4 Weekends Data Science Training is being delivered as Instructor-led, guided training with Real-life, Practical Hands-On Lab exercises from April 4, 2020 - April 26, 2020 for 16 hours over 4 weekends, 8 sessions, 2 sessions per week, 2 hours per session.

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

4 WEEKENDS Data Science TRAINING SCHEDULE

• April 4 - April 26 , 2020 US Pacific time
• 4 Weekends | Saturday, Sunday every weekend
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your location and find your local date and time for the 1st Session

FEATURES AND BENEFITS

• 4 weekends, 8 sessions, 16 hours of total Instructor-led and guided, Practical Hands-On 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

Data Science Training Course Pre-requisite Skills

It is not required but preferred that you have some basic understanding of:

• Mathematics

• Statistics

• Any Programming Language

Who should take this this Course?

• Any IT Professional interested in enhancing or building their career in in the field of Data Science or becoming Data Scientist.
• Any Working Professional.
• Data Science Enthusiasts.

Data Science Training Course Objectives

After completion of the Data Science Course, you will have the following knowledge:

• Explore the data science process

• Probability and statistics in data science

• Data exploration and visualization

• Data ingestion, cleansing, and transformation

• Introduction to machine learning

• The hands-on elements of this course leverage a combination of R, Python, and Machine Learning

Data Science Training Course Outline

1. Introduction to Data Science
2. Data Science Deep Dive
3. Data Manipulation
4. Data Import Techniques
5. Exploratory Data Analysis
6. Data Visualization
7. Statistics
8. Statistics basics
9. Introduction to Machine Learning
10. Understanding Supervised and Unsupervised Learning Techniques
11. Clustering
12. Implementing Association rule mining
13. Understanding Process flow of Supervised Learning Techniques
14. Decision Tree Classifier
15. Random Forest Classifier
16. What is Random Forests
17. Naive Bayes Classifier.
18. Problem Statement and Analysis
19. Linear Regression
20. Logistic Regression
21. Text Mining
22. Sentimental Analysis
23. Support Vector Machines
24. Deep Learning
25. Time Series Analysis
26. Data Preprocessing
27. Linear And Logistic Regression Models.
28. K-means and Hierarchical Clustering.
29. Natural Language Processing.
30. Artificial Neural Networks.
31. Convolutional Neural Network.