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Mastering Data Science : Learn Hands-On

Mastering Data Science : Learn Hands-On

In the field data science Python, R and SAS are the three most popular languages. Let me explain you about these three languages


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 learning

To 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.


In the field data science Python, R and SAS are the three most popular languages. Let me explain you about these three languages

R - R is the common language of statistics. R is a free and open source programming language used to perform advanced data analysis tasks.
Python – Python is very powerful and multi-purpose language, free and open source programming language which has become very popular in data science due to its active community and data mining libraries.
SAS – SAS has been the global analytics leader in the enterprise analytics space. It offers a huge array of statistical functions. Easy to work with a good GUI for people to learn quickly and provides excellent technical support.
If you are looking to start a career in data science or to gain the skills to be able to transition to this field in the future. Then you are probably doing some research on which of these three programming languages you should learn first to maximize your chances of landing your dream job. Should you focus on mastering R? Or would be it better to make SAS a priority? Or should you learn Python?

This program will help to develop all the required skill to become a successful data scientist.

Basic knowledge
Basic knowledge in math and statistics

Course Information

Basic knowledge in math and statistics

What will you learn
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

Any students in college who want to start a career in Data Science.
Anyone who want to explore their career in Data Science

• Lifetime Access to Each Course
• Certificate on Completion of Course
• No Extra Charges Or Admin Fees
• Easy Access to Courses
• High Priority Support After Sales.
• Big Discounts on Individual Courses

Course Specifications

Find various Data Analyst courses including a diploma in data analytics

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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