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  • 40 hours of instructor-led training
  • 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

Course description

  • What’s the focus of this course?

    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 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.

  • What are the course objectives?

    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

  • Who should take this 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 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.

  • What projects are included in this course?

    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

    • Lesson 00 - Course Overview 04:34
      • 0.1 Course Overview04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.1 Introduction to Data Science08:42
      • 1.2 Different Sectors Using Data Science05:59
      • 1.3 Purpose and Components of Python05:02
      • 1.4 Quiz
      • 1.5 Key Takeaways00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.1 Data Analytics Process07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.5 EDA - Graphical Technique00:57
      • 2.6 Data Analytics Conclusion or Predictions04:30
      • 2.7 Data Analytics Communication02:06
      • 2.8 Data Types for Plotting
      • 2.9 Data Types and Plotting02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.1 Introduction to Statistics01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.3 Major Categories of Statistics01:34
      • 3.4 Statistical Analysis Considerations
      • 3.5 Population and Sample02:15
      • 3.6 Statistical Analysis Process
      • 3.7 Data Distribution01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.10 Histogram03:59
      • 3.11 Knowledge Check
      • 3.12 Testing08:18
      • 3.13 Knowledge Check
      • 3.14 Correlation and Inferential Statistics02:57
      • 3.15 Quiz
      • 3.16 Key Takeaways01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.1 Anaconda02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.3 Data Types with Python13:28
      • 4.4 Basic Operators and Functions06:26
      • 4.5 Quiz
      • 4.6 Key Takeaways01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.1 Introduction to Numpy05:30
      • 5.2 Activity-Sequence it Right
      • 5.3 Demo 01-Creating and Printing an ndarray04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.6 Basic Operations07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.9 Mathematical Functions of Numpy05:01
      • 5.10 Assignment 01
      • 5.11 Assignment 01 Demo03:55
      • 5.12 Assignment 02
      • 5.13 Assignment 02 Demo03:16
      • 5.14 Quiz
      • 5.15 Key Takeaways00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.1 Introduction to SciPy06:57
      • 6.2 SciPy Sub Package - Integration and Optimization05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.5 Demo - Calculate Eigenvalues and Eigenvector01:36
      • 6.6 Knowledge Check
      • 6.7 SciPy Sub Package - Statistics, Weave and IO05:46
      • 6.8 Assignment 01
      • 6.9 Assignment 01 Demo01:20
      • 6.10 Assignment 02
      • 6.11 Assignment 02 Demo00:55
      • 6.12 Quiz
      • 6.13 Key Takeaways01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.1 Introduction to Pandas12:29
      • 7.2 Knowledge Check
      • 7.3 Understanding DataFrame05:31
      • 7.4 View and Select Data Demo05:34
      • 7.5 Missing Values03:16
      • 7.6 Data Operations09:56
      • 7.7 Knowledge Check
      • 7.8 File Read and Write Support00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.10 Pandas Sql Operation02:00
      • 7.11 Assignment 01
      • 7.12 Assignment 01 Demo04:09
      • 7.13 Assignment 02
      • 7.14 Assignment 02 Demo02:34
      • 7.15 Quiz
      • 7.16 Key Takeaways01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.1 Machine Learning Approach03:57
      • 8.2 Steps 1 and 201:00
      • 8.3 Steps 3 and 4
      • 8.4 How it Works01:24
      • 8.5 Steps 5 and 601:54
      • 8.6 Supervised Learning Model Considerations00:30
      • 8.7 Knowledge Check
      • 8.8 Scikit-Learn02:10
      • 8.9 Knowledge Check
      • 8.10 Supervised Learning Models - Linear Regression11:19
      • 8.11 Supervised Learning Models - Logistic Regression08:43
