Introduction to python as a data analytics tool

Peter Maina — Thu, 10 Oct 2024 09:06:26 +0000

Python is one of the most popular programming language for data analysis, because of its simplicity and flexibility. It also has a lot of frameworks and libraries designed to handle analysis and visualization of data. Its friendly to both beginners and experienced analysts as it offers a wide range of tools that optimizes and streamlines their workflows.

Why python for data analytics?
Easy to learn. Python is very easy for beginners since its syntax is clear and readable. It helps analysts to focus more on problem solving rather than trying to understand complex syntax.

Data manipulation and cleaning. Pandas is a powerful Python library tool for cleaning and manipulating data into useful and insightful data. These tasks include handling missing values, filtering data, creating new features , and merging datasets.

Data visualization. Libraries such as matplotlib and seaborn has a range of plots and charts for data visualization, helping analysts to communicate insights visually. These libraries can generate bar plots, line charts, heatmaps, and many more.

Integration with databases. Python is able to integrate with a wide range of databases such as SQL, Mongo dB, and SQLite which makes it easier to extract, manipulate, and analyze data.

Basic workflow of data analytics with python.
Data collection. Involves extracting data from various sources such as excel, databases, APIs, and scrapping the web.

Data cleaning. Handling missing data, correcting data types, removing duplicates, and filtering irrelevant data.

Exploratory Data Analysis. Analyzing data and generating visualizations to identify patterns and to draw insights.

Visualization and reporting. Visualization tools and libraries are used to create dashboards, plots and reports.

Conclusion.
Python is a powerful and flexible tool for data analytics. Its ecosystem provides everything from basic data manipulation and cleaning to advanced machine learning. Python libraries offer solutions for analyzing and visualizing data while working with both small and large datasets.

AN ULTIMATE GUIDE TO DATA ANALYTICS, TECHNIQUES AND TOOLS

Peter Maina — Tue, 06 Aug 2024 14:12:41 +0000

An ultimate guide to Data analytics, techniques and Tools

Data analytics is the process of analyzing raw data in order to draw meaningful and useful insights, which are used by businesses to make informed and smart business decisions. It helps to predict future trends and behaviors instead of basing your strategies and decisions on guess work, thus making informed decisions according to what the data is telling you.

There are four key types of data analytics: predictive, descriptive, diagnostic, and prescriptive. Each of them helps the organization to make informed decisions and they tells us the following:
 Descriptive analytics tells us what happened in the past.
 Diagnostic analytics tells us why something happened.
 Predictive analytics tells us what will likely happen in the future.
 Prescriptive analytics tells us how to act and decisions that should happen.
The engineers who work with data explores these four areas using data analysis process, which includes identifying the question, collecting raw data, cleaning data, analyzing data, and interpreting the results.
Below are some of the data analytics techniques used by data analysts.
 Regression analysis which is used to estimate the relationship between a set of variables.
 Factor analysis helps the data analysts to identify the underlying variables that drives people’s behavior and the choices they make.
 Cluster analysis helps in identifying structures within a dataset.
 Time-series analysis helps in measuring the same variable at different points in time
Now lets us look at some of the tools which data analytics engineers might work with.
 Microsoft Excel
 Tableau
 SAS
 RapidMiner
 PowerBI

DEV Community: Peter Maina

Introduction to python as a data analytics tool

AN ULTIMATE GUIDE TO DATA ANALYTICS, TECHNIQUES AND TOOLS