Course description

  • Why learn Data Science with Python?

    Python is a multi-paradigm or versatile programming language that can be considered as a sort of swiss knife for the coding world. This is because it supports structured programming, Object Oriented Programming, and even functional programming patterns. The versatility of Python undoubtedly makes it the best-suited programming language for the data scientists. Here are some of the other advantages of python for data science, which will help you understand why you should learn data science with Python:

    • Python is a powerful open source programming language, which means that it’s free to use while having all the properties that a programming language should have.
    • It is a versatile programming language that supports Object-Oriented Programming, Structured Programming, and functional programming patterns.
    • Python has some 72,000 libraries in the Python Package Index that aid in scientific calculations and machine learning applications.
    • Python sports an easy to understand and readable syntax that ensures that the development time is cut into half when compared with other programming languages.
    • Python enables you to perform data analysis, data manipulation, and data visualization, which are very important in data science.

    All the above mentioned advantages of Python programming language make it ideal to be used for data science by the data scientists. Owing to the extensibility and general purpose nature, it is recommended that you 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: Products rating prediction for Amazon

    Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

    Domain: E-commerce

    Project 2: Demand Forecasting for Walmart

    Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

    Domain: Retail

    Project 3: Improving customer experience for Comcast

    Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

    Domain: Telecom

    Project 4: Attrition Analysis for IBM

    IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

    Domain: Workforce Analytics
     

    Project 5: NYC 311 Service Request Analysis

    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.

    Domain: Telecommunication
     
    Project 6: MovieLens Dataset Analysis

    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.

    Domain: Engineering
     
    Project 7: Stock Market Data Analysis

    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.

    Domain: Stock Market
     
    Project 8: Titanic Dataset Analysis

    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.

    Domain: Hazard

Course preview

    • Lesson 00 - Course Overview

      04:34
      • 0.001 Course Overview
        04:34
    • Lesson 01 - Data Science Overview

      20:27
      • 1.001 Introduction to Data Science
        08:42
      • 1.002 Different Sectors Using Data Science
        05:59
      • 1.003 Purpose and Components of Python
        05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways
        00:44
    • Lesson 02 - Data Analytics Overview

      18:20
      • 2.001 Data Analytics Process
        07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique
        00:57
      • 2.006 Data Analytics Conclusion or Predictions
        04:30
      • 2.007 Data Analytics Communication
        02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting
        02:29
      • 2.11 Quiz
      • 2.012 Key Takeaways
        00:57
      • 2.10 Knowledge Check
    • Lesson 03 - Statistical Analysis and Business Applications

      23:53
      • 3.001 Introduction to Statistics
        01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics
        01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample
        02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution
        01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram
        03:59
      • 3.11 Knowledge Check
      • 3.012 Testing
        08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics
        02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways
        01:31
    • Lesson 04 - Python Environment Setup and Essentials

      23:58
      • 4.001 Anaconda
        02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python
        13:28
      • 4.004 Basic Operators and Functions
        06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways
        01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy)

      30:31
      • 5.001 Introduction to Numpy
        05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray
        04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations
        07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy
        05:01
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo
        03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo
        03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways
        00:55
    • Lesson 06 - Scientific computing with Python (Scipy)

      23:35
      • 6.001 Introduction to SciPy
        06:57
      • 6.002 SciPy Sub Package - Integration and Optimization
        05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector
        01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO
        05:46
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo
        01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo
        00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways
        01:10
    • Lesson 07 - Data Manipulation with Pandas

      47:34
      • 7.001 Introduction to Pandas
        12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame
        05:31
      • 7.004 View and Select Data Demo
        05:34
      • 7.005 Missing Values
        03:16
      • 7.006 Data Operations
        09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support
        00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation
        02:00
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo
        04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo
        02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways
        01:34
    • Lesson 08 - Machine Learning with Scikit–Learn

      1:02:10
      • 8.001 Machine Learning Approach
        03:57
      • 8.002 Steps 1 and 2
        01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works
        01:24
      • 8.005 Steps 5 and 6
        01:54
      • 8.006 Supervised Learning Model Considerations
        00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn
        02:10
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression
        11:19
      • 8.011 Supervised Learning Models - Logistic Regression
        08:43
      • 8.012 Unsupervised Learning Models
        10:40
      • 8.013 Pipeline
        02:37
      • 8.014 Model Persistence and Evaluation
        05:45
      • 8.16 Assignment 01
      • 8.15 Knowledge Check
      • 8.017 Assignment 01
        05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02
        05:14
      • 8.20 Quiz
      • 8.021 Key Takeaways
        01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn

      49:03
      • 9.001 NLP Overview
        10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge Check
      • 9.004 NLP Libraries-Scikit
        12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search
        10:17
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01
        06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02
        08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway
        01:03
    • Lesson 10 - Data Visualization in Python using matplotlib

      32:43
      • 10.001 Introduction to Data Visualization
        08:01
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots
        10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots
        09:32
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo
        02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo
        01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways
        00:59
    • Lesson 11 - Web Scraping with BeautifulSoup

      52:26
      • 11.001 Web Scraping and Parsing
        12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree
        12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree
        04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree
        05:37
      • 11.008 Parsing and Printing the Document
        09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo
        01:55
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo
        04:57
      • 11.13 Quiz
      • 11.014 Key takeaways
        00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark

      40:39
      • 12.001 Why Big Data Solutions are Provided for Python
        04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming
        07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
        08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark
        07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count
        04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo
        02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo
        03:30
      • 12.13 Quiz
      • 12.014 Key takeaways
        01:20
    • Math Refresher