      • 8.12 Unsupervised Learning Models10:40
      • 8.13 Pipeline02:37
      • 8.14 Model Persistence and Evaluation05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.17 Assignment 0105:45
      • 8.18 Assignment 02
      • 8.19 Assignment 0205:14
      • 8.20 Quiz
      • 8.21 Key Takeaways01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.1 NLP Overview10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.4 NLP Libraries-Scikit12:29
      • 9.5 Extraction Considerations
      • 9.6 Scikit Learn-Model Training and Grid Search10:17
      • 9.7 Assignment 01
      • 9.8 Demo Assignment 0106:32
      • 9.9 Assignment 02
      • 9.10 Demo Assignment 0208:00
      • 9.11 Quiz
      • 9.12 Key Takeaway01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.1 Introduction to Data Visualization08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.4 (x,y) Plot and Subplots10:01
      • 10.5 Knowledge Check
      • 10.6 Types of Plots09:34
      • 10.7 Assignment 01
      • 10.8 Assignment 01 Demo02:23
      • 10.9 Assignment 02
      • 10.10 Assignment 02 Demo01:47
      • 10.11 Quiz
      • 10.12 Key Takeaways00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.1 Web Scraping and Parsing12:50
      • 11.2 Knowledge Check
      • 11.3 Understanding and Searching the Tree12:56
      • 11.4 Navigating options
      • 11.5 Demo3 Navigating a Tree04:22
      • 11.6 Knowledge Check
      • 11.7 Modifying the Tree05:38
      • 11.8 Parsing and Printing the Document09:05
      • 11.9 Assignment 01
      • 11.10 Assignment 01 Demo01:55
      • 11.11 Assignment 02
      • 11.12 Assignment 02 demo04:57
      • 11.13 Quiz
      • 11.14 Key takeaways00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.1 Why Big Data Solutions are Provided for Python04:55
      • 12.2 Hadoop Core Components
      • 12.3 Python Integration with HDFS using Hadoop Streaming07:20
      • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count08:52
      • 12.5 Knowledge Check
      • 12.6 Python Integration with Spark using PySpark07:43
      • 12.7 Demo 02 - Using PySpark to Determine Word Count04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.10 Assignment 01 Demo02:47
      • 12.11 Assignment 02
      • 12.12 Assignment 02 Demo03:30
      • 12.13 Quiz
      • 12.14 Key takeaways01:20
    • Project 1 18:36
      • Project 1 Stock Market Data Analysis
      • Project 1 Demo18:36
    • Project 2 20:06
      • Project 02
      • Main project 0220:06
    • Course Feedback
      • Course Feedback
    • Lesson 00 - Course Overview 04:44
      • 0.1 Introduction00:13
      • 0.2 Offerings00:07
      • 0.3 Course Objectives00:29
      • 0.4 Course Overview00:21
      • 0.5 Target Audience00:27
      • 0.6 Course Prerequisites00:11
      • 0.7 Need of Python00:49
      • 0.8 Python vs. Rest Other Languages00:25
      • 0.9 Value to the Professionals00:16
      • 0.10 Value to the Professionals (contd.)00:31
      • 0.11 Value to the Professionals (contd.)00:24
      • 0.12 Lessons Covered00:23
      • 0.13 Conclusion00:08
    • Lesson 01 - Introduction to Python 28:15
      • 1.1 Introduction00:12
      • 1.2 Objectives00:16
      • 1.3 An Introduction to Python01:27
      • 1.4 Features of Python00:44
      • 1.5 The History of Python00:27
      • 1.6 Releases00:33
      • 1.7 Installation on Ubuntu-based Machines01:00
      • 1.8 Installation on Windows00:59
      • 1.9 Demo-Install and Run Python00:08
      • 1.10 Demo-Install and Run Python14:17
      • 1.11 Example of a Python Program01:08
      • 1.12 Modes of Python00:27
      • 1.13 Batch Script Mode00:29
      • 1.14 Demo-Run Python in the Batch Mode00:05
      • 1.15 Demo-Run Python in the Batch Mode01:14
      • 1.16 Interpreter Mode00:46
      • 1.17 Demo-Run Python in the Interpreter Mode00:05
      • 1.18 Demo-Run Python in the Interpreter Mode00:31
      • 1.19 Indentation in Python00:49
      • 1.20 Indentation in Python (contd.)00:26
      • 1.21 Writing Comments in Python01:06
      • 1.22 Business Scenario00:23
      • 1.23 Quiz
      • 1.24 Summary00:33
      • 1.25 Conclusion00:10
    • Lesson 02 - Python Data Types 19:34
      • 2.1 Python Data Types00:10
      • 2.2 Objectives00:18
