- Instructor: admin
- Duration: 3 hours
Thinking about starting a boot camp or a new field and unsure of your background or level of technical skills? We’ve got you covered.
Join our Data Analysis with Python Mini Bootcamp where you’ll learn the necessary prerequisite skills while learning basic data science. This boot camp is for non-coders who are interested in learning data science or those who have a technical background although are not familiar with the fundamentals of data science.
Data Analysis with Python Mini Bootcamp will teach you the fundamental skills of Python, SQL, Statistics and an introduction to machine learning. With those skills, you will be ready to take any kind of data science boot-camp or you’ll be ready to start learning data science on your own. During our prep course you will explore a hands-on experience that will ignite your enthusiasm and confidence to learn data science and its’ theory.
12 hours , 3hours per week.
All Zoom lectures are monitored by trained staff.
Arafat is a graduate student from Tel Aviv University and then continued his education at Hamburg and Colorado. Now, he is working as a Data Scientist at Stanford University. He has 25 years of data experience and teaching. Arafat Mokhtar is a Business Intelligence Engineer at Stanford School of Medicine, who develop Python code for data collections, validation, cleansing, and analytics to provide actionable data insights. Arafat holds a Ph.D. in Physics. His work expands into fields of research, finance, and operation. Arafat has several years of Python, R, SQL, and Tableau work experience.
Students will be awarded different certifications in each batch.
Adults
– Week 1 :
Review Python common functionalities and data structures used in data science Learn the important Python libraries in data science (Pandas, Numpy, Matplotlib)
– Week 2:
Read and write data from/to different formats (excel, csv, text, etc.) Cleanse and select important records from Dataframes Deal with missing data: identify, replace, and eliminate records Sort Dataframes by multiple columns.
– Week 3:
Leverage the functions apply, lambda, filter, and map Merge/Join Dataframes by foreign keys Learn pivot tables in Pandas.
– Week 4:
Learn data visualizations with the libraries Matplotlib and Seaborn Introduction to the Machine Learning library Sklearn Apply linear and logistic regression with Sklearn
500$
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