4 Weeks Data Science Training is being delivered as Instructor-led, guided training with Real-life, Practical Hands-On Lab exercises from April 6, 2020 - April 29, 2020 for 16 hours over 4 weeks, 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 WEEKS Data Science TRAINING SCHEDULE
FEATURES AND BENEFITS
- 4 weeks, 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:
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Mathematics
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Statistics
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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 ObjectivesAfter completion of the Data Science Course, you will have the following knowledge:
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Explore the data science process
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Probability and statistics in data science
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Data exploration and visualization
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Data ingestion, cleansing, and transformation
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Introduction to machine learning
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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
- Statistics basics
- Introduction to Machine Learning
- Understanding Supervised and Unsupervised Learning Techniques
- Clustering
- 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.
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07/04/2020
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