      • 2.3 Variables00:52
      • 2.4 Types of Variables01:09
      • 2.5 Types of Variables-String01:07
      • 2.6 Types of Variables-Numeric Types00:34
      • 2.7 Types of Variables-Boolean Variables00:34
      • 2.8 Types of Variables-Boolean Variables (contd.)00:35
      • 2.9 Types of Variables-List00:24
      • 2.10 Adding Elements to a List00:48
      • 2.11 Accessing the Elements of a List01:09
      • 2.12 Types of Variables-Dictionary00:30
      • 2.13 Adding Elements to a Dictionary00:50
      • 2.14 Accessing the Elements of a Dictionary00:12
      • 2.15 Dictionary Methods00:32
      • 2.16 Dictionary Methods (contd.)00:30
      • 2.17 Operators00:21
      • 2.18 Opeators (contd.)00:10
      • 2.19 Logical Operators00:44
      • 2.20 Logical Operators (contd.)00:47
      • 2.21 Logical Operators (contd.)00:39
      • 2.22 Arithmetic Operations on Numeric Values00:58
      • 2.23 Order of Operands01:03
      • 2.24 Operators on Strings01:03
      • 2.25 Variables Comparison01:06
      • 2.26 Variables Comparison (contd.)01:05
      • 2.27 Variables Comparison (contd.)00:33
      • 2.28 Quiz
      • 2.29 Summary00:41
      • 2.30 Conclusion00:10
    • Lesson 03 - Control Statements 09:27
      • 3.1 Introduction00:10
      • 3.2 Objectives00:13
      • 3.3 Pass Statements00:15
      • 3.4 Conditional Statements00:45
      • 3.5 Types of Conditional Statements00:18
      • 3.6 If Statements00:28
      • 3.7 If…Else Statements00:49
      • 3.8 If…Else If Statements01:06
      • 3.9 If…Else If…Else Statements00:18
      • 3.10 Nested If Statements00:38
      • 3.11 Demo-Use “If…Else” Statement00:05
      • 3.12 Demo-Use “If…Else” Statement02:12
      • 3.13 In Clause00:56
      • 3.14 Ternary Operators00:44
      • 3.15 Quiz
      • 3.16 Summary00:21
      • 3.17 Conclusion00:09
    • Lesson 04 - Loops 08:10
      • 4.1 Introduction00:10
      • 4.2 Objectives00:12
      • 4.3 Loops in Python00:37
      • 4.4 Range Function00:28
      • 4.5 For Loop00:35
      • 4.6 For Loop (contd.)00:23
      • 4.7 While Loop00:35
      • 4.8 Nested Loop00:50
      • 4.9 Demo-Create Loops00:05
      • 4.10 Demo-Create Loops02:21
      • 4.11 Break Statements00:48
      • 4.12 Continue Statements00:36
      • 4.13 Quiz
      • 4.14 Summary00:22
      • 4.15 Conclusion00:08
    • Lesson 05 - Functions 09:27
      • 5.1 Introduction00:10
      • 5.2 Objectives00:13
      • 5.3 Introduction to Functions00:49
      • 5.4 Creating Functions00:49
      • 5.5 Calling Functions00:43
      • 5.6 Arguments and Return Statement01:28
      • 5.7 Variable-Length Arguments00:53
      • 5.8 Variable-Length Arguments (contd.)00:33
      • 5.9 Recursion00:43
      • 5.10 Demo-Create a Function00:05
      • 5.11 Demo-Create a Function02:19
      • 5.12 Quiz
      • 5.13 Summary00:33
      • 5.14 Conclusion00:09
    • Lesson 06 - Classes 11:23
      • 6.1 Introduction00:10
      • 6.2 Objectives00:14
      • 6.3 Classes01:39
      • 6.4 Objects00:33
      • 6.5 Creating a Basic Class00:35
      • 6.6 Accessing Variables of a Class00:39
      • 6.7 Adding Functions to a Class00:40
      • 6.8 Built-in Class Attributes00:37
      • 6.9 Init Function00:38
      • 6.10 Example of Defining and Using a Class00:42
      • 6.11 Example of Defining and Using a Class (contd.)00:27
      • 6.12 Demo-Create a Class00:05
      • 6.13 Demo-Create a Class03:34
      • 6.14 Quiz
      • 6.15 Summary00:40
      • 6.16 Conclusion00:10
    • Lesson 07 - Imports and Modules 12:01
      • 7.1 Introduction00:11
      • 7.2 Objectives00:16
      • 7.3 Modules00:54
      • 7.4 Creating Modules00:18
      • 7.5 Using Modules00:14
      • 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 Search00:57
      • 7.10 Demo-Create and Import a Module00:06
      • 7.11 Demo-Create and Import a Module02:24
      • 7.12 Namespace and Scoping00:57
      • 7.13 Dir() Function00:29
      • 7.14 Dir() Function (contd.)00:23
      • 7.15 Global and Local Functions00:31
      • 7.16 Reload a Module00:48
      • 7.17 Packages in Python00:46
      • 7.18 Quiz
      • 7.19 Summary00:34
      • 7.20 Conclusion00:10
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Exam & certification FREE PRACTICE TEST

