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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. Module 1 - SLIDES - Part 1

    2. Module 1 - SLIDES - Part 2

    3. Module 1 - SLIDES - Part 3

    4. Segment 01 - Introduction to Data Science

      FREE PREVIEW

    5. Segment 02 - Reports vs. Insights..., and What are the Data Scientist Building Blocks

    6. Segment 03 - Doing Data Science

    7. Segment 04 - Problem Definitions and Collecting Data

    8. Segment 05 - Data Pipelines, Data Preparation, Data Cleaning and Related Subjects

    9. Segment 06 - Model Building Validation Visualization and Data Science Applications

    10. Segment 07 - Data Science Methodology, Data Analytics Tools and Open Source Tools

    11. Segment 08 - Data Science and Further Readings

    12. Segment 09 - AI Primer and Machine Learning Concepts

    13. Segment 09a - Questions and Answers (Group 001)

    14. Segment 10 - Machine Learning Applications

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    15. Segment 11 - Types of Machine Learning

    16. Segment 12 - Supervised and Unsupervised Machine Learning

    17. Segment 13 - Supervised and Unsupervised Learning, Methodologies and Clustering

    18. Segment 14 - Python vs. R

    19. Segment 15 - Tools for Scalable Machine Learning

    20. Segment 16 - Introduction to Python

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    21. Segment 17 - More Python Introductory Details

    22. Segment 18 - Python Examples

    23. Segment 19 - Anaconda Navigator

    1. Module 2 - SLIDES - Part 1 - Python Introduction

    2. Module 2 - SLIDES - Part 2 - NumPy

    3. Module 2 - SLIDES - Part 3 - Pandas

    4. Segment 20 - Introduction to Python Notebook

    5. Segment 21 - Git and REPL

    6. Segment 22 - Introduction IDE and Jupyter Notebook

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    7. Segment 23 - Lab Tutorials - Learning Jupyter Notebook

    8. Segment 24 - Python Loops and Functions

    9. Segment 25 - Python Objects Introduction

    10. Slides - Module 2, Part 3 - Pandas

    11. Segment 26 - Python and NumPy

    12. Segment 27 - Arrays

    13. Segment 28 - Advanced Arrays

      FREE PREVIEW

    14. Segment 29 - Matrices

    15. Segment 30 - NumPy Lab Tutorial

    1. Module 3 - SLIDES - Part 1 - Pandas

    2. Module 3 - SLIDES - Part 2 - Visualization

    3. Segment 31 - Review Session Python for Data Science

    4. Segment 32 - Why Pandas

    5. Segment 33 - Data Series

    6. Segment 34 - Series, Keys and Indices

    7. Segment 35 - NumPy Array vs. Panda Series

    8. Segment 36 - Dataframe

    9. Segment 37 - Dataframe Operations

    10. Segment 38 - Using Lambda

    11. Segment 38a - Questions and Answers (Group 001)

    12. Segment 39 - Dataframe Operations (Continued)

    13. Segment 40 - Statistical Analysis, Calculations and Operations

    14. Segment 41 - Lab - Advanced Operations in Action

    15. Segment 41a - Questions and Answers (Group 002)

    16. Segment 42 - Lab - Advanced Operations in Action (Continued)

    17. Segment 43 - Pandas Visualization and Matplotlib

    18. Segment 44 - Seaborn

    19. Segment 45 - ggplot

    20. Segment 46 - Statistical Graphs

    21. Segment 47 - Questions and Answers (Group 003)

    1. Module 4 - SLIDES - Scikit-Learn

    2. Segment 48 - Introduction to Scikit-Learn

    3. Segment 49 - Scikit-Learn Uses and Applications

    4. Segment 50 - Scikit-Learn vs. Other Tools

    5. Segment 51 - Setting Up Scikit-Learn

    6. PYT-Seg-51b - Scikit-Learn Classes, Utils and Data Sets

    7. Segment 52 - Estimators and Algorithms

    8. Segment 52A - Questions and Answers (Group 001)

    9. Segment 53 - Preprocessing and Feature Engineering

    10. Segment 54 - Metrics

    11. Segment 55 - Clustering

    12. Segment 56 - Prediction

    13. Segment 56A - Questions and Answers (Group 002)

    14. Segment 57 - Principal Component Analysis

    15. Segment 58 - Lab - Classification Algorithm

    1. Quiz Overview

    2. Quiz 1

    3. Quiz 2

    4. Quiz 3

    5. Quiz 4

    6. Quiz 5

    7. Quiz 6

    1. Overview

    2. INSTRUCTIONS: Virtual labs (For Colaboratory)

    3. Lab 1

    4. Lab 2

    5. Lab 3

    6. Lab 4

    7. Lab 5

    8. Lab 6

About this course

  • $6.95 / month with 14 day free trial
  • 89 lessons
  • 9.5 hours of video content