Python for Data Science Certification Training Course

Overview

machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.

Price (*ask for discount) 400 USD
Access period 180 days

Prerequisite list

  • There are no prerequisites for this course. The Python basics course included with this course provides an additional coding guidance.

Audience list

  • Analytics professionals who want to work with Python
  • Software professionals looking for a career switch in the field of analytics
  • IT professionals interested in pursuing a career in analytics
  • Graduates looking to build a career in Analytics and Data Science
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of Data Science

What is included

  • 24 hours of self-paced learning videos.
  • 4 real-life industry-based projects in the domains of telecom, stock market, etc.
  • Interactive learning with Jupyter notebooks labs
  • Includes concepts of web scraping.
  • Includes a free Python basics course.

Certification Info

  • How To Earn?  Complete any one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer. Score a minimum of 60% in any one of the two simulation tests. Complete 85% of the course. You need to attend one complete batch.
  • How To Maintain?  N/A

Certification Exam Format

  • No Exam

Retake policy

  • N/A.

Enrollment Policy

  • You should pay the online course fee then the online course access will be granted to you within 1 week after receiving payment.
  • Course fee payment is not refundable.

Frequently Asked Questions

Course Outline

Data Science Overview
  • Introduction to Data Science
  • Different Sectors Using Data Science
  • Purpose and Components of Python
  • Quiz
  • Key Takeaways
Data Analytics Overview
  • Data Analytics Process
  • Knowledge Check
  • Exploratory Data Analysis(EDA)
  • EDA-Quantitative Technique
  • EDA - Graphical Technique
  • Data Analytics Conclusion or Predictions
  • Data Analytics Communication
  • Data Types for Plotting
  • Data Types and Plotting
  • Knowledge Check
  • Quiz
  • Key Takeaways
Statistical Analysis and Business Applications
  • Introduction to Statistics
  • Statistical and Non-statistical Analysis
  • Major Categories of Statistics
  • Statistical Analysis Considerations
  • Population and Sample
  • Statistical Analysis Process
  • Data Distribution
  • Dispersion
  • Knowledge Check
  • Histogram
  • Knowledge Check
  • Testing
  • Knowledge Check
  • Correlation and Inferential Statistics
  • Quiz
  • Key Takeaways
Python Environment Setup and Essentials
  • Anaconda
  • Installation of Anaconda Python Distribution (contd.)
  • Data Types with Python
  • Basic Operators and Functions
  • Quiz
  • Key Takeaways
Mathematical Computing with Python (NumPy)
  • Introduction to Numpy
  • Activity-Sequence it Right
  • Demo 01-Creating and Printing an ndarray
  • Knowledge Check
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity-Slice It
  • Copy and Views
  • Mathematical Functions of Numpy
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways
Scientific computing with Python (Scipy)
  • Introduction to SciPy
  • SciPy Sub Package - Integration and Optimization
  • Knowledge Check
  • SciPy sub package
  • Demo - Calculate Eigenvalues and Eigenvector
  • Knowledge Check
  • SciPy Sub Package - Statistics, Weave and IO
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways
Data Manipulation with Pandas
  • Introduction to Pandas
  • Knowledge Check
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • Knowledge Check
  • File Read and Write Support
  • Knowledge Check-Sequence it Right
  • Pandas Sql Operation
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways
Machine Learning with Scikit–Learn
  • Machine Learning Approach
  • Steps 1 and 2
  • Steps 3 and 4
  • How it Works
  • Steps 5 and 6
  • Supervised Learning Model Considerations
  • Knowledge Check
  • Scikit-Learn
  • Knowledge Check
  • Supervised Learning Models - Linear Regression
  • Supervised Learning Models - Logistic Regression
  • Unsupervised Learning Models
  • Pipeline
  • Model Persistence and Evaluation
  • Knowledge Check
  • Assignment 01
  • Assignment 01
  • Assignment 02
  • Assignment 02
  • Quiz
  • Key Takeaways
Natural Language Processing with Scikit Learn
  • NLP Overview
  • NLP Applications
  • Knowledge check
  • NLP Libraries-Scikit
  • Extraction Considerations
  • Scikit Learn-Model Training and Grid Search
  • Assignment 01
  • Demo Assignment 01
  • Assignment 02
  • Demo Assignment 02
  • Quiz
  • Key Takeaway
Data Visualization in Python using matplotlib
  • Introduction to Data Visualization
  • Knowledge Check
  • Line Properties
  • (x,y) Plot and Subplots
  • Knowledge Check
  • Types of Plots
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways
Web Scraping with BeautifulSoup
  • Web Scraping and Parsing
  • Knowledge Check
  • Understanding and Searching the Tree
  • Navigating options
  • Demo3 Navigating a Tree
  • Knowledge Check
  • Modifying the Tree
  • Parsing and Printing the Document
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 demo
  • Quiz
  • Key takeaways
Python integration with Hadoop MapReduce and Spark
  • Why Big Data Solutions are Provided for Python
  • Hadoop Core Components
  • Python Integration with HDFS using Hadoop Streaming
  • Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • Knowledge Check
  • Python Integration with Spark using PySpark
  • Demo 02 - Using PySpark to Determine Word Count
  • Knowledge Check
  • Assignment 01
  • Assignment 01 Demo
  • Assignment 02
  • Assignment 02 Demo
  • Quiz
  • Key takeaways
Project 1
  • Project 1 Stock Market Data Analysis
  • Project 1 Demo
Project 2
  • Project 02
  • Main project 02