Begin with Python’s basics and progress to sophisticated Data Science applications. Covering essential programming skills to advanced data analysis, our course includes 18 modules designed for practical learning and application.
Are you ready to propel your Python skills into the stratosphere and beyond?
Get set to navigate the boundless expanse of Python programming with our comprehensive program that takes you on a celestial journey through its most dynamic facets.
Exclusively tailored to mold aspiring Data Scientists, our program with 18 meticulously crafted demos are your starship, navigating you through the pulsating heart of Python’s core functionalities and advanced Data Science topics.
Begin your journey in Python programming with us. We outline a comprehensive curriculum that guides you through the essential aspects of Python.
Our program introduces you to the best coding practices in Python. Armed with this knowledge, you’ll be equipped to navigate through Python programming with confidence and proficiency.
Dive into PyCharm, the integrated development environment (IDE) that will be your command center for Python programming. Learn to utilize its full suite of features for an efficient coding workflow.
Begin with the basics of Python data types and variables. This section covers everything from integers and strings to more complex structures like arrays and lists, forming the building blocks of Python programming.
Discover the versatility of lists, matrices, and dictionaries in Python. This module teaches you how to effectively create, modify, and access these fundamental data structures.
Gain a thorough understanding of For-loops in Python, a crucial tool for iterating over data. This includes practical exercises on nested loops, enhancing your coding efficiency and logic.
This section demystifies the use of If-Else statements, enabling you to control the flow of your Python programs with conditions, boolean expressions, and logical operators.
Explore the extensive library ecosystem in Python, focusing on Data Wrangling, Visualization, Machine Learning, NLP, and Deep Learning. Learn how these libraries are applied in Data Science to solve real-world problems.
Learn the essential techniques for loading data from various sources including CSV, TXT, Excel files, JSON, and SQL Databases. This module prepares you to handle data efficiently in your Data Science projects.
This module covers essential techniques in data wrangling, including handling missing values and efficiently accessing DataFrame rows using iloc and loc.
This section teaches you to create random datasets using the Numpy library, focusing on random integers, vectors, and distributions for practical applications.
Master data manipulation techniques in Python, essential for data analysis. Discover how to filter, sort, and group data, utilizing the Pandas library to refine and prepare datasets for analysis.
Unlock the power of descriptive statistics to interpret data sets. This module guides you through calculating mean, median, variance, standard deviation, and mode in Python, providing a foundational understanding of data analysis.
Explore and apply dataset merging and joining techniques in Python. Learn about different merge types, including inner join, left join, right join, and left-anti join, and how to use them in practical data science applications
Enhance your Python skill set by creating custom, user-defined functions (UDFs). This segment focuses on developing functions that streamline coding tasks, including the use of conditional statements for more complex data operations.
Enhance your NLP models with advanced text preparation techniques in Python. Learn how to clean and prepare text data efficiently, including tasks like lowercasing, character removal, deduplication, tokenization, stop word removal, and lemmatization using the NLTK library.
Master data visualization in Python to effectively communicate your findings. This module covers essential visualization techniques, including line plots, bar charts, histograms, and combination plots, utilizing the Matplotlib library.
Dive into comprehensive data sampling methods in Python. This two-part series covers both basic and advanced techniques such as random sampling, systematic sampling, cluster sampling, weighted sampling, and stratified sampling, integrated with user-defined functions for enhanced learning.
Gain practical expertise in A/B testing with a focus on results analysis. This two-part workshop covers everything from generating click data and understanding power analysis to performing advanced statistical tests. Learn to conduct a thorough A/B test analysis, including 2-Sample Z-Test calculations, and interpret results for both statistical and practical significance.
