Python for Data Science Certification Training Course:

Key features

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

Exam & certification

What do I need to do to unlock my Digital Evolution Orbit certificate?

To become a Certified Data Scientist with Python, you must fulfill the following criteria:



  • 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.



Note:



  • When you have completed the course, you will receive a three-month experience certificate for implementing the projects using Python.

  • It is mandatory that you fulfill both the criteria i.e., completion of any one project and clearing the online exam with minimum score of 60%, to become a certified data scientist.

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Course Details

Course description

The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, 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 NumPy, SciPy, Pandas, Scikit, Matplotlib library and web scrapping for performing data analysis, data wrangling, data exploration, data visualization, hypothesis building, and testing. 



Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression and integrating Python with Hadoop, Spark, and MapReduce.

This course will enable you to:



  • Gain an in-depth understanding of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.

  • Install the required Python environment and other auxiliary tools and libraries

  • Understand the essential concepts of Python programming like data types, tuples, lists, dicts, basic operators, and functions.

  • Perform high-level mathematical computing using NumPy package and its large library of mathematical functions

  • Perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.

  • Perform data analysis and manipulation using data structures and tools provided in Pandas package

  • Gain expertise in machine learning using the Scikit-Learn package

  • Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline

  • Use Scikit-Learn package for natural language processing

  • Use matplotlib library of Python for data visualization

  • Extract useful data from websites by performing web scrapping using Python

  • Integrate Python with Hadoop, Spark, and MapReduce

There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:



  • 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



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

The course includes four real-life, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:



Project-1: NYC 311 Service Request Analysis

Telecommunication: Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.



Project-2: MovieLens Dataset Analysis

Engineering: The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in many research projects related to the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform the analysis using the Exploratory Data Analysis technique for user datasets.



Project-3: Stock Market Data Analysis

Stock Market: As a part of the project, you need to import data using Yahoo data reader of the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. Perform fundamental analytics including plotting  closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all the stocks.



Project-4: Titanic Dataset Analysis

Hazard: On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform the analysis through the exploratory data analysis technique. In particular, we want you to apply the tools of machine learning to predict which passengers survived the tragedy.

Course Preview

  • 0.1 Course Overview
  • 1.1 Introduction to Data Science
  • 1.2 Different Sectors Using Data Science
  • 1.3 Purpose and Components of Python
  • 1.4 Quiz
  • 1.5 Key Takeaways
  • 2.1 Data Analytics Process
  • 2.2 Knowledge Check
  • 2.3 Exploratory Data Analysis(EDA)
  • 2.4 EDA-Quantitative Technique
  • 2.5 EDA - Graphical Technique
  • 2.6 Data Analytics Conclusion or Predictions
  • 2.7 Data Analytics Communication
  • 2.8 Data Types for Plotting
  • 2.9 Data Types and Plotting
  • 2.10 Knowledge Check
  • 2.11 Quiz
  • 2.12 Key Takeaways
  • 3.1 Introduction to Statistics
  • 3.2 Statistical and Non-statistical Analysis
  • 3.3 Major Categories of Statistics
  • 3.4 Statistical Analysis Considerations
  • 3.5 Population and Sample
  • 3.6 Statistical Analysis Process
  • 3.7 Data Distribution
  • 3.8 Dispersion
  • 3.9 Knowledge Check
  • 3.10 Histogram
  • 3.11 Knowledge Check
  • 3.12 Testing
  • 3.13 Knowledge Check
  • 3.14 Correlation and Inferential Statistics
  • 3.15 Quiz
  • 3.16 Key Takeaways
  • 4.1 Anaconda
  • 4.2 Installation of Anaconda Python Distribution (contd.)
  • 4.3 Data Types with Python
  • 4.4 Basic Operators and Functions
  • 4.5 Quiz
  • 4.6 Key Takeaways
  • 5.1 Introduction to Numpy
  • 5.2 Activity-Sequence it Right
  • 5.3 Demo 01-Creating and Printing an ndarray
  • 5.4 Knowledge Check
  • 5.5 Class and Attributes of ndarray
  • 5.6 Basic Operations
  • 5.7 Activity-Slice It
  • 5.8 Copy and Views
  • 5.9 Mathematical Functions of Numpy
  • 5.10 Assignment 01
  • 5.11 Assignment 01 Demo
  • 5.12 Assignment 02
  • 5.13 Assignment 02 Demo
  • 5.14 Quiz
  • 5.15 Key Takeaways
  • 6.1 Introduction to SciPy
  • 6.2 SciPy Sub Package - Integration and Optimization
  • 6.3 Knowledge Check
  • 6.4 SciPy sub package
  • 6.5 Demo - Calculate Eigenvalues and Eigenvector
  • 6.6 Knowledge Check
  • 6.7 SciPy Sub Package - Statistics, Weave and IO
  • 6.8 Assignment 01
  • 6.9 Assignment 01 Demo
  • 6.10 Assignment 02
  • 6.11 Assignment 02 Demo
  • 6.12 Quiz
  • 6.13 Key Takeaways
  • 7.1 Introduction to Pandas
  • 7.2 Knowledge Check
  • 7.3 Understanding DataFrame
  • 7.4 View and Select Data Demo
  • 7.5 Missing Values
  • 7.6 Data Operations
  • 7.7 Knowledge Check
  • 7.8 File Read and Write Support
  • 7.9 Knowledge Check-Sequence it Right
  • 7.10 Pandas Sql Operation
  • 7.11 Assignment 01
  • 7.12 Assignment 01 Demo
  • 7.13 Assignment 02
  • 7.14 Assignment 02 Demo
  • 7.15 Quiz
  • 7.16 Key Takeaways
  • 8.1 Machine Learning Approach
  • 8.2 Steps 1 and 2
  • 8.3 Steps 3 and 4
  • 8.4 How it Works
  • 8.5 Steps 5 and 6
  • 8.6 Supervised Learning Model Considerations
  • 8.7 Knowledge Check
  • 8.8 Scikit-Learn
  • 8.9 Knowledge Check
  • 8.10 Supervised Learning Models - Linear Regression
  • 8.11 Supervised Learning Models - Logistic Regression
  • 8.12 Unsupervised Learning Models
  • 8.13 Pipeline
  • 8.14 Model Persistence and Evaluation
  • 8.15 Knowledge Check
  • 8.16 Assignment 01
  • 8.17 Assignment 01
  • 8.18 Assignment 02
  • 8.19 Assignment 02
  • 8.20 Quiz
  • 8.21 Key Takeaways
  • 9.1 NLP Overview
  • 9.2 NLP Applications
  • 9.3 Knowledge check
  • 9.4 NLP Libraries-Scikit
  • 9.5 Extraction Considerations
  • 9.6 Scikit Learn-Model Training and Grid Search
  • 9.7 Assignment 01
  • 9.8 Demo Assignment 01
  • 9.9 Assignment 02
  • 9.10 Demo Assignment 02
  • 9.11 Quiz
  • 9.12 Key Takeaway
  • 10.1 Introduction to Data Visualization
  • 10.2 Knowledge Check
  • 10.3 Line Properties
  • 10.4 (x,y) Plot and Subplots
  • 10.5 Knowledge Check
  • 10.6 Types of Plots
  • 10.7 Assignment 01
  • 10.8 Assignment 01 Demo
  • 10.9 Assignment 02
  • 10.10 Assignment 02 Demo
  • 10.11 Quiz
  • 10.12 Key Takeaways
  • 11.1 Web Scraping and Parsing
  • 11.2 Knowledge Check
  • 11.3 Understanding and Searching the Tree
  • 11.4 Navigating options
  • 11.5 Demo3 Navigating a Tree
  • 11.6 Knowledge Check
  • 11.7 Modifying the Tree
  • 11.8 Parsing and Printing the Document
  • 11.9 Assignment 01
  • 11.10 Assignment 01 Demo
  • 11.11 Assignment 02
  • 11.12 Assignment 02 demo
  • 11.13 Quiz
  • 11.14 Key takeaways
  • 12.1 Why Big Data Solutions are Provided for Python
  • 12.2 Hadoop Core Components
  • 12.3 Python Integration with HDFS using Hadoop Streaming
  • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • 12.5 Knowledge Check
  • 12.6 Python Integration with Spark using PySpark
  • 12.7 Demo 02 - Using PySpark to Determine Word Count
  • 12.8 Knowledge Check
  • 12.9 Assignment 01
  • 12.10 Assignment 01 Demo
  • 12.11 Assignment 02
  • 12.12 Assignment 02 Demo
  • 12.13 Quiz
  • 12.14 Key takeaways
  • Project 1 Stock Market Data Analysis
  • Project 1 Demo
  • Project 02
  • Main project 02
  • Course Feedback
  • 0.1 Introduction
  • 0.2 Offerings
  • 0.3 Course Objectives
  • 0.4 Course Overview
  • 0.5 Target Audience
  • 0.6 Course Prerequisites
  • 0.7 Need of Python
  • 0.8 Python vs. Rest Other Languages
  • 0.9 Value to the Professionals
  • 0.10 Value to the Professionals (contd.)
  • 0.11 Value to the Professionals (contd.)
  • 0.12 Lessons Covered
  • 0.13 Conclusion
  • 1.1 Introduction
  • 1.2 Objectives
  • 1.3 An Introduction to Python
  • 1.4 Features of Python
  • 1.5 The History of Python
  • 1.6 Releases
  • 1.7 Installation on Ubuntu-based Machines
  • 1.8 Installation on Windows
  • 1.9 Demo-Install and Run Python
  • 1.10 Demo-Install and Run Python
  • 1.11 Example of a Python Program
  • 1.12 Modes of Python
  • 1.13 Batch Script Mode
  • 1.14 Demo-Run Python in the Batch Mode
  • 1.15 Demo-Run Python in the Batch Mode
  • 1.16 Interpreter Mode
  • 1.17 Demo-Run Python in the Interpreter Mode
  • 1.18 Demo-Run Python in the Interpreter Mode
  • 1.19 Indentation in Python
  • 1.20 Indentation in Python (contd.)
  • 1.21 Writing Comments in Python
  • 1.22 Business Scenario
  • 1.23 Quiz
  • 1.24 Summary
  • 1.25 Conclusion
  • 2.1 Python Data Types
  • 2.2 Objectives
  • 2.3 Variables
  • 2.4 Types of Variables
  • 2.5 Types of Variables-String
  • 2.6 Types of Variables-Numeric Types
  • 2.7 Types of Variables-Boolean Variables
  • 2.8 Types of Variables-Boolean Variables (contd.)
  • 2.9 Types of Variables-List
  • 2.10 Adding Elements to a List
  • 2.11 Accessing the Elements of a List
  • 2.12 Types of Variables-Dictionary
  • 2.13 Adding Elements to a Dictionary
  • 2.14 Accessing the Elements of a Dictionary
  • 2.15 Dictionary Methods
  • 2.16 Dictionary Methods (contd.)
  • 2.17 Operators
  • 2.18 Opeators (contd.)
  • 2.19 Logical Operators
  • 2.20 Logical Operators (contd.)
  • 2.21 Logical Operators (contd.)
  • 2.22 Arithmetic Operations on Numeric Values
  • 2.23 Order of Operands
  • 2.24 Operators on Strings
  • 2.25 Variables Comparison
  • 2.26 Variables Comparison (contd.)
  • 2.27 Variables Comparison (contd.)
  • 2.28 Quiz
  • 2.29 Summary
  • 2.30 Conclusion
  • 3.1 Introduction
  • 3.2 Objectives
  • 3.3 Pass Statements
  • 3.4 Conditional Statements
  • 3.5 Types of Conditional Statements
  • 3.6 If Statements
  • 3.7 If…Else Statements
  • 3.8 If…Else If Statements
  • 3.9 If…Else If…Else Statements
  • 3.10 Nested If Statements
  • 3.11 Demo-Use “If…Else” Statement
  • 3.12 Demo-Use “If…Else” Statement
  • 3.13 In Clause
  • 3.14 Ternary Operators
  • 3.15 Quiz
  • 3.16 Summary
  • 3.17 Conclusion
  • 4.1 Introduction
  • 4.2 Objectives
  • 4.3 Loops in Python
  • 4.4 Range Function
  • 4.5 For Loop
  • 4.6 For Loop (contd.)
  • 4.7 While Loop
  • 4.8 Nested Loop
  • 4.9 Demo-Create Loops
  • 4.10 Demo-Create Loops
  • 4.11 Break Statements
  • 4.12 Continue Statements
  • 4.13 Quiz
  • 4.14 Summary
  • 4.15 Conclusion
  • 5.1 Introduction
  • 5.2 Objectives
  • 5.3 Introduction to Functions
  • 5.4 Creating Functions
  • 5.5 Calling Functions
  • 5.6 Arguments and Return Statement
  • 5.7 Variable-Length Arguments
  • 5.8 Variable-Length Arguments (contd.)
  • 5.9 Recursion
  • 5.10 Demo-Create a Function
  • 5.11 Demo-Create a Function
  • 5.12 Quiz
  • 5.13 Summary
  • 5.14 Conclusion
  • 6.1 Introduction
  • 6.2 Objectives
  • 6.3 Classes
  • 6.4 Objects
  • 6.5 Creating a Basic Class
  • 6.6 Accessing Variables of a Class
  • 6.7 Adding Functions to a Class
  • 6.8 Built-in Class Attributes
  • 6.9 Init Function
  • 6.10 Example of Defining and Using a Class
  • 6.11 Example of Defining and Using a Class (contd.)
  • 6.12 Demo-Create a Class
  • 6.13 Demo-Create a Class
  • 6.14 Quiz
  • 6.15 Summary
  • 6.16 Conclusion
  • 7.1 Introduction
  • 7.2 Objectives
  • 7.3 Modules
  • 7.4 Creating Modules
  • 7.5 Using Modules
  • 7.6 Using Modules (contd.)
  • 7.7 Using Modules (contd.)
  • 7.8 Using Modules (contd.)
  • 7.9 Python Interpreter Module Search
  • 7.10 Demo-Create and Import a Module
  • 7.11 Demo-Create and Import a Module
  • 7.12 Namespace and Scoping
  • 7.13 Dir() Function
  • 7.14 Dir() Function (contd.)
  • 7.15 Global and Local Functions
  • 7.16 Reload a Module
  • 7.17 Packages in Python
  • 7.18 Quiz
  • 7.19 Summary
  • 7.20 Conclusion