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DATA SCIENCE

Data Science with Python

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The Data Science with Python course provides a complete overview of Data Analytics tools and techniques using Python. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlock your career as a Data Scientist.
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Eligibility & Pre-requisites

  • Eligibility

    The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

  • Pre-requisites

    To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this program.

Data Science with Python Training Overview

The Python Data Science course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. Upon course completion, you will master the essential tools of Data Science with Python.

Benefits

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581-percent by 2021 and professionals with Python skills will have an additional advantage.

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      Skills Covered​

      Data wrangling
      Data exploration
      Data visualization
      Mathematical computing
      Web scraping
      Hypothesis building
      Python programming concepts
      NumPy and SciPy package
      ScikitLearn package for Natural Language Processing

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      Course Currilcum

      • Lesson 00 - Course Overview

        04:34Preview
        • 0.001 Course Overview
          04:34

      • Lesson 01 - Data Science Overview

        20:27Preview
        • 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

        18:20Preview
        • 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

        23:53Preview
        • 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

        23:58Preview
        • 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)

        30:31Preview
        • 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
        • 5.010 Analyse GDP of Countries
        • 5.011 Assignment 01 Demo
          03:55
        • 5.012 Analyse London Olympics Dataset
        • 5.013 Assignment 02 Demo
          03:16
        • 5.14 Quiz
        • 5.015 Key Takeaways
          00:55

      • Lesson 06 - Scientific computing with Python (Scipy)

        23:32Preview
        • 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
        • 6.008 Solving Linear Algebra problem using SciPy
        • 6.009 Assignment 01 Demo
          01:20
        • 6.010 Perform CDF and PDF using Scipy
        • 6.011 Assignment 02 Demo
          00:52
        • 6.12 Quiz
        • 6.013 Key Takeaways
          01:10

      • Lesson 07 - Data Manipulation with Pandas

        47:34Preview
        • 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
        • 7.011 Analyse the Federal Aviation Authority Dataset using Pandas
        • 7.012 Assignment 01 Demo
          04:09
        • 7.013 Analyse NewYork city fire department Dataset
        • 7.014 Assignment 02 Demo
          02:34
        • 7.15 Quiz
        • 7.016 Key Takeaways
          01:34

      • Lesson 08 - Machine Learning with Scikit–Learn

        01:01:54Preview
        • 8.001 Machine Learning Approach
          03:57
        • 8.002 Steps One and Two
          01:00
        • 8.3 Steps Three and Four
        • 8.004 How it Works
          01:24
        • 8.005 Steps Five and Six
          01:54
        • 8.006 Supervised Learning Model Considerations
          00:30
        • 8.008 ScikitLearn
          02:10
        • 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
        • 8.15 Knowledge Check
        • 8.016 Analysing Ad Budgets for different media channels
        • 8.017 Assignment One
          05:45
        • 8.018 Building a model to predict Diabetes
        • 8.019 Assignment Two
          04:58
        • Knowledge Check
        • 8.021 Key Takeaways
          01:12

      • Lesson 09 - Natural Language Processing with Scikit Learn

        49:03Preview
        • 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
        • 9.007 Analysing Spam Collection Data
        • 9.008 Demo Assignment 01
          06:32
        • 9.009 Sentiment Analysis using NLP
        • 9.010 Demo Assignment 02
          08:00
        • 9.11 Quiz
        • 9.012 Key Takeaway
          01:03

      • Lesson 10 - Data Visualization in Python using matplotlib

        33:20Preview
        • 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
        • 10.007 Draw a pair plot using seaborn library
        • 10.008 Assignment 01 Demo
          02:23
        • 10.009 Analysing Cause of Death
        • 10.010 Assignment 02 Demo
          02:24
        • 10.11 Quiz
        • 10.012 Key Takeaways
          00:59

      • Lesson 11 - Web Scraping with BeautifulSoup

        52:26Preview
        • 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
        • 11.009 Web Scraping of Simplilearn Website
        • 11.010 Assignment 01 Demo
          01:55
        • 11.011 Web Scraping of Simplilearn Website Resource page
        • 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

        40:39Preview
        • 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
        • 12.009 Determine the wordcount
        • 12.010 Assignment 01 Demo
          02:47
        • 12.011 Display all the airports based in New York using PySpark
        • 12.012 Assignment 02 Demo
          03:30
        • 12.13 Quiz
        • 12.014 Key takeaways
          01:20

      • Practice Projects

        • IBM HR Analytics Employee Attrition Modeling.

      • Math Refresher

        30:36Preview
        • Math Refresher
          30:36

      • Lesson 1 Introduction

        02:55Preview
        • 1.1 Introduction
          02:55

      • Lesson 2 Sample or population data

        03:56Preview
        • 2.1 Sample or population data
          03:56

      • Lesson 3 The fundamentals of descriptive statistics

        21:18Preview
        • 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

        25:17Preview
        • 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

        16:17Preview
        • 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

        23:36Preview
        • 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

        14:27Preview
        • 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

        12:36Preview
        • 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

        06:31Preview
        • 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

        23:25Preview
        • 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

        19:16Preview
        • 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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          SAP FICO Training FAQs

          Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
           

          Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of data science libraries and it is the ideal language for implementing algorithms and the rapid development of applications.

          The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn’s Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.
           

          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 instructions on how to install them.

          All of our highly qualified Data Science 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.

          Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science 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.