5 Weeks Only Data Science with Python Training Course Singapore
5 Weeks Only Data Science with Python Training course is being delivered from April 19, 2021 - May 19, 2021 US Pacific Time
5 Weeks Only Data Science with Python Training course is being delivered from April 19, 2021 - May 19, 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
5 Weeks Only Data Science with Python Programming Training Course Schedule
- April 19, 2021 - May 19, 2021 US Pacific time
- 5 Weeks | 2 Hours on Mondays, 2 Hours on Wednesdays every week US Pacific time
- 6:30 PM - 8:30 PM 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 April 19, 2021 at 6:30 PM 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
5 Weeks Only Data Science with Python Training course is being delivered from April 19, 2021 - May 19, 2021 US Pacific Time
5 Weeks Only Data Science with Python Training course is being delivered from April 19, 2021 - May 19, 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
5 Weeks Only Data Science with Python Programming Training Course Schedule
- April 19, 2021 - May 19, 2021 US Pacific time
- 5 Weeks | 2 Hours on Mondays, 2 Hours on Wednesdays every week US Pacific time
- 6:30 PM - 8:30 PM 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 April 19, 2021 at 6:30 PM 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