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 7.5 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.
Python Fundamentals & NumPy Package
-
1Segment - 02-introduction-to-data-science-fin
-
2Segment - 03
-
3Segment - 04-doing-data-science
-
4Segment - 05-problem-definitions-and-collecting-data
-
5Segment - 06-data-pipelines-preparation-cleaning-understanding
-
6Segment - 07-model-building-validation-visualization-data-science-applications
-
7Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools
-
8Segment - 09-data-science-future-readings
-
9Segment - 10-ai-primer-and-machine-learning-concepts
-
10Segment - 11-machine-learning-applications
-
11Segment - 12-machine-learning-supervised-unsupervised
-
12Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX
-
13Segment - 13-supervised-unsupervised-learning-methodology-clustering
-
14Segment - 14-python-vs-r
-
15Segment - 15-tools-for-scalable-machine-learning
-
16Segment - 16-introduction-to-python
-
17Segment - 17-more-python-details
-
18Segment - 18-python-examples
-
19Segment - 19-anaconda-navigator
Data Analysis using Pandas and Data Visualization
-
20Segment - 20-introduction-to-python-notebook
-
21Segment - 21-git-and-repl
-
22Segment - 22-introduction-ids-and-juypter-notebook
-
23Segment - 23-lab-tutorials-learning-juypter-notebook
-
24Segment - 24-python-loops-and-functions
-
25Segment - 25-python-objects-introduction
-
26Segment - 26-python-numpy
-
27Segment - 27-arrays
-
28Segment - 28-advanced-arrays
-
29Segment - 29-matrices
Supervised (Regression and Classification) & Unsupervised (Clustering) Machine L
-
30Segment - 30-numpy-lab-tutorial
-
31Segment 31 -review-session-python-for-data-science
-
32Segment 32 - Why Pandas
-
33Segment 33 - Data Series
-
34Segment 34 - Series, Keys and Indices
-
35Segment 35 - NumPy Array vs. Panda Series
-
36Segment 36 - Dataframe
-
37Segment 37 - Dataframe Operations
-
38Segment 38 - Using Lambda
-
39Segment 39 - Dataframe Operations (Continued)
-
40Segment 40 - Statistical Analysis, Calculations and Operations
-
41Segment 41 - Lab - Advanced Operations in Action
-
42Segment 42 - Lab - Advanced Operations in Action (Continued)
-
43Segment 43 - Pandas Visualization and Matplotlib
-
44Segment 44 - Seaborn
-
45Segment 45 - ggplot
-
46Segment 46 - Statistical Graphs
-
47Segment 47 - Lab - Visualizations
Quizzes
-
48Segment 48 - Introduction to Scikit-Learn
-
49Segment 49 - Scikit-Learn Uses and Applications
-
50Segment 50 - Scikit-Learn vs. Other Tools
-
51Segment 51 - Scikit-Learn Classes, Utils and Data Sets
-
52Segment 51 - Setting Up Scikit-Learn
-
53Segment 52 - Estimators and Algorithms
-
54Segment 53 - Preprocessing and Feature Engineering
-
55Segment 54 - Metrics
-
56Segment 55 - Clustering
-
57Segment 56 - Prediction
-
58Segment 57 - Principal Component Analysis
-
59Segment 58 - Lab - Classification Algorithm