      30:36
      • Math Refresher
        30:36
    • Lesson 1 - Course Objective

      • Learning Objectives
    • Lesson 2 - Defining Data Science

      12:46
      • Learning Objectives
      • 1.1 What is data science
        02:37
      • 1.2 There are many paths to data science
        03:55
      • 1.3 Any advice for new data scientist
        02:59
      • 1.4 What is the cloud
        03:15
    • Lesson 3 - What do Data Science People do

      11:24
      • Learning Objectives
      • 2.1 A day in the life of a data science person
        03:53
      • 2.2 R versus Python
        01:51
      • 2.3 Data science tools and technology
        05:40
    • Lesson 4 - Data Science in Business

      10:40
      • Learning Objectives
      • 3.1 How should companies get started in data science
        03:00
      • 3.2 Recruiting for data science
        07:40
    • Lesson 5 - Use Cases for Data Science

      06:28
      • Learning Objectives
      • 4.1 Applications of data science
        06:28
    • Lesson 6 - Data Science People

      01:05
      • Learning Objectives
      • 5.1 Things data science people say
        01:05
      • Unlocking IBM Certificate
    • Lesson 1 - Welcome

      02:28
      • Welcome
        02:28
      • Learning Objectives
    • Lesson 2 - Python Basics

      11:55
      • 2.1 Learning Objectives
      • 2.2 Your first program
        01:15
      • 2.3 Types
        02:57
      • 2.4 Expressions and Variables
        03:50
      • 2.5 Write your First Python Code
      • 2.6 String Operations
        03:53
      • 2.7 String Operations
    • Lesson 3 - Python Data Structures

      16:22
      • 3.1 Learning Objectives
      • 3.2 Lists and Tuples
        08:46
      • 3.3 Lists and Tuples
      • 3.4 Sets
        05:12
      • 3.5 Sets
      • 3.6 Dictionaries
        02:24
      • 3.7 Dictionaries
    • Lesson 4 - Python Programming Fundamentals

      41:08
      • 4.1 Learning Objectives
      • 4.2 Conditions and Branching
        10:13
      • 4.3 Conditions and Branching
      • 4.4 Loops
        06:40
      • 4.5 Loops
      • 4.6 Functions
        13:28
      • 4.7 Functions
      • 4.8 Objects and Classes
        10:47
      • 4.9 Objects and Classes
    • Lesson 5 - Working with Data in Python

      12:35
      • 5.1 Learning Objectives
      • 5.2 Reading files with open
        03:38
      • 5.3 Reading Files
      • 5.4 Writing files with open
        02:49
      • 5.5 Writing Files
      • 5.6 Loading data with Pandas
        04:07
      • 5.7 Working with and Saving data with Pandas
        02:01
      • 5.8 Loading Data and Viewing Data
    • Lesson 6 - Working with Numpy Arrays

      18:26
      • 6.1 Learning Objectives
      • 6.2 Numpy One-Dimensional Arrays
        11:18
      • 6.3 Working with One-Dimensional Numpy Arrays
      • 6.4 Numpy Two-Dimensional Arrays
        07:08
      • 6.5 Working with Two-Dimensional Numpy Arrays
    • Lesson 7 - Course Summary

      01:13
      • Course Summary
        01:13
      • Unlocking IBM Certificate
    • Lesson 1 Introduction

      02:55
      • 1.1 Introduction
        02:55
    • Lesson 2 Sample or population data

      03:56
      • 2.1 Sample or population data
        03:56
    • Lesson 3 The fundamentals of descriptive statistics

      21:18
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47
    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30
    • Lesson 6 Distributions

      16:17
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19
    • Lesson 7 Estimators and Estimates

      23:36
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38
    • Lesson 8 Confidence intervals advanced topics

      14:27
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24
    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37
    • Lesson 10 Hypothesis testing Introduction

      12:36
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20
    • Lesson 11 Hypothesis testing Let's start testing!

      26:39
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48
    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31
    • Lesson 13 The fundamentals of regression analysis

      18:32
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36
    • Lesson 14 Subtleties of regression analysis

      23:25
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00
    • Lesson 15 Assumptions for linear regression analysis

      19:16
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22
    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20
    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42
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Exam & certification

  • How do I earn my Simplilearn certificate?

    To become a Certified Data Scientist with Python, you must fulfil 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.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Free Data Science with Python Practice Test to understand the type of tests that are part of the course curriculum.  

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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Sutirtha Sahu
Sutirtha Sahu Bangalore

I have enrolled in the Data Science Masters program and completed a couple of courses. The course material is very well designed — starting with basics for beginners and then moving towards more advanced concepts. The tutors are very supportive, too, and try to keep the sessions quite interactive. They are always ready to repeat a particular idea until the majority of the learners understood the concept. The faculty is quite helpful with assignment submissions. Overall, a perfect experience and I recommend to learners who want to keep themselves updated with market requirements.

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Tham Chup Wai
Tham Chup Wai Singapore

I just completed 3 classes under this program - Data Science Using SAS, R and Big Data Hadoop and Spark Developer. I am currently enrolled in Python training. What I like the most is that the live recordings from each class are lifetime references for us to review in the future. The self-running videos in each topic were also very useful as they cover theory which might not have been covered during the live classes. I have made significant gains so far in my knowledge of key technologies and tools in Data Science. Together with electives offered under this program, I will eventually be getting a comprehensive foundation training in Data Science.

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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.

    • * Disclaimer

      * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

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