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The course covers popular ML libraries and tools such as Scikit-Learn, TensorFlow, and PyTorch.

Utilize Power BI to connect, visualize, and customize data reports, enabling them to effectively analyze and communicate insights from diverse data sources.

Covers descriptive and inferential statistics, probability concepts, correlation and regression analysis, and applying statistics and probability to analytics projects.

You'll learn how to create APIs , containerize your machine learning models, and deploy them using MLOps.

Learn how to implement these algorithms, evaluate their performance, and apply them to real-world problems.

This course on supervised learning with Python covers the basics of machine learning, data preparation, and encoding categorical variables.

It covers topics such as data product management, the data product lifecycle, and defining data product strategy.

Familiarity with data management concepts and technologies, basic project management principles, and proficiency in Python, are required.

Practical techniques and methodologies to effectively execute the development, deployment, and continuous improvement of data-driven products.

The course aims to equip participants with practical knowledge and insights to effectively manage and lead data product initiatives.

Design and implement data governance and security measures that protect sensitive data, maintain data quality, and meet regulatory requirements.

The course aims to equip participants with the knowledge and skills to build, and optimize lakehouses, perform ETL processes using Spark

Develop technical skills in the high-growth field of advanced data analytics.

In this course, you will learn about exploratory data analysis techniques in Python, including EDA for data preparation.

The course covers topics such as normalization, denormalization, and advanced techniques for modeling complex relationships, temporal data, and schema evolution.

The course covers advanced SQL concepts, data manipulation and transformation, handling large datasets, advanced joins and set operations.

This program provide participants with a comprehensive understanding of data warehousing concepts and practices in the context of data engineering.

The course will provide an introduction to data consultancy, including the essential skills and tools required to succeed in this field.

The objective of this course is to provide learners with a comprehensive understanding of dimensionality reduction techniques using Python programming language.

This Excel for Data Analytics course covers advanced SQL skills in data analytics.

Learn how to manipulate and analyze large datasets using SQL, as well as how to integrate SQL with other tools like Excel and Power BI.

Leverage the power of automation for your data processing needs with our course

This course is designed to provide learners with a comprehensive understanding of statistical concepts and hypothesis testing using Python.

The Data Consultancy Toolkit course covers data-related topics and requires basic data science knowledge, programming familiarity, and participation.