Key Highlights

Data Science using R & Python Trainingcourse includes:

Job Opportunities

Why should you take Data Science?

What you’ll learn

This Data Science course from SMEClabs is best for individuals who are looking to transform their careers. People who have the passion to use the data, analyze, visualize and use it for the betterment of the Business and the society.  For those mathematics enthusiasts, who can apply maths in real life and solve complex business challenges. This is specifically ideal for the people who are

  • Analysts and Software engineers looking for a career shift in the data science stream.
  • Freshers who want to start the career as we teach from the basics and gradually build up your skills.
  • Individuals who are graduated and working in the Data Science field and looking to upgrade their careers.

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:

  • Developers aspiring to be a ‘Data Scientist’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘R’ professionals who wish to work Big Data
  • Analysts wanting to understand Data Science methodologies
Data Science using R & Python Training Syllabus:
  • ➢ Life Cycle of Data Science
    ➢ Skills required for Data Science
    ➢ Careers Path in Data Science
    ➢ Applications of Data Science

➢ Relationship between Statistics and Data Science
➢ Introduction to Data
➢ Descriptive Statistics
➢ Inferential Statistics
➢ Random Sampling and Probability Distribution

➢ Python programming
➢Python for Exploratory Data Analysis

  • • Introduction to RDBMS
    • Retrieving
    • Updating
    • Inserting
    • Deleting
    • Sorting AND Filtering
    • Summarizing AND Grouping
    • Using Subqueries
    • Joining Tables
    • Views
    • Stored Procedure
    • Python Database ConnectionAPI
  • ➢ Introduction To Machine Learning

• Text preprocessing using Bag of words technique
• TF(Term Frequency)
• IDF(Inverse Document Frequency)
• Normalization
• Vectorization
• NLP with Python

Detailed Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects


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