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Big data, business intelligence, business analytics, machine learning, and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognize the appropriate approach to solving a problem. Our course on Data Science will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

60,999.00 49,999.00
Course Access

2 weeks, 1 day

Last Updated

March 27, 2020

Students Enrolled

3

Total Video Time

EXPIRED

Posted by

Certification
Download Syllabus Course Description  
  • This course will help learners gain expertise in skills required to be a Data Scientist
  • Training on programming tools such as R and Python along with real time hands on projects.
  • This course would also help to create dashboards and storytelling with Tableau.
  Requirements  
  • Learners should have understanding of the services involved in the IT lifecycle.
  • Learners should have knowledge of basic statistical and mathematical functions.
  What are the system requirements for this course?   Hardware Requirements:
  • Memory – Minimum 8 GB RAM
  • Processor – Intel Core i3 CPU @2.00 GHz or above
  • Storage – 250 GB HDD/SDD or above
  Software Requirements:
  • Operating System – Windows 7 or above, Ubuntu 14 or above
  What am I going to get from this course?  
  • Strong problem solving skills.
  • Experience using statistical computer languages (R and Python) to manipulate data and draw insights from large
  • data sets.
  • Experience working with and creating data architectures.
  • Knowledge of a variety of machine learning algorithms (Supervised, unsupervised and reinforced) and their realworld
  • advantages/drawbacks.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests
  • and proper usage, etc.) and experience with applications.

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Course Currilcum

  • Class Details 00:00:00
    • Statistical Learning Details 00:00:00
    • Python Environment Setup and Essentials Details 00:00:00
    • R Environment Setup and Essentials Details 00:00:00
    • Python language Basic Constructs Details 00:00:00
    • OOP concepts in Python Details 00:00:00
    • NumPy for mathematical computing Details 00:00:00
    • SciPy for scientific computing Details 00:00:00
    • Matplotlib for data visualization Details 00:00:00
    • Pandas for data analysis and machine learning Details 00:00:00
    • Introduction to Machine Learning with R and Python Details 00:00:00
    • Supervised Learning and Linear Regression Details 00:00:00
    • Classification and Logistic Regression Details 00:00:00
    • Decision Tree and Random Forest Details 00:00:00
    • Naïve Bayes and Support Vector Machine Details 00:00:00
    • Unsupervised Learning Details 00:00:00
    • Natural Language Processing and Text Mining Details 00:00:00
    • Time Series Analysis Details 00:00:00

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