This event has been UPDATED since it was first published. View the Complete, UPDATED & Detailed Data Science with Python Training Information here.

20 Hours Only Data Science with Python Training course is being delivered from May 4, 2021 - June 3, 2021 for 20 Hours over 5 weeks, 10 sessions, 2 sessions per week, 2 hours per session.

• All Published Ticket Prices are in US Dollars
• The course will be taught in English language

20 Hours Data Science with Python Training Course Schedule

• May 4, 2021 - June 3, 2021 US Pacific time
• 20 Hours | 2 Hours on Tuesday, 2 Hours on Thursday every week US Pacific time
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your location and check your local date and time for first session to be held on May 4, 2021 at 8:30 AM US Pacific Time.

Features and Benefits

• 20 Hours, 10 sessions, 5 weeks of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Actual code and scripts provided
• Real-life Scenarios

Course Prerequisites

Some background in python programming may be helpful.

Who can take this course?

• Software engineers and data analysts
• Business intelligence professionals
• Those aspiring for a career in data science
• Professionals and Students looking to enter the Data Science industry

Course Outline*

*The actual course content covered during the training may slightly vary based on the background and expectations of the students and instructor preference.

1. Descriptive Statistics

• Mean/Median
• Variance/ Std Deviation
• Quartile/Box Plot/IQR/Percentile
• Outlier
• Z Score
• Co-variance / Co-relation

2. Inferential Statistics and Exploratory Data Analysis

• Probability and Random Variables
• Population and Sample
• Normal Distribution, Binomial Distribution
• Testing of Hypothesis
• t Test / ANOVA/ Chi Square Test

3. Machine Learning Models

• Linear Regression
• Logistic Regression
• Naïve Bayes
• K Nearest Neighbors
• Decision Tree
• Support Vector Machine
• Ensemble Techniques

4. Advanced Machine Learning Techniques

• Model Evaluation
• Ridge Regression
• Lasso Regression
• Grid Search Cross validation

5. Unsupervised Learning

• Clustering
• PCA
• Apriori Algorithm

6. Time Series Forecasting

• Moving Average
• Exponential Moving Average
• ARIMA

Student Testimonials and Reviews

"I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science and common terms associated with the field (like machine learning). " - Julie, Data Analyst, Orlando ★★★★★

"Very good learning experience, I am a beginner in DS, but the instructors in this course simplified the contents that made me I could easily understand, tools and materials were very helpful to start with."-Tom, Data Engineer, Salt Lake City ★★★★★

" I liked this course very much. The way it is organized to collect different data scientists opinions about the same topic, make it very valuable to the ones who are starting the road to Data Science"- Omar, SQL Data Analyst, Charleston ★★★★★

"Very useful. I liked and enjoyed the journey of learning in these five weeks. the instructor is very clear and taught very interestingly. Thanks to him. he looked poised and cheerful and professional ."-Derrick, Renton★★★★★

" Terrific introduction to the Data Science course. Never expected but was extremely excited with the quality of content and a very honest attempt to making this course interesting" - Michelle, IT Professional, Philadelphia ★★★★★

" After completing this course you can easily understand and define what is Data Science and clear your doubt about Data Science. I recommend this course to all beginners" - Mark, IT Professional, Duluth ★★★★★