4 Weekends Data Science Training in Newark | Introduction to Data Science for beginners | Getting started with Data Science | What is Data Science? Why Data Science? Data Science Training | April 4, 2020 - April 26, 2020
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:
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
- Introduction to Data Science
- Data Science Deep Dive
- Data Manipulation
- Data Import Techniques
- Exploratory Data Analysis
- Data Visualization
- Statistics basics
- Introduction to Machine Learning
- Understanding Supervised and Unsupervised Learning Techniques
- Implementing Association rule mining
- Understanding Process flow of Supervised Learning Techniques
- Decision Tree Classifier
- Random Forest Classifier
- What is Random Forests
- Naive Bayes Classifier.
- Problem Statement and Analysis
- Linear Regression
- Logistic Regression
- Text Mining
- Sentimental Analysis
- Support Vector Machines
- Deep Learning
- Time Series Analysis
- Data Preprocessing
- Linear And Logistic Regression Models.
- K-means and Hierarchical Clustering.
- Natural Language Processing.
- Artificial Neural Networks.
- Convolutional Neural Network.