Launch your Python expedition with us. We set the trajectory for our cosmic voyage, outlining the plan that will guide us through Python cosmos specifically for your Data Science mission.
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.
Launch your Python expedition with us. We set the trajectory for our cosmic voyage, outlining the plan that will guide us through the Python cosmos.
Clear the cosmic fog around text data by mastering text cleaning and preparation techniques in Python, such as lowercasing, removing unnecessary characters, deduplication, tokenization, stop word removal, and lemmatization with NLTK Python library. required for any Natural Language Preprocessing (NLP) model.
Learn to picture the cosmos through data visualization techniques in Python, from line plots and bar charts to histograms and combination plots. Learn how to use Matplotlib Python Visualization library first hand.
Embark on the 2 part series of your star-probing mission: Data Sampling. Learn and Master simple as well as advanced data sampling techniques such as random sampling, systematic sampling, cluster sampling, weighted sampling, and stratified sampling in Python. We will combine these topics with Python UDFs and other topics for more enhance learning.
Decode cosmic messages with end-to-end A/B Test Results Analysis in 2-part session.
Generate your own click data, refresh power analysis, and understand the theoretical aspects of advanced statistical tests. Once we refresh the theory knowledge, In this hands-on programming session, we will conduct an end-to-end A/B Test results Analysis.
Perform the calculations for 2-Sample Z-Test including click probabilities, pooled variance, test statistics, P-value, and confidence interval. Interpret all the results and test the hypothesis for statistical and practical significance.