Blog
Library

Master Data Science : Hands-On Data Science Bootcamp

Do You Want To Learn Data Science but Don't Know How? Join This Hands-On Data Science Machine Learning Course To Master Latest Scientific Techniques

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video

Data Science Certification Course: Master The Latest Scientific Techniques

Description

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.

Software

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

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
Course Curriculum
Number of Lectures: 167 Total Duration: 22:50:43
Reviews

No Review Yet