Data Science Skills you will learn

  • 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

Who should learn Data Science with Python

  • Analytics Professionals
  • Software Professionals
  • IT Professionals
  • Data Scientist
  • Data Analyst

What you will learn in Data Science with Python

  • Data Science with Python

    • 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
      • 5.010 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)

      23:32
      • 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

      47:34
      • 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
      • 7.011 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

      01:02:10
      • 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
        05:14
      • Knowledge Check
      • 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
      • 9.007 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

      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
      • 10.007 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
        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
      • 11.009 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

      40:39
      • 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
      • 12.009 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.

Why you should learn Data Science with Python

46% of jobs

In the data science field require Python

1581% by 2020

Growth in demand for data science professionals

Career Opportunities

FAQs

  • What are the prerequisites to attend the Data Science with Python program?

    Learners should have a basic understanding of mathematics concepts like statistics, calculus, linear algebra, and probability before taking this data science with Python course. Knowledge of any programming language is also beneficial.

  • How long does it take to get started with data science with Python?

    This data science course gives you access to 15 hours of in-depth learning material that you can follow at your own pace. It is important to practice Python programming to gain a strong hold in data science and this course will help you do it in a short time.

  • How do beginners learn data science with Python?

    Beginners always look for a step-by-step guide when they want to learn data science. While online tutorials and books are good to begin with, this data science with Python basics program helps you learn everything from scratch.

  • What is the Data Science with Python course?

    This Data Science with Python course is the ideal stepping stone in your learning journey as an aspiring data scientist. It will give you an understanding of data analytics tools and techniques, data analysis, visualization, Python basics and its libraries, web scraping, and natural language processing. 

  • What should I learn first in the data science program?

    You can begin the data science course by first learning about the data analytics process, data types, and statistical analysis. You can then go ahead with Python programming fundamentals.

  • Is the Data Science with Python program easy to learn?

    The instructors who have designed this Data Science with Python program have rich teaching experience and are aware of the various learner needs. As such, professionals who don’t have any prior knowledge of data science can still get started easily with Python through this course.

  • What are the basics in a Data Science with Python training program?

    Exploratory data analytics, data types and plotting, statistical analysis process, and data manipulation are the basics covered in this data science with Python course.

  • What is data science used for?

    The importance of data science has increased significantly over the past few years. Companies are using data science to convert raw data into meaningful information through effective processing, analysis, modeling, and visualization. By uncovering such patterns and insights from data, they are able to make more informed decisions and serve customers better.

  • Why should you learn Data Science with Python?

    Data science is the hottest technology of the digital age. Python is one of the most popular languages in data science, which is used to perform data analysis, data manipulation, and data visualization. Getting started with Python is one of the primary steps in your journey to become a data scientist which is one of the top ranking professionals in any analytics organization. Despite numerous lucrative opportunities for skilled data professionals, there aren’t enough data scientists. Because of this, now would be the right time to learn Data Science with Python. Watch this video to know more.

  • What are 5 essential steps to learn Data Science with Python?

    The 5 steps that any aspirant should follow to learn Data Science with Python include:
    Step 1: Master the fundamentals of Python
    Step 2: Build multiple Python projects to fine-tune your skills
    Step 3: Learn Python libraries like NumPy, Pandas, and Matplotlib
    Step 4: Develop a versatile data science portfolio
    Step 5: Enroll in advanced data science programs to excel further

  • What are the top skills required for a career in data science?

     The top skills required for a career in data science include:

    • Knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java
    • An understanding of SAS and other analytical tools
    • Adept at working with unstructured data
    • A sharp business acumen
    • Excellent communication skills
    • Strong intuition regarding data

  • Can I complete this data science with Python program in 90 days?

    The lessons covered in this data science with Python course have rich content and are easy to follow. Learners can study at their own pace and complete the course quite earlier than 90 days.

  • Will I get a certificate after completing the Data Science with Python program?

    You will not receive a course completion certificate after completing the Data Science with Python free course. However, you can upgrade and enroll for the paid version of the course to earn your certificate.


     

  • What are the career opportunities in data science?

    The world is wide open for professionals willing to start a career in data science. Those who gain data science skills through this program can aim for designations like data scientists, data architects, data analysts, Python programmers, and more. You’ll find a lot of open positions related to these job roles when you search in any job portal.

  • What are my next best learning options after completing this data science with Python program?

    After completing this data science with Python training program, you can enroll in other courses like Data Scientist Master’s Program or Post Graduate Program in Data Science.

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