The Epic Python Developer Certification Bundle

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12 Courses & 91 Hours
$39.99$1,794.00
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What's Included

Complete Data Science Training with Python for Data Analysis
  • Certification included
  • Experience level required: Intermediate
  • Access 116 lectures & 12 hours of content 24/7
  • Length of time users can access this course: Lifetime

Course Curriculum

116 Lessons (12h)

  • Your First Program

  • Introduction to the Data Science in Python Bootcamp

    What is Data Science?3:37
    Introduction to the Course & Instructor11:34
    Data and Scripts for the Course
    Introduction to the Python Data Science Tool10:57
    For Mac Users4:05
    Introduction to the Python Data Science Environment19:15
    Some Miscellaneous IPython Usage Facts5:25
    Online iPython Interpreter3:26
    Conclusion to Section 12:36

  • Introduction to Python Pre-Requisites for Data Science

    Different Types of Data Used in Statistical & ML Analysis3:37
    Different Types of Data Used Programatically3:46
    Python Data Science Packages To Be Used3:16
    Conclusion to Section 21:59

  • Introduction to Numpy

    Numpy: Introduction3:46
    Create Numpy Arrays10:51
    Numpy Operations16:48
    Matrix Arithmetic and Linear Systems7:34
    Numpy for Basic Vector Arithmetic6:16
    Numpy for Basic Matrix Arithmetic5:16
    Broadcasting for Numpy3:52
    Solve for Equations5:04
    Numpy For Statistics7:23
    Conclusions to Section 32:24

  • Introduction to Pandas

    What are Pandas?12:06
    Read CSV Data in Python5:42
    Read in Excel File5:31
    Read HTML Data12:06
    Read JSON Data3:09
    Conclusions to Section 42:06

  • Data Pre-Processing/Wrangling

    Rationale behind this section4:19
    Remove NA Values10:28
    Basic Data Handling: Starting with Conditional Data Selection5:24
    Drop Column/Row4:42
    Subset and Index Data9:44
    Basic Data Grouping Based on Qualitative Attributes9:47
    Crosstabulation4:54
    Reshaping9:26
    Pivotting8:30
    Rank and Sort Data8:03
    Concatenate8:16
    Merge10:47
    Conclusion to Section 5

  • Introduction to Data Visualization

    What is Data Visualisation?9:33
    Theory Behind Data Visualisation6:46
    Histograms- Visualise the Distribution of Quantitative Variables12:13
    Boxplot- Visualise the Data Summary5:54
    Scatterplot- Visualise The Relationship Between Quantitative Variables11:57
    Line Chart12:31
    Barplot22:25
    Pie Chart5:29
    Conclusion to Section 62:14

  • Basic Statistical Data Analysis

    What is Statistical Data Analysis?10:08
    Some Pointers on Collecting Data for Statistical Studies8:38
    Explore the Quantitative Data: Descriptive Statistics9:05
    Group By Qualitative Categories10:25
    Visualize Descriptive Statistics-Boxplots5:28
    Common Terms Relating to Descriptive Statistics5:15
    Data Distribution- Normal Distribution4:07
    Check for Normal Distribution6:23
    Standard Normal Distribution and Z-scores4:10
    Confidence Interval-Theory6:06
    Confidence Interval-Calculation5:20
    Conclusion to Section 71:28

  • Statistical Inference & Relationship Between Variables

    What is Hypothesis Testing?5:42
    Test the Difference Between Two Groups7:30
    Test the Difference Between More Than Two Groups10:55
    Explore the Relationship Between Two Quantitative Variables4:26
    Correlation Analysis8:26
    Linear Regression-Theory10:44
    Linear Regression-Implementation in Python11:18
    Conditions of Linear Regression-Check in Python12:03
    Polynomial Regression3:53
    GLM: Generalized Linear Model5:25
    Logistic Regression11:10
    Conclusion to Section 81:52

  • Machine Learning for Data Science

    How is Machine Learning Different from Statistical Data Analysis?11:12
    What is Machine Learning (ML) About? Some Theoretical Pointers5:32

  • Unsupervised Learning

    Some Basic Pointers1:38
    kmeans-theory2:31
    KMeans-implementation on the iris data8:01
    Quantifying KMeans Clustering Performance3:53
    kmeans clustering on real data4:16
    How Do We Select the Number of Clusters?5:38
    Theory of hierarchical clustering4:10
    Implement hierarchical clustering9:19
    Theory of Principal Component Analysis (PCA)2:37
    Implement PCA3:52
    Conclusion to Section 102:08
    Data Preparation for Supervised Classification9:47
    Classification accuracy evaluation9:42
    Random Forest (RF) For Regression9:20

  • Supervised Learning

    What is this section about?10:10
    Logistic regression with classification8:26
    Random Forest (RF) For Classification12:02
    Linear Support Vector Machine (SVM) Classification3:10
    Non-Linear Support Vector Machine (SVM) Classification2:06
    Support Vector Regression4:30
    kNN Classification7:46
    kNN Regression3:48
    Gradient Boosting Machine (GBM) Classification5:54
    GBM Classification
    Gradient Boosting Regression (GBR)4:46
    Voting Classifier4:00
    Conclusion to Section 112:46

  • Artificial Neural Networks (ANN) and Deep Learning

    Introduction
    Perceptrons for Binary Classification4:27
    Getting Started with ANN-binary classification3:26
    Multi-label classification with MLP4:53
    Regression with MLP3:48
    MLP with PCA on a Large Dataset7:33
    Start With Deep Neural Network (DNN)
    Start with H204:14
    Default H2O Deep Learning Algorithm3:20
    Specify the Activation Function2:06
    Deep Learning Predictions5:02
    Conclusion to section 122:03

Complete Data Science Training with Python for Data Analysis

MS
Minerva Singh

Minerva Singh | Bestselling Udemy Instructor & Data Scientist

4.2/5 Instructor Rating: ★ ★ ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

60,288 Total Students
10,120 Reviews

Description

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

1,229 positive ratings from 6,976 students enrolled

  • Access 116 lectures & 12 hours of content 24/7
  • Get a full introduction to Python Data Science & Anaconda
  • Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
  • Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
  • Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
  • Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more
  • Discover how to create artificial neural networks & deep learning structures

"It is just what you need to learn when starting with data science" – Sagar Farkale

Specs

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Prior knowledge of Python will be useful but not required
  • Basic computer skills

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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