SQL Project
SQL is a fundamental skill for data analysts. It is a versatile language that can be used to perform a wide variety of data analysis tasks.
Hi, I'm GaLib. A Data Analyst.
I use my skills in data collection, cleaning, analysis, and visualization to help businesses make better decisions. I'm passionate about using data to solve problems and improve people's lives.
Excel Project
Excel is a powerful tool for data analysis. It has a variety of functions and features that can be used to perform a wide variety of data analysis tasks (like- Data cleaning, Data exploration, Data modeling, Data visualization etc.).
Python Project
Python is a popular programming language for data analysis. It has a variety of libraries and tools that are specifically designed for data analysis tasks, such as: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn
Power Bi project
Power BI is a business intelligence and data visualization tool that can be used for data analysis. It has a variety of features that allow users to connect to data sources, clean and transform data, create visualizations, and share insights with others.
Tableau Project
Tableau is a popular data visualization tool that can be used for data analysis. It has a drag-and-drop interface that makes it easy to create visualizations without any coding experience.
Data Science Project
Data science is the field of extracting knowledge from data. It involves using a variety of techniques, including statistics, machine learning, and artificial intelligence, to discover patterns and trends in data.
MY EXPERIENCES ON
The data analysis process is not always linear. You may need to go back and forth between steps as you learn more about the data and your questions. It is also important to be flexible and willing to try different approaches. There is no one-size-fits-all solution to data analysis.
SQL
SQL, or Structured Query Language, is a programming language designed for managing data in relational databases. It is the most common method of accessing data in databases today. SQL has a variety of functions that allow its users to read, manipulate, and change data.SQL is a fundamental skill for data analysts. It is a versatile language that can be used to perform a wide variety of data analysis tasks.
Excel
Excel is a powerful tool for data analysis. It has a variety of functions and features that can be used to perform a wide variety of data analysis tasks (like- Data cleaning, Data exploration, Data modeling, Data visualization etc.).Excel is a versatile tool that can be used for a variety of data analysis tasks. However, it is important to note that Excel is not a statistical software. If you need to perform complex statistical analysis, you will need to use a dedicated statistical software.
Python
Python is a popular programming language for data analysis. It has a variety of libraries and tools that are specifically designed for data analysis tasks, such as: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn.Python is a versatile language that can be used for a variety of data analysis tasks. It is a good choice for beginners because it is relatively easy to learn. However, it is also powerful enough to be used by experienced data scientists.
Power BI
Power BI is a business intelligence and data visualization tool that can be used for data analysis. It has a variety of features that allow users to connect to data sources, clean and transform data, create visualizations, and share insights with others.
Tableau
Tableau is a popular data visualization tool that can be used for data analysis. It has a drag-and-drop interface that makes it easy to create visualizations without any coding experience. Tableau also has a wide range of built-in features for data exploration, data cleaning, and data analysis.Tableau can be a valuable tool for data analysts because it allows them to quickly and easily explore, analyze, and visualize data. This can help data analysts to gain insights from data that would be difficult to obtain using other methods. Tableau can also help data analysts to communicate their findings to others in a clear and concise way.
Data Science
Data science is the field of extracting knowledge from data. It involves using a variety of techniques, including
statistics, machine learning, and artificial intelligence, to discover patterns and trends in data. Data scientists use
this knowledge to solve problems and make decisions.
A data science algorithm is a series of instructions that a computer can follow to analyze data and make predictions.
Data science algorithms are used in a variety of applications, such as fraud detection, medical diagnosis, and product
recommendations.
There are many different types of data science algorithms. Some of the most common types of algorithms include: Supervised learni, Unsupervised learnin, Reinforcement learning
Here are some examples of data science algorithms: Linear regression, Logistic regression, Decision trees, Support vector machines (SVMs), K-nearest neighbors (KNN), K-means clustering, Q-learning.