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Course description

  • Why learn Data Science with Python?

  • What are the course objectives?

    The Data Science with Python course will furnish you with 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. 
     
    Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.

  • What skills will you learn?

    This Python for Data Science training course will enable you to:
    • Gain an in-depth understanding of data science processes, 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 such as data types, tuples, lists, dicts, basic operators and functions
    • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
    • Perform scientific and technical computing using the 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 the Pandas package
    • Gain expertise in machine learning using the Scikit-Learn package
    • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
    • Use the Scikit-Learn package for natural language processing
    • Use the 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

  • Who should take this Python for Data Science course?

    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 with Python training particularly for the following professionals:
     
    • Analytics professionals who want to work with Python
    • Software professionals looking to get into 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 Data Science with Python course. The Python basics course included with this program provides additional coding guidance.
     

  • What projects are included in this Python for Data Science certification course?

    The course includes four real-world, 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 data wrangling techniques to understand patterns in the data and 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 several research projects in the fields of information filtering, collaborative filtering and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.
     
    Project 3: Stock Market Data Analysis
    Stock Market: As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft and Google. You will 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 of 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 an analysis using the exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

Course preview

  • Data Science with Python
    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science 08:42
      • 1.2 Different Sectors Using Data Science 05:59
      • 1.3 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique 00:57
      • 2.6 Data Analytics Conclusion or Predictions 04:30
      • 2.7 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.12 Testing 08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python 13:28
      • 4.4 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo 01-Creating and Printing an ndarray 04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations 07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy 05:01
      • 5.10 Assignment 01
      • 5.11 Assignment 01 Demo 03:55
      • 5.12 Assignment 02
      • 5.13 Assignment 02 Demo 03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy 06:57
      • 6.2 SciPy Sub Package - Integration and Optimization 05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector 01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO 05:46
      • 6.8 Assignment 01
      • 6.9 Assignment 01 Demo 01:20
      • 6.10 Assignment 02
      • 6.11 Assignment 02 Demo 00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas 12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame 05:31
      • 7.4 View and Select Data Demo 05:34
      • 7.5 Missing Values 03:16
      • 7.6 Data Operations 09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support 00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation 02:00
      • 7.11 Assignment 01
      • 7.12 Assignment 01 Demo 04:09
      • 7.13 Assignment 02
      • 7.14 Assignment 02 Demo 02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach 03:57
      • 8.2 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works 01:24
      • 8.5 Steps 5 and 6 01:54
      • 8.6 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression 11:19
      • 8.11 Supervised Learning Models - Logistic Regression 08:43
      • 8.12 Unsupervised Learning Models 10:40
      • 8.13 Pipeline 02:37
      • 8.14 Model Persistence and Evaluation 05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.19 Assignment 02 05:14
      • 8.20 Quiz
      • 8.21 Key Takeaways 01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview 10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit 12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search 10:17
      • 9.7 Assignment 01
      • 9.8 Demo Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.10 Demo Assignment 02 08:00
      • 9.11 Quiz
      • 9.12 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization 08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots 10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots 09:34
      • 10.7 Assignment 01
      • 10.8 Assignment 01 Demo 02:23
      • 10.9 Assignment 02
      • 10.10 Assignment 02 Demo 01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing 12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree 12:56
      • 11.4 Navigating options
      • 11.5 Demo3 Navigating a Tree 04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree 05:38
      • 11.8 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.10 Assignment 01 Demo 01:55
      • 11.11 Assignment 02
      • 11.12 Assignment 02 demo 04:57
      • 11.13 Quiz
      • 11.14 Key takeaways 00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python 04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming 07:20
      • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count 08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark 07:43
      • 12.7 Demo 02 - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Assignment 01 Demo 02:47
      • 12.11 Assignment 02
      • 12.12 Assignment 02 Demo 03:30
      • 12.13 Quiz
      • 12.14 Key takeaways 01:20
