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Included in this program

This Python for Data Science course is an introduction to Python and how to apply it in data science. The course contains ~60 lectures and 9.5 hours of content taught by Praba Santanakrishnan, a highly experienced data scientist from Microsoft. Staring with some fundamentals about "what is data science," and "who is a data scientist," the program rapidly move into the specific challenges of data science. This includes the challenges of problem definitions and collecting data, to data pipelines, data preparation, data cleaning and related subjects. Data science methodologies, data analytics tools and open source tools are all covered. Model building validation, visualization and various data science applications are also covered. Discussion of the types of machine learning are covered, including supervised and unsupervised machine learning, as well as methodologies and clustering. NumPy, Pandas, Python Notebook, Git, REPL, IDS and Jupyter Notebook are also covered. Arrays, advanced arrays, and matrices are discussed in some detail to ensure you understand what it is all about and how these tools are implemented.

  • 9 ½ hours on-demand video

  • 15 downloadable resources

  • Full lifetime access

  • Full lifetime access

  • Access on mobile and PC

  • Certificate of completion

Course curriculum

  • 1

    Welcome!

  • 2

    Slack Channel and More

    • Slack Channel (All announcements, Q&A with instructors, etc.)
    • If (and when) you need help...
  • 3

    Module 1 - Introduction to Machine Learning and It’s Technologies

    • Module 1 - SLIDES - Part 1
    • Module 1 - SLIDES - Part 2
    • Module 1 - SLIDES - Part 3
    • Segment 01 - Introduction to Data Science FREE PREVIEW
    • Segment 02 - Reports vs. Insights..., and What are the Data Scientist Building Blocks
    • Segment 03 - Doing Data Science
    • Segment 04 - Problem Definitions and Collecting Data
    • Segment 05 - Data Pipelines, Data Preparation, Data Cleaning and Related Subjects
    • Segment 06 - Model Building Validation Visualization and Data Science Applications
    • Segment 07 - Data Science Methodology, Data Analytics Tools and Open Source Tools
    • Segment 08 - Data Science and Further Readings
    • Segment 09 - AI Primer and Machine Learning Concepts
    • Segment 09a - Questions and Answers (Group 001)
    • Segment 10 - Machine Learning Applications FREE PREVIEW
    • Segment 11 - Types of Machine Learning
    • Segment 12 - Supervised and Unsupervised Machine Learning
    • Segment 13 - Supervised and Unsupervised Learning, Methodologies and Clustering
    • Segment 14 - Python vs. R
    • Segment 15 - Tools for Scalable Machine Learning
    • Segment 16 - Introduction to Python FREE PREVIEW
    • Segment 17 - More Python Introductory Details
    • Segment 18 - Python Examples
    • Segment 19 - Anaconda Navigator
  • 4

    Module 2 - Python Fundamentals & NumPy Package

    • Module 2 - SLIDES - Part 1 - Python Introduction
    • Module 2 - SLIDES - Part 2 - NumPy
    • Module 2 - SLIDES - Part 3 - Pandas
    • Segment 20 - Introduction to Python Notebook
    • Segment 21 - Git and REPL
    • Segment 22 - Introduction IDE and Jupyter Notebook FREE PREVIEW
    • Segment 23 - Lab Tutorials - Learning Jupyter Notebook
    • Segment 24 - Python Loops and Functions
    • Segment 25 - Python Objects Introduction
    • Slides - Module 2, Part 3 - Pandas
    • Segment 26 - Python and NumPy
    • Segment 27 - Arrays
    • Segment 28 - Advanced Arrays FREE PREVIEW
    • Segment 29 - Matrices
    • Segment 30 - NumPy Lab Tutorial
  • 5

    Module 3 - Data Analysis using Pandas and Data Visualization

    • Module 3 - SLIDES - Part 1 - Pandas
    • Module 3 - SLIDES - Part 2 - Visualization
    • Segment 31 - Review Session Python for Data Science
    • Segment 32 - Why Pandas
    • Segment 33 - Data Series
    • Segment 34 - Series, Keys and Indices
    • Segment 35 - NumPy Array vs. Panda Series
    • Segment 36 - Dataframe
    • Segment 37 - Dataframe Operations
    • Segment 38 - Using Lambda
    • Segment 38a - Questions and Answers (Group 001)
    • Segment 39 - Dataframe Operations (Continued)
    • Segment 40 - Statistical Analysis, Calculations and Operations
    • Segment 41 - Lab - Advanced Operations in Action
    • Segment 41a - Questions and Answers (Group 002)
    • Segment 42 - Lab - Advanced Operations in Action (Continued)
    • Segment 43 - Pandas Visualization and Matplotlib
    • Segment 44 - Seaborn
    • Segment 45 - ggplot
    • Segment 46 - Statistical Graphs
    • Segment 47 - Questions and Answers (Group 003)
  • 6

    Module 4 - Supervised (Regression and Classification) & Unsupervised (Clustering) Machine Learning

    • Module 4 - SLIDES - Scikit-Learn
    • Segment 48 - Introduction to Scikit-Learn
    • Segment 49 - Scikit-Learn Uses and Applications
    • Segment 50 - Scikit-Learn vs. Other Tools
    • Segment 51 - Setting Up Scikit-Learn
    • PYT-Seg-51b - Scikit-Learn Classes, Utils and Data Sets
    • Segment 52 - Estimators and Algorithms
    • Segment 52A - Questions and Answers (Group 001)
    • Segment 53 - Preprocessing and Feature Engineering
    • Segment 54 - Metrics
    • Segment 55 - Clustering
    • Segment 56 - Prediction
    • Segment 56A - Questions and Answers (Group 002)
    • Segment 57 - Principal Component Analysis
    • Segment 58 - Lab - Classification Algorithm
  • 7

    Quizzes

    • Quiz Overview
    • Quiz 1
    • Quiz 2
    • Quiz 3
    • Quiz 4
    • Quiz 5
    • Quiz 6
  • 8

    Labs

    • Overview
    • INSTRUCTIONS: Virtual labs (For Colaboratory)
    • Lab 1
    • Lab 2
    • Lab 3
    • Lab 4
    • Lab 5
    • Lab 6
  • 9

    Final Examination

    • Final Exam: Overview and Instructions
    • Final Exam: Launch here
  • 10

    Next Steps

    • Congrats! Here's what's next...
    • Yippee! You're an alumnus!
    • Alumni Slack Channel