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 January 19, 2021 - February 18, 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 Only Data Science with Python Training course Schedule

• January 19, 2021 - February 18, 2021 US Pacific time
• 20 Hours | 2 Hours on Tuesday, 2 Hours on Thursday every week US Pacific time
• 7:30 AM - 9:30 AM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on January 19, 2021 at 7.30 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