  • What do I need to do to unlock my Simplilearn 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.

Course advisor

Ronald van Loon
Ronald van Loon Top 10 Big Data & Data Science Influencer, Director - Adversitement

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

FAQs

  • What are the System Requirements?

    To run Python, your system needs to fulfil the following requirements:
    • 32 or 64-bit Operating System
    • 1GB RAM
    The instruction require Anaconda and Jupyter notebooks. The e-learning videos provide detail instruction on how to install these.

  • Who are the trainers?

    The trainings are delivered by highly qualified and certified instructors with relevant industry experience.

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

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the option to attend the course remotely from your desktop via video conferencing. This format saves productivity challenges and decreases your time spent away from work or home.

    Online Self-Learning: In this mode, you will receive the lecture videos and you can go through the course as per your convenience.

    WinPython portable distribution is the open source environment on which all hands-on exercises will be done. Instructions for installation will be conveyed during the training.

  • What if I miss a class?

    We provide the recordings of the class after the session is conducted. So, if you miss a class, you can go through the recordings before the next session.

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

    Yes, you can cancel your enrolment. We provide a complete refund after deducting the administration fee. To know more, please go through our Refund Policy.

  • Who provides the certification?

    At the end of the training, subject to satisfactory evaluation of the project as well as clearing the online exam (minimum 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 know more about the group discounts.

  • What are the payment options?

    Payments can be made using any of the following options and a receipt of the same will be issued to you automatically via email.
    • Visa Debit/credit Card
    • American Express and Diners Club Card
    • Master Card, Or
    • PayPal

  • Who are our Faculties and how are they selected?

    All our trainers are working professionals and industry experts with at least 10-12 years of relevant teaching experience.

    Each of them have gone through a rigorous selection process which includes profile screening, technical evaluation, and training demo before they are certified to train for us.  

    We also ensure that only those trainers with a high alumni rating continue to train for us.

  • What is Global Teaching Assistance?

    Our teaching assistants are here to help you get certified in your first attempt.

    They are a dedicated team of subject matter experts to help you at every step and enrich your learning experience from class onboarding to project mentoring and job assistance.

    They engage with the students proactively to ensure the course path is followed.

    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.

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