Course Overview

Learn the most demanding, exciting, and challenging job training of today and the future. The training contents start from SQL, reporting, BI tools, Statistics, Python programming, and towards utilization of these all into Machine learning for real-life situations. This is the most effective, time-efficient, and structured data science training available to you.

Course Features

  • Live Instructor-Led online training.
  • Receive a comprehensive set of materials, including course notes and all the class examples.
  • Easy access to the trainer for any questions and follow-ups.
  • An ample a number of practical assignments to test your skills.
  • Assignments evaluation, feedbacks, and encouragements to develop your skills in a better way.
  • Focus on hands-on training during sessions.

Data Science with Python

Course Duration

80 hours of training

Course Contents

  • Why Software Systems?
  • Types of software Applications
  • Components of a software system
  • Types of Software projects
  • Why to learn Data science?
  • Data Science Domains
  • Skills required for Data scientist
  • Importance of Database management systems
  • Select queries for single table
  • Joins and Unions
  • Aggregating data
  • Designing and modifying data and tables
  • Working with Subqueries
  • Views and Stored procedures
  • Introduction and installation of Tableau
  • Connecting to different data sources
  • Measures, dimensions and calculated field
  • Working with colors, labels and formatting
  • Performing AB test
  • Aliases and anomalies
  • Validating the tests
  • Visualization of tests
  • Introduction to Data warehouse
  • Overview of ETL process
  • Installing SSDT on SQL server
  • Preparing data to Load
  • Setting up source and destination sources
  • Performing Extract and Transforms
  • Due diligence upload QA
  • Error handling
  • Minimizing Anomalies
  • Basics of ETL QA
  • Python basics
  • Python Data structure
  • File I/O
  • Exception handling
  • Python OOP
  • Basics of statistics
  • Importing libraries and datasets
  • Splitting dataset into training set and test set
  • Simple and multiple linear regression
  • Polynomial regression
  • Decision tree regression
  • Support Vector regression
  • Logistic regression
  • K-nearest neighbors
  • Naïve Bayes
  • K-means clustering
  • Hierarchical clustering
  • NLP libraries and example

Course Prerequisites

  • Students should have a very basic understanding of Microsoft Word and Excel.
  • Knowledge of any DBMS at a basic level along with any programming language knowledge will be added advantage.
  • ​A PC with a minimum Windows 7 Operating system with MS Office and an Internet connection.
  • Candidates should have basic skills in Office tools with some programming knowledge.

Course Outcome

  • Learn from scratch and gradually builds up your skills.
  • Comprehensive contents to utilize machine learning algorithms.
  • Learn to visualize data with a powerful tools like Tableau.
  • Understand the basics of Statistics.
  • Learn linear and logical regression analysis.
  • Understand the importance of BI tools for data preparation and transformations.
  • Learn to implement Machine Learning Algorithms with Python.
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