Learn the fundamental programming skills of a Data Analyst with this beginner-friendly learning path. Use Python libraries to clean, analyze, and visualize data. These 6 fun courses will help your kick-start your Data Analytics career.
Overview
Syllabus
- Python Libraries for Data Analysis
- Immerse yourself in the world of Python’s powerful libraries used for Data Analysis. This course covers the installation and operation of the numpy and pandas libraries. Delve into the practical application of these libraries with special focus on creating and manipulating numpy arrays and pandas dataframes.
- Descriptive and Inferential Statistics with Python
- Take a deep dive into the fascinating world of statistics using Python with this well-curated course. It focuses on the calculation and practical application of descriptive and inferential statistics using Python's pandas, numpy, and scipy libraries.
- Data Cleaning and Preprocessing Techniques
- Embarking on this course allows you to deeply understand and apply data cleaning and preprocessing techniques. It systematically covers the concepts of data cleaning, handling missing values, normalization, binning, encoding, and more, aiming to equip you with practical skills for preparing data for analysis or machine learning tasks.
- Advanced Preprocessing and Collecting Techniques
- This course delves deeper into advanced data preprocessing and collection techniques with an extensive focus on merging DataFrames, grouping, and sorting values. It aims to provide an in-depth comprehension of these techniques, preparing the learners to effectively manage, transform, and prepare complex datasets for analysis.
- Hypothesis Testing with Python
- Embark on your journey to mastering Hypothesis Testing with Python in this comprehensive course. It thoroughly covers how to conduct a variety of statistical tests, analyze and interpret results, enabling you to make data-driven decisions and inferences.
- Reporting and Visualization for Data Analysts
- This course transports you to the captivating world of Data Visualization and Reporting. It extensively familiarizes you with various techniques and tools for creating meaningful, interactive and visually appealing reports and dashboards using libraries like Matplotlib and Seaborn.
Courses
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Immerse yourself in the world of Python’s powerful libraries used for Data Analysis. This course covers the installation and operation of the numpy and pandas libraries. Delve into the practical application of these libraries with special focus on creating and manipulating numpy arrays and pandas dataframes.
-
Take a deep dive into the fascinating world of statistics using Python with this well-curated course. It focuses on the calculation and practical application of descriptive and inferential statistics using Python's pandas, numpy, and scipy libraries.
-
Embarking on this course allows you to deeply understand and apply data cleaning and preprocessing techniques. It systematically covers the concepts of data cleaning, handling missing values, normalization, binning, encoding, and more, aiming to equip you with practical skills for preparing data for analysis or machine learning tasks.
-
This course delves deeper into advanced data preprocessing and collection techniques with an extensive focus on merging DataFrames, grouping, and sorting values. It aims to provide an in-depth comprehension of these techniques, preparing the learners to effectively manage, transform, and prepare complex datasets for analysis.
-
Embark on your journey to mastering Hypothesis Testing with Python in this comprehensive course. It thoroughly covers how to conduct a variety of statistical tests, analyze and interpret results, enabling you to make data-driven decisions and inferences.
-
This course transports you to the captivating world of Data Visualization and Reporting. It extensively familiarizes you with various techniques and tools for creating meaningful, interactive and visually appealing reports and dashboards using libraries like Matplotlib and Seaborn.
