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1.
Data viz in Power BI with DAX
The fundamentals of utilizing Power BI for data analysis and visualization are covered in this module. You will perform ETL operations and connect to multiple data sources after configuring Power BI Desktop. For efficient data presentation and analysis, learn how to model your data, generate interactive dashboards and reports, and make use of cutting-edge features like custom charts and AI-driven insights.
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2.
Data Retrieval and Processing using SSMS
In this SQL course with SSMS, basic ideas and useful skills for database administration and querying are covered. Database design, SQL syntax, joins, subqueries, data retrieval (select statements), data manipulation (insert, update, delete), writing SQL queries, optimizing database performance, and effectively managing databases using SSMS are among the topics covered.
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3.
Mathematics for Data Science
A thorough introduction to analytics and statistics designed for data science applications is given in this course. Descriptive statistics, probability theory, data visualization techniques, regression analysis, hypothesis testing, and classification strategies are among the topics covered. Practical data science applications are emphasized, with practical exercises utilizing well-known tools like Python.
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4.
Python for Data Science
In order to improve data processing, participants learn functional programming techniques like map, filter, and lambda expressions. The implementation of hashmaps for effective data storage and retrieval is also covered in the curriculum. In addition, students practice building generators and iterators to handle large datasets and become proficient in serialization for data persistence. The principles of object-oriented programming are combined to create modular, reusable code.
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5.
Advanced Data manipulation and EDA with Numpy and Pandas
Participants gain knowledge of sophisticated pandas techniques, such as reshaping, merging, and aggregating datasets. They also look at data visualization, statistical analysis, and handling missing data as EDA techniques. Students learn how to use NumPy and pandas for complex data manipulation and deriving insights from data through practical exercises.