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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 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
    • Project 1 18:36
      • Project 1 Stock Market Data Analysis
      • Project 1 Demo 18:36
    • Project 2 20:06
      • Project 02
      • Main project 02 20:06
    • Course Feedback
      • Course Feedback
    • 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

  • 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, and he 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.

Contact Us

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