Master Data Science : Hands-On Data Science Bootcamp
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Online
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Methodology
Online
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Welcome! If you're interested in the exciting world of data science, but don't know where to start, then this is the beginning for you.Data Science course description:Hands-On Data science and Machine learning course designed to impart the training to understand the scientific techniques to extract meaning and insights from data. A data scientist requires skill sets spanning mathematics, statistics, machine learning and knowledge of data analytics software like Python, R and SAS. This course designed to introduce participant’s to this rapidly growing field and equip them with some of its basic principles and frequently used tools as well as its general mindset. Participants will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, machine learning exploratory data analysis, predictive modeling, descriptive modeling, Algorithm techniques, Linear algebra, evaluation, and effective communication. Emphasis placed on integration and synthesis of concepts and their application to solving real life problems. To make the learning contextual, case studies from a variety of disciplines used in this course.Machine learningTo automate analytical model building we use Machine learning. Machine learning is a field of research that enable computers to learn from data. ML uses to recognize objects in images, to identify meaning in text and trends in data – involving a variety of useful techniques that can be applied to big data.SoftwareIn the field data science Python, R and SAS are the three most popular languages. Let me explain you about these three languagesR - R is the common language of statistics. R is a free and open source programming language used to perform advanced data analysis tasks.
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About this course
Linear Regression, SVR, Decision Tree Regression, Random Forest Regression
Polynomial Regression
Logistic Regression in Python, R & SAS
K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification
Random Forest Classification
Clustering: K-Means, Hierarchical Clustering in Python , R & SAS
Data Visualization in Python with MatPlotLib and Seaborn
Dimensionality Reduction: PCA, PCA sklearn
Supervised Learning & Unsupervised Learning
Support Vector Machine
Curse of Dimensionality
Neural Networks
Learn R programming from scratch
Use of R Studio
Principles of programming
Concept of vectors in R
Create your own variable
Data types in R
Know the use of while() and for()
Build and use matrices in R
Use matrix() function, learn rbind() and cbind()
Install packages in R
Add your own functions into apply statements
Practice working with statistical data in R
Understand the Normal distribution
R functions
Create your own function
Hypothesis testing for mean
Multiple Linear Regression in R & SAS
Time Series Analysis in both R & SAS
Factor Analysis in Python , R & SAS
Decision Tree in R
Text Mining and Sentimental Analysis in R
Market Basket Analysis in R
Proc SQL
Create table using Proc SQL
Different types of joining using proc SQL
How to find duplicate records in SAS
How to use summary functions
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Subjects
- Probability
- Project
- Algebra
- Statistics
- Data analysis
- Installation
Course programme
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Master Data Science : Hands-On Data Science Bootcamp
