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Data Analyst in Python for beginners
Unleashing Data Insights: Mastering Python for Data Analysis
This Course Includes
udemy
4.2 (10 reviews )
2h 48m 
english
Online - Self Paced
professional certificate
Udemy
About Data Analyst in Python for beginners
The course is designed to equip students with the essential skills and knowledge required to become proficient data analysts using the Python programming language. This comprehensive course caters to individuals who aspire to work with data, derive meaningful insights, and make data-driven decisions. No prior experience in programming or data analysis is required. Throughout this course, students will learn the core concepts and techniques necessary for effective data analysis using Python. From data acquisition and cleaning to exploratory data analysis, statistical analysis, and data visualization, students will gain hands-on experience in performing a wide range of data analysis tasks using popular Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Course Objectives: Understand the fundamentals of data analysis and the role of a data analyst in various industries. Acquire proficiency in using Python programming language for data analysis tasks. Develop skills in acquiring, cleaning, and preprocessing data for analysis. Learn exploratory data analysis (EDA) techniques to uncover patterns, trends, and relationships in datasets. Apply statistical analysis techniques to draw meaningful conclusions and make data-driven decisions. Master the art of data visualization to effectively communicate insights and findings. Gain the practical Python skills hands-on experience in working with real-world datasets through practical exercises and projects.
What You Will Learn?
- Understand the fundamentals of data analysis and its role in decision-making. .
- Acquire proficiency in using Python programming language for data analysis tasks. .
- Learn to handle data acquisition from various sources and formats. .
- Master data cleaning techniques to ensure data quality and reliability. .
- Explore exploratory data analysis (EDA) techniques to uncover patterns, trends, and relationships in data. .
- Apply statistical analysis techniques to draw meaningful conclusions and make data-driven decisions. .
- Develop skills in data preprocessing and transformation for analysis purposes. .
- Gain proficiency in using popular Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn for data analysis. .
- Learn to create informative and visually appealing data visualizations using Python. .
- Understand the basics of machine learning and its applications in data analysis. .
- Develop proficiency in working with real-world datasets and solving data analysis problems. .
- Gain experience in presenting data analysis findings and insights effectively. Show moreShow less.