- What is Data Science?
- Introduction to the Course & Instructor
- Data For the Course
- Introduction to the Python Data Science Tool
- For Mac Users
- Introduction to the Python Data Science Environment
- Some Miscellaneous IPython Usage Facts
- Online iPython Interpreter
- Conclusion to Section 1
Online
₹ 449 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
certificate availability
certificate providing authority
The syllabus
Introduction to the Data Science in Python Bootcamp
Introduction to Python Pre-Requisites for Data Science
- Rationale Behind This Section
- Different Types of Data Used in Statistical & ML Analysis
- Different Types of Data Used Programatically
- Python Data Science Packages To Be Used
- Conclusions to Section 2
Introduction to Numpy
- Numpy: Introduction
- Create Numpy Arrays
- Numpy Operations
- Matrix Arithmetic and Linear Systems
- Numpy for Basic Vector Arithmetric
- Numpy for Basic Matrix Arithmetic
- Broadcasting with Numpy
- Solve Equations with Numpy
- Numpy for Statistical Operation
- Conclusion to Section 3
- Section 3 Quiz
Introduction to Pandas
- Data Structures in Python
- Read in Data
- Read in CSV Data Using Pandas
- Read in Excel Data Using Pandas
- Reading in JSON Data
- Read in HTML Data
- Conclusion to Section 4
Data Pre-Processing/Wrangling
- Rationale behind this section
- Removing NAs/No Values From Our Data
- Basic Data Handling: Starting with Conditional Data Selection
- Drop Column/Row
- Subset and Index Data
- Basic Data Grouping Based on Qualitative Attributes
- Crosstabulation
- Reshaping
- Pivoting
- Rank and Sort Data
- Concatenate
- Merging and Joining Data Frames
- Conclusion to Section 5
Introduction to Data Visualizations
- What is Data Visualization?
- Some Theoretical Principles Behind Data Visualization
- Histograms-Visualize the Distribution of Continuous Numerical Variables
- Boxplots-Visualize the Distribution of Continuous Numerical Variables
- Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
- Barplot
- Pie Chart
- Line Chart
- Conclusions to Section 6
Statistical Data Analysis-Basic
- What is Statistical Data Analysis?
- Some Pointers on Collecting Data for Statistical Studies
- Some Pointers on Exploring Quantitative Data
- Explore the Quantitative Data: Descriptive Statistics
- Grouping & Summarizing Data by Categories
- Visualize Descriptive Statistics-Boxplots
- Common Terms Relating to Descriptive Statistics
- Data Distribution- Normal Distribution
- Check for Normal Distribution
- Standard Normal Distribution and Z-scores
- Confidence Interval-Theory
- Confidence Interval-Calculation
- Conclusions to Section 7
Statistical Inference & Relationship Between Variables
- What is Hypothesis Testing?
- Test the Difference Between Two Groups
- Test the Difference Between More Than Two Groups
- Explore the Relationship Between Two Quantitative Variables
- Correlation Analysis
- Linear Regression-Theory
- Linear Regression-Implementation in Python
- Conditions of Linear Regression
- Conditions of Linear Regression-Check in Python
- Polynomial Regression
- GLM: Generalized Linear Model
- Logistic Regression
- Conclusions to Section 8
- Section 8 Quiz
Machine Learning for Data Science
- How is Machine Learning Different from Statistical Data Analysis?
- What is Machine Learning (ML) About? Some Theoretical Pointers
Unsupervised Learning in Python
- Unsupervised Classification- Some Basic Ideas
- KMeans-theory
- KMeans-implementation on the iris data
- Quantifying KMeans Clustering Performance
- KMeans Clustering with Real Data
- How Do We Select the Number of Clusters?
- Hierarchical Clustering-theory
- Hierarchical Clustering-practical
- Principal Component Analysis (PCA)-Theory
- Principal Component Analysis (PCA)-Practical Implementation
- Conclusions to Section 10
Supervised Learning
- What is This Section About?
- Data Preparation for Supervised Learning
- Pointers on Evaluating the Accuracy of Classification and Regression Modelling
- Using Logistic Regression as a Classification Model
- RF-Classification
- RF-Regression
- SVM- Linear Classification
- SVM- Non Linear Classification
- Support Vector Regression
- knn-Classification
- knn-Regression
- Gradient Boosting-classification
- Gradient Boosting-regression
- Voting Classifier
- Conclusions to Section 11
- Section 11 Quiz
Artificial Neural Networks (ANN) and Deep Learning (DL)
- Theory Behind ANN and DNN
- Perceptrons for Binary Classification
- Getting Started with ANN-binary classification
- Multi-label classification with MLP
- Regression with MLP
- MLP with PCA on a Large Dataset
- Start With Deep Neural Network (DNN)
- Start with H20
- Default H2O Deep Learning Algorithm
- Specify the Activation Function
- H2O Deep Learning For Predictions
- Conclusions to Section 12
- Section 12 Quiz
Miscellaneous Lectures & Information
- Data For This Section
- Read in Data from Online CSV
- Read Data from a Database
- Data Imputation
- Accessing Github
Instructors
Ms Minerva Singh
Data Scientist
Udemy
Other Masters, Ph.D, M.Phil.