptive statistics are used to provide a brief summary of your data. It includes measures such as mean, median (middle value), mode (most frequently occurring value), standard deviation (the spread of the data), quartiles, and range. This information provides an initial understanding of the distribution and characteristics of your data.
2. Univariate Analysis
Univariate analysis involves exploring data for one single variable at a time. It includes visualization and descriptive statistics for a single variable. Examples of univariate analysis include histograms (to see the distribution of data), box plots (to see outliers), and summary statistics for each variable.
3. Multivariate Analysis
Multivariate analysis is the stage where you explore the relationships between two or more variables in your dataset. It includes techniques such as correlation matrices (to see the correlation between pairs of variables), regression analysis (to understand the effect of one variable on another), and visualizations such as matrix scatter plots (to see simultaneous relationships between multiple variables).
These three techniques complement each other in the EDA process. Descriptive statistics provide an overview of the data, univariate analysis allows for an in-depth understanding of each variable separately, while multivariate analysis reveals how the variables interact with each other.
All of this helps you answer questions, identify patterns, and make decisions based on a better understanding of the dataset.
Why is EDA so Important for a Data Scientist and Data Analyst?
Exploratory Data Analysis (EDA) is one of the important stages in the data analysis process carried out by data scientists and data analysts. This is a very relevant and important initial step before conducting further analysis, statistical modeling, or decision making based on data. Here is an explanation of the importance of EDA for a Data Science and Data Analyst:
Uncovering Hidden Information:
EDA helps identify hidden patterns and insights in data.
Understanding Data Characteristics: Understanding data distribution and statistics helps in selecting the appropriate analysis method.
Anomaly Identification: Identifying outliers and anomalies helps ensure the accuracy of the analysis.
Measuring Correlation: Understanding the relationship between variables helps design effective analysis.
Understanding Data Limitations: Identifying ew 线索 missing values or incomplete data helps in overcoming data limitations.
Formulating the Right Questions: EDA helps formulate relevant questions and hypotheses for analysis.
Informed Decision Making: EDA results aid in evidence-based decision making.
Communication of Findings: Visualization of EDA facilitates communication with non-technical stakeholders.
Understanding Data Context: EDA helps understand the origin and purpose of data collection, strengthening the understanding of data context.
In the data analysis chain, EDA is a critical step that forms the basis for deeper understanding and better decision making. Without a solid EDA, the risk of misinterpreting data or designing analytical models can increase significantly.
Data Analyst Bootcamp Recommendations
Don’t miss the opportunity to take the first step in your journey to becoming a successful Data Analyst through a bootcamp. This bootcamp will help you master EDA and open the door to exciting career opportunities in the world of data.
Here are recommendations for the best bootcamps that you can join:
1. Pacmann
Pacmann is one of the highly recommended data analysis bootcamps to learn about Exploratory Data Analysis (EDA). They offer an intensive training program specifically designed to equip participants with the skills needed to dig into and analyze data thoroughly.
Pacmann is known for its comprehensive curriculum and experienced instructors in the field. They focus on teaching practical skills that are relevant in the real world, and participants will have the opportunity to work with actual data. With an interactive approach and practical projects, Pacmann helps participants understand EDA in depth.
2. Rakamin Academy
Rakamin Academy is another data analysis bootcamp option worth considering for learning Exploratory Data Analysis. They offer a structured program with a focus on applying EDA tools and techniques in a business and industry context.
Rakamin Academy is known for its project-focused learning approach, where participants will work on real-world cases to hone their EDA skills. Instructors at Rakamin Academy have extensive experience in data analysis, and they offer strong support to participants throughout their learning journey.
So, how interested are you in learning EDA and want to become a reliable Data Analysis? Come on, register now and use Danacita to help realize your dreams with cost relief!