Data Analysis

Data analysis tools must be capable of performing the following tasks.

• Exploratory data analysis (EDA) is a statistical approach to analyzing datasets and uncovering significant insights, patterns, and relationships among variables. EDA encompasses the basic Initial Data Analysis (IDA) processes, such as checking model assumptions, addressing missing values, and preparing data for hypothesis testing. It uses charts such as histograms, scatter plots, and box plots, alongside summary statistics, for visualization. EDA also provides insights into data trends and informs feature selection, model optimization, and decision-making.

• Data visualization is the art of presenting information and data insights in a graphical form, such as bar charts, line charts, scatter plots, or heatmaps. It highlights trends, relationships, and anomalies using visual elements to enable informed decision making.

• Reports are structured documents that summarize and present data insights to help businesses make informed decisions. Reports help businesses identify trends, patterns, and key metrics that guide strategies and operations. Businesses use standardized report formats in PowerPoint or Excel for consistency and reuse. Reports include charts, graphs, and tables to make complex data easier to understand. When linked to dashboards or data, they provide real-time updates, making them an essential tool for clear communication and effective decision-making.

Note

The upcoming sections will cover EDA, data visualization, and reports in greater detail.

Popular Data Analysis technology and tools are,

• MS Excel
• MS Power BI
• Tableau
• Infoveave
• SQL
• Python
• R

SQL

Structured Query Language (SQL) is a programming language used to store, retrieve, and manipulate data in relational databases. It allows end-users to communicate with databases and perform tasks like creating, updating, and deleting databases. SQL acts as a foundational tool across various stages of the data lifecycle between raw data and actionable insights.

In data engineering, SQL is used to extract, transform, and load (ETL) data across diverse systems. SQL also supports creating and managing relational databases and defining table schemas. It can handle deduplication, missing values, and data type corrections.

In machine learning, SQL plays a great role, starting from data retrieval and preparation to pulling and pre-processing data from databases using queries. It supports tasks like filtering, joining, aggregating data, and handling ng values. SQL is used to store both training data and models.

In data analysis, SQL supports querying, aggregating, and summarizing data to generate insights. SQL helps to uncover trends, correlations, and patterns by filtering, sorting, and grouping data. SQL is highly used to create data reports and dashboards with functions like SUM, AVG, and COUNT. SQL also supports the creation of star schemas and snowflake schemas by joining tables, which is fundamental to designing data warehouse architectures.

Python

Python is a versatile, high-level programming language known for its simplicity, readability, and rich ecosystem of libraries. It is widely used in DE, DA, and ML for tasks ranging from data manipulation to building complex predictive models.

In data engineering, Python supports data acquisition, data wrangling, and custom data transformation. The wide range of Python libraries supports data cleaning, transformation, and aggregation efficiently. It supports seamless database integration for querying and managing data through SQLAlchemy. Python supports workflow automation tools such as Apache Airflow to streamline pipeline management.

In data analysis, Python supports collecting data using libraries like pandas and BeautifulSoup and cleaning and processing it with libraries like pandas and numpy. It supports analyzing results using statistical libraries such as SciPy or Statsmodels and sharing insights through visualizations created with Matplotlib or Seaborn.

In machine learning, Python libraries such as scikit-learn are used for implementing various machine learning algorithms, ranging from regression to classification and clustering. Python supports TensorFlow and PyTorch frameworks for deep learning, enabling the development of neural networks for image recognition and natural language processing (NLP).