  • Free Course
    Python Basics
    • Lesson 00 - Course Overview 04:44
      • 0.1 Introduction 00:13
      • 0.2 Offerings 00:07
      • 0.3 Course Objectives 00:29
      • 0.4 Course Overview 00:21
      • 0.5 Target Audience 00:27
      • 0.6 Course Prerequisites 00:11
      • 0.7 Need of Python 00:49
      • 0.8 Python vs. Rest Other Languages 00:25
      • 0.9 Value to the Professionals 00:16
      • 0.10 Value to the Professionals (contd.) 00:31
      • 0.11 Value to the Professionals (contd.) 00:24
      • 0.12 Lessons Covered 00:23
      • 0.13 Conclusion 00:08
    • Lesson 01 - Introduction to Python 28:15
      • 1.1 Introduction 00:12
      • 1.2 Objectives 00:16
      • 1.3 An Introduction to Python 01:27
      • 1.4 Features of Python 00:44
      • 1.5 The History of Python 00:27
      • 1.6 Releases 00:33
      • 1.7 Installation on Ubuntu-based Machines 01:00
      • 1.8 Installation on Windows 00:59
      • 1.9 Demo-Install and Run Python 00:08
      • 1.10 Demo-Install and Run Python 14:17
      • 1.11 Example of a Python Program 01:08
      • 1.12 Modes of Python 00:27
      • 1.13 Batch Script Mode 00:29
      • 1.14 Demo-Run Python in the Batch Mode 00:05
      • 1.15 Demo-Run Python in the Batch Mode 01:14
      • 1.16 Interpreter Mode 00:46
      • 1.17 Demo-Run Python in the Interpreter Mode 00:05
      • 1.18 Demo-Run Python in the Interpreter Mode 00:31
      • 1.19 Indentation in Python 00:49
      • 1.20 Indentation in Python (contd.) 00:26
      • 1.21 Writing Comments in Python 01:06
      • 1.22 Business Scenario 00:23
      • 1.23 Quiz
      • 1.24 Summary 00:33
      • 1.25 Conclusion 00:10
    • Lesson 02 - Python Data Types 19:34
      • 2.1 Python Data Types 00:10
      • 2.2 Objectives 00:18
      • 2.3 Variables 00:52
      • 2.4 Types of Variables 01:09
      • 2.5 Types of Variables-String 01:07
      • 2.6 Types of Variables-Numeric Types 00:34
      • 2.7 Types of Variables-Boolean Variables 00:34
      • 2.8 Types of Variables-Boolean Variables (contd.) 00:35
      • 2.9 Types of Variables-List 00:24
      • 2.10 Adding Elements to a List 00:48
      • 2.11 Accessing the Elements of a List 01:09
      • 2.12 Types of Variables-Dictionary 00:30
      • 2.13 Adding Elements to a Dictionary 00:50
      • 2.14 Accessing the Elements of a Dictionary 00:12
      • 2.15 Dictionary Methods 00:32
      • 2.16 Dictionary Methods (contd.) 00:30
      • 2.17 Operators 00:21
      • 2.18 Opeators (contd.) 00:10
      • 2.19 Logical Operators 00:44
      • 2.20 Logical Operators (contd.) 00:47
      • 2.21 Logical Operators (contd.) 00:39
      • 2.22 Arithmetic Operations on Numeric Values 00:58
      • 2.23 Order of Operands 01:03
      • 2.24 Operators on Strings 01:03
      • 2.25 Variables Comparison 01:06
      • 2.26 Variables Comparison (contd.) 01:05
      • 2.27 Variables Comparison (contd.) 00:33
      • 2.28 Quiz
      • 2.29 Summary 00:41
      • 2.30 Conclusion 00:10
    • Lesson 03 - Control Statements 09:27
      • 3.1 Introduction 00:10
      • 3.2 Objectives 00:13
      • 3.3 Pass Statements 00:15
      • 3.4 Conditional Statements 00:45
      • 3.5 Types of Conditional Statements 00:18
      • 3.6 If Statements 00:28
      • 3.7 If…Else Statements 00:49
      • 3.8 If…Else If Statements 01:06
      • 3.9 If…Else If…Else Statements 00:18
      • 3.10 Nested If Statements 00:38
      • 3.11 Demo-Use “If…Else” Statement 00:05
      • 3.12 Demo-Use “If…Else” Statement 02:12
      • 3.13 In Clause 00:56
      • 3.14 Ternary Operators 00:44
      • 3.15 Quiz
      • 3.16 Summary 00:21
      • 3.17 Conclusion 00:09
    • Lesson 04 - Loops 08:10
      • 4.1 Introduction 00:10
      • 4.2 Objectives 00:12
      • 4.3 Loops in Python 00:37
      • 4.4 Range Function 00:28
      • 4.5 For Loop 00:35
      • 4.6 For Loop (contd.) 00:23
      • 4.7 While Loop 00:35
      • 4.8 Nested Loop 00:50
      • 4.9 Demo-Create Loops 00:05
      • 4.10 Demo-Create Loops 02:21
      • 4.11 Break Statements 00:48
      • 4.12 Continue Statements 00:36
      • 4.13 Quiz
      • 4.14 Summary 00:22
      • 4.15 Conclusion 00:08
    • Lesson 05 - Functions 09:27
      • 5.1 Introduction 00:10
      • 5.2 Objectives 00:13
      • 5.3 Introduction to Functions 00:49
      • 5.4 Creating Functions 00:49
      • 5.5 Calling Functions 00:43
      • 5.6 Arguments and Return Statement 01:28
      • 5.7 Variable-Length Arguments 00:53
      • 5.8 Variable-Length Arguments (contd.) 00:33
      • 5.9 Recursion 00:43
      • 5.10 Demo-Create a Function 00:05
      • 5.11 Demo-Create a Function 02:19
      • 5.12 Quiz
      • 5.13 Summary 00:33
      • 5.14 Conclusion 00:09
    • Lesson 06 - Classes 11:23
      • 6.1 Introduction 00:10
      • 6.2 Objectives 00:14
      • 6.3 Classes 01:39
      • 6.4 Objects 00:33
      • 6.5 Creating a Basic Class 00:35
      • 6.6 Accessing Variables of a Class 00:39
      • 6.7 Adding Functions to a Class 00:40
      • 6.8 Built-in Class Attributes 00:37
      • 6.9 Init Function 00:38
      • 6.10 Example of Defining and Using a Class 00:42
      • 6.11 Example of Defining and Using a Class (contd.) 00:27
      • 6.12 Demo-Create a Class 00:05
      • 6.13 Demo-Create a Class 03:34
      • 6.14 Quiz
      • 6.15 Summary 00:40
      • 6.16 Conclusion 00:10
    • Lesson 07 - Imports and Modules 12:01
      • 7.1 Introduction 00:11
      • 7.2 Objectives 00:16
      • 7.3 Modules 00:54
      • 7.4 Creating Modules 00:18
      • 7.5 Using Modules 00:14
      • 7.6 Using Modules (contd.) 01:10
      • 7.7 Using Modules (contd.) 00:27
      • 7.8 Using Modules (contd.) 00:26
      • 7.9 Python Interpreter Module Search 00:57
      • 7.10 Demo-Create and Import a Module 00:06
      • 7.11 Demo-Create and Import a Module 02:24
      • 7.12 Namespace and Scoping 00:57
      • 7.13 Dir() Function 00:29
      • 7.14 Dir() Function (contd.) 00:23
      • 7.15 Global and Local Functions 00:31
      • 7.16 Reload a Module 00:48
      • 7.17 Packages in Python 00:46
      • 7.18 Quiz
      • 7.19 Summary 00:34
      • 7.20 Conclusion 00:10
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Exam & certification FREE PRACTICE TEST

  • How do I earn my Simplilearn certificate?

    To become a Certified Data Scientist with Python, you must fulfill the following criteria:
    • Complete 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
    • 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 (completion of any one project and passing the online exam with minimum score of 60%) to become a certified data scientist.

Course advisor

Alvaro Fuentes
Alvaro Fuentes Founder and Data Scientist at Quant Company

Alvaro is a Data Scientist who founded Quant Company and has also worked as a lead Economic analyst in the Central Bank of Guatemala. He is a M.S. in Quantitative Economics and Applied Mathematics and is actively involved in consulting and training in the data science space.

Reviews

Solomon Olutu
Solomon Olutu Snr Principal QA Architect at Comcast, Philadelphia

Simplilearn's Data Science with Python training was a great experience. Their trainers are the best that I have come across since I started learning with Silplilearn. He is always prepared for class with a well-documented note session which is also useful for hands-on learning after class to enhance the learning experience. Thanks Simplilearn. This is the best platform that I have come across.

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Gaurav Dubey
Gaurav Dubey Associate Consultant at Syntel, Pune

Prior to joining Data Science course with Simplilearn, I had little knowledge about it. The certification helped me to understand the Machine Learning, Web Scraping, Natural Language Processing in detail. The trainer was very helpful and was always there to guide me in every step. The certification helped me to enhance my career from Software Engineer to Associate Consultant with a salary hike. I am planning to take a few more course from Simplilearn in future.

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Jatin Alwani
Jatin Alwani Student at Lovely Professional University, Jalandhar

I have enrolled for Data Science certification from Simplilearn. The course materials are great and the trainers are also very helpful. The industry-based project is the best part of the course. Simplilearn is better than any others in the market.

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Shoeb Mohammad
Shoeb Mohammad Analyst at Accenture, Delhi

I had joined the Data Science certification from Simplilearn. The course content was really good. The trainer puts a lot of efforts into explaining every detail which made the learning very absorbing. The customer support is always available whenever you need help. I actually feel one step forward towards my goal. Thank you.

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FAQs

  • What are the system requirements?

    To run Python, your system must fulfill the following basic requirements:
    • 32 or 64-bit Operating System
    • 1GB RAM 
    The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instruction on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What are the modes of training offered for this Python for Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
     
    Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
     
    WinPython portable distribution is the open source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session.

  • Can I cancel my enrollment? Will I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • Who provides the certification?

    At the end of the training, subject to satisfactory evaluation of the project as well as passing the online exam (minimum score 80%), you will receive a certificate from Simplilearn stating that you are a certified data scientist with Python.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about the group discounts.

  • How do I enroll for the Data Science with Python online training?

    You can enroll for this training on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal 
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

  • What does it mean to be GSA approved course?

    The course is part of Simplilearn’s contract with GSA (only US) with special pricing for GSA approved agencies & organizations. To know more click here

  • How do i know if I am eligible to buy this course at GSA price?

    You should be employed with GSA approved agencies & organizations. The list of approved agencies is provided here

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    Contact Us

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    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
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