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1.
Data viz in Power BI with DAX
Using Power BI for data analysis and visualization is covered in this module. It involves configuring Power BI Desktop, establishing connections to data sources, utilizing Power Query for ETL, and modeling data. Using resources like personalized charts and AI-driven insights, you will create dynamic dashboards and reports.
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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.
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6.
Machine Learning with Python
Essential algorithms such as decision trees, ensemble methods, SVMs, KNNs, clustering, linear and logistic regression, and ensemble methods are covered in this machine learning course. In addition to learning about model evaluation and hyperparameter tuning, participants also learn about theoretical underpinnings and practical application.
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7.
Natural Language Processing with Spacy
An extensive introduction to processing and analyzing text data is given in this course on Natural Language Processing (NLP) with spaCy. In order to perform tasks like tokenization, named entity recognition (NER), part-of-speech tagging (POS), dependency parsing, and sentiment analysis, participants learn how to use spaCy, a potent NLP library.
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8.
Time Series Analysis with Python
Time series analysis is the main topic of this Python course, which also covers preprocessing, visualization, and modeling using pandas, NumPy, and statsmodels. In addition to seasonal decomposition and anomaly detection, participants learn forecasting techniques such as ARIMA and SARIMA. Students obtain practical experience for real-world applications through projects.
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9.
Model Selection , Feature Engineering and Hyperparameter Tuning
The topics covered in this course include selecting, managing imbalanced data, hyperparameter tuning, and feature engineering. By preprocessing data, choosing pertinent features, and fine-tuning model parameters—especially for unbalanced datasets—participants learn how to maximize model performance. To address class imbalance, they investigate methods like resampling, ensemble approaches, and class weighting using Python libraries like scikit-